AutoAndroidController/py/venv/Lib/site-packages/transformers/models/parakeet/modeling_parakeet.py

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# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
from dataclasses import dataclass
from typing import Callable, Optional, Union

import torch
from torch import nn

from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, CausalLMOutput
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import check_model_inputs
from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig


class ParakeetEncoderRelPositionalEncoding(nn.Module):
    """Relative positional encoding for Parakeet."""

    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: ParakeetEncoderConfig, device=None):
        super().__init__()
        self.max_position_embeddings = config.max_position_embeddings
        base = 10000.0
        inv_freq = 1.0 / (
            base
            ** (
                torch.arange(0, config.hidden_size, 2, dtype=torch.int64).to(device=device, dtype=torch.float)
                / config.hidden_size
            )
        )

        self.register_buffer("inv_freq", inv_freq, persistent=False)

    @torch.no_grad()
    def forward(self, hidden_states: torch.Tensor):
        seq_length = hidden_states.shape[1]
        if seq_length > self.max_position_embeddings:
            raise ValueError(
                f"Sequence Length: {seq_length} has to be less or equal than "
                f"config.max_position_embeddings {self.max_position_embeddings}."
            )

        position_ids = torch.arange(seq_length - 1, -seq_length, -1, device=hidden_states.device)
        inv_freq_expanded = (
            self.inv_freq[None, :, None].float().expand(hidden_states.shape[0], -1, 1).to(hidden_states.device)
        )
        position_ids_expanded = position_ids[None, None, :].float()

        device_type = (
            hidden_states.device.type
            if isinstance(hidden_states.device.type, str) and hidden_states.device.type != "mps"
            else "cpu"
        )
        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            sin = freqs.sin()
            cos = freqs.cos()
            # interleave sin and cos
            pos_embed = torch.stack([sin, cos], dim=-1)
            pos_embed = pos_embed.reshape(*pos_embed.shape[:-2], -1)

        return pos_embed.to(dtype=hidden_states.dtype)


class ParakeetEncoderFeedForward(nn.Module):
    def __init__(self, config: ParakeetEncoderConfig):
        super().__init__()
        self.linear1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=config.attention_bias)
        self.activation = ACT2FN[config.hidden_act]
        self.linear2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=config.attention_bias)
        self.activation_dropout = config.activation_dropout

    def forward(self, hidden_states):
        hidden_states = self.activation(self.linear1(hidden_states))
        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
        hidden_states = self.linear2(hidden_states)
        return hidden_states


class ParakeetEncoderConvolutionModule(nn.Module):
    def __init__(self, config: ParakeetEncoderConfig, module_config=None):
        """
        Args:
            config (ParakeetEncoderConfig): Configuration for the model.
            module_config (dict): Configuration for the module (e.g., encoder or decoder).
        """
        super().__init__()
        channels = config.hidden_size
        # kernel_size should be an odd number for 'SAME' padding
        if module_config is None:
            # e.g. using `ParakeetEncoderEncoderConfig` in src/transformers/models/parakeet_encoder/configuration_parakeet_encoder.py
            kernel_size = config.conv_kernel_size
            self.activation = ACT2FN[getattr(config, "hidden_act", "silu")]
        else:
            kernel_size = module_config["kernel_size"]
            self.activation = ACT2FN[module_config.get("activation", "silu")]
        self.padding = (kernel_size - 1) // 2
        self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=True)
        self.depthwise_conv = nn.Conv1d(
            channels, channels, kernel_size, stride=1, padding=self.padding, groups=channels, bias=True
        )
        self.norm = nn.BatchNorm1d(channels)
        self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1, stride=1, padding=0, bias=True)

    def forward(self, hidden_states, attention_mask=None):
        """
        Compute convolution module.

        Args:
            hidden_states (`torch.Tensor` of shape `(batch, time, channels)`): Input tensor.
            attention_mask (`torch.Tensor` of shape `(batch, 1, time)`): Attention mask.

        Returns:
            `torch.Tensor`: Output tensor of shape `(batch, time, channels)`.

        """
        # exchange the temporal dimension and the feature dimension
        hidden_states = hidden_states.transpose(1, 2)

        # GLU mechanism, (batch_size, 2*channel, dim)
        hidden_states = self.pointwise_conv1(hidden_states)
        # (batch_size, channel, dim)
        hidden_states = nn.functional.glu(hidden_states, dim=1)

        # Apply padding mask before convolution
        if attention_mask is not None:
            all_masked_rows = torch.all(~attention_mask, dim=-1)
            hidden_states = hidden_states.masked_fill(all_masked_rows, 0.0)

        # 1D Depthwise Conv
        hidden_states = self.depthwise_conv(hidden_states)
        hidden_states = self.norm(hidden_states)
        hidden_states = self.activation(hidden_states)
        hidden_states = self.pointwise_conv2(hidden_states)

        return hidden_states.transpose(1, 2)


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class ParakeetEncoderAttention(nn.Module):
    """Multi-head attention with relative positional encoding. See section 3.3 of https://huggingface.co/papers/1901.02860."""

    def __init__(self, config: ParakeetEncoderConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = config.attention_dropout
        self.is_causal = False

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )
        # W_{k,R} projection
        self.relative_k_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)
        # global content bias
        self.bias_u = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim))
        # global positional bias
        self.bias_v = nn.Parameter(torch.zeros(config.num_attention_heads, self.head_dim))

    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: Optional[torch.Tensor],
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        batch_size, seq_length = input_shape
        hidden_shape = (batch_size, seq_length, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        query_states_with_bias_u = query_states + self.bias_u.view(
            1, self.config.num_attention_heads, 1, self.head_dim
        )
        query_states_with_bias_v = query_states + self.bias_v.view(
            1, self.config.num_attention_heads, 1, self.head_dim
        )

        relative_key_states = self.relative_k_proj(position_embeddings)
        relative_key_states = relative_key_states.view(batch_size, -1, self.config.num_attention_heads, self.head_dim)

        # terms (b) and (d)
        matrix_bd = query_states_with_bias_v @ relative_key_states.permute(0, 2, 3, 1)
        matrix_bd = self._rel_shift(matrix_bd)
        matrix_bd = matrix_bd[..., :seq_length]
        matrix_bd = matrix_bd * self.scaling

        if attention_mask is not None:
            # here the original codebase uses -10000.0 rather than float("-inf") and then manual masked fill with 0.0s
            # see: https://github.com/NVIDIA-NeMo/NeMo/blob/8cfedd7203462cb251a914e700e5605444277561/nemo/collections/asr/parts/submodules/multi_head_attention.py#L320-L340
            # we rather went for a straight-forward approach with float("-inf")
            matrix_bd = matrix_bd.masked_fill_(attention_mask.logical_not(), float("-inf"))

        # will compute matrix_ac - terms (a) and (c) - and add matrix_bd
        attn_output, attn_weights = attention_interface(
            self,
            query=query_states_with_bias_u,
            key=key_states,
            value=value_states,
            attention_mask=matrix_bd,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights

    def _rel_shift(self, attention_scores):
        """Relative position shift for Shaw et al. style attention. See appendix B of https://huggingface.co/papers/1901.02860."""
        batch_size, num_heads, query_length, position_length = attention_scores.shape
        attention_scores = nn.functional.pad(attention_scores, pad=(1, 0))
        attention_scores = attention_scores.view(batch_size, num_heads, -1, query_length)
        attention_scores = attention_scores[:, :, 1:].view(batch_size, num_heads, query_length, position_length)
        return attention_scores


class ParakeetEncoderSubsamplingConv2D(nn.Module):
    def __init__(self, config: ParakeetEncoderConfig):
        super().__init__()

        self.kernel_size = config.subsampling_conv_kernel_size
        self.stride = config.subsampling_conv_stride
        self.channels = config.subsampling_conv_channels
        self.padding = (self.kernel_size - 1) // 2
        self.num_layers = int(math.log2(config.subsampling_factor))

        # define layers
        self.layers = nn.ModuleList()
        self.layers.append(
            nn.Conv2d(1, self.channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding)
        )
        self.layers.append(nn.ReLU())
        for i in range(self.num_layers - 1):
            # depthwise conv
            self.layers.append(
                nn.Conv2d(
                    self.channels,
                    self.channels,
                    kernel_size=self.kernel_size,
                    stride=self.stride,
                    padding=self.padding,
                    groups=self.channels,
                )
            )
            # pointwise conv
            self.layers.append(nn.Conv2d(self.channels, self.channels, kernel_size=1))
            # activation
            self.layers.append(nn.ReLU())

        out_length = config.num_mel_bins // (self.stride**self.num_layers)
        self.linear = nn.Linear(config.subsampling_conv_channels * out_length, config.hidden_size, bias=True)

    def _get_output_length(self, input_lengths: torch.Tensor, conv_layer: nn.Conv2d):
        if hasattr(conv_layer, "stride") and conv_layer.stride != (1, 1):
            padding = conv_layer.padding
            kernel_size = conv_layer.kernel_size[0]
            stride = conv_layer.stride[0]

            output_lengths = (input_lengths + padding[0] + padding[1] - kernel_size) // stride + 1
            return output_lengths

        return input_lengths

    def forward(self, input_features: torch.Tensor, attention_mask: torch.Tensor = None):
        hidden_states = input_features.unsqueeze(1)
        current_lengths = attention_mask.sum(-1) if attention_mask is not None else None

        for layer in self.layers:
            hidden_states = layer(hidden_states)

            # mask the hidden states
            if isinstance(layer, nn.Conv2d) and attention_mask is not None:
                current_lengths = self._get_output_length(current_lengths, layer)
                current_seq_length = hidden_states.shape[2]
                channel_mask = (
                    torch.arange(current_seq_length, device=attention_mask.device) < current_lengths[:, None]
                )
                hidden_states *= channel_mask[:, None, :, None]

        hidden_states = hidden_states.transpose(1, 2).reshape(hidden_states.shape[0], hidden_states.shape[2], -1)
        hidden_states = self.linear(hidden_states)

        return hidden_states


class ParakeetEncoderBlock(GradientCheckpointingLayer):
    def __init__(self, config: ParakeetEncoderConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.gradient_checkpointing = False

        self.feed_forward1 = ParakeetEncoderFeedForward(config)
        self.self_attn = ParakeetEncoderAttention(config, layer_idx)
        self.conv = ParakeetEncoderConvolutionModule(config)
        self.feed_forward2 = ParakeetEncoderFeedForward(config)

        self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size)
        self.norm_self_att = nn.LayerNorm(config.hidden_size)
        self.norm_conv = nn.LayerNorm(config.hidden_size)
        self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size)
        self.norm_out = nn.LayerNorm(config.hidden_size)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_embeddings: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states))
        hidden_states = residual + 0.5 * hidden_states  # the conformer architecture uses a factor of 0.5

        normalized_hidden_states = self.norm_self_att(hidden_states)
        attn_output, _ = self.self_attn(
            hidden_states=normalized_hidden_states,
            attention_mask=attention_mask,
            position_embeddings=position_embeddings,
            **kwargs,
        )
        hidden_states = hidden_states + attn_output

        conv_output = self.conv(self.norm_conv(hidden_states), attention_mask=attention_mask)
        hidden_states = hidden_states + conv_output

        ff2_output = self.feed_forward2(self.norm_feed_forward2(hidden_states))
        hidden_states = hidden_states + 0.5 * ff2_output  # the conformer architecture uses a factor of 0.5

        hidden_states = self.norm_out(hidden_states)

        return hidden_states


@auto_docstring
class ParakeetPreTrainedModel(PreTrainedModel):
    config: ParakeetCTCConfig
    base_model_prefix = "model"
    main_input_name = "input_features"
    supports_gradient_checkpointing = True
    _no_split_modules = ["ParakeetEncoderBlock"]
    _supports_flat_attention_mask = True
    _supports_sdpa = True
    _supports_flex_attn = True

    # TODO: @eustlb, add support when flash attention supports custom attention bias
    _supports_flash_attn = False

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": ParakeetEncoderBlock,
        "attentions": ParakeetEncoderAttention,
    }

    def _init_weights(self, module):
        super()._init_weights(module)

        if hasattr(self.config, "initializer_range"):
            std = self.config.initializer_range
        else:
            # 0.02 is the standard default value accross the library
            std = getattr(self.config.get_text_config(), "initializer_range", 0.02)

        if isinstance(module, ParakeetEncoderAttention):
            # Initialize positional bias parameters
            module.bias_u.data.normal_(mean=0.0, std=std)
            module.bias_v.data.normal_(mean=0.0, std=std)

    def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
        encoder_config = self.config.encoder_config if isinstance(self.config, ParakeetCTCConfig) else self.config

        kernel_size = encoder_config.subsampling_conv_kernel_size
        stride = encoder_config.subsampling_conv_stride
        num_layers = int(math.log2(encoder_config.subsampling_factor))

        all_paddings = (kernel_size - 1) // 2 * 2
        add_pad = all_paddings - kernel_size
        lengths = input_lengths

        for _ in range(num_layers):
            lengths = torch.div(lengths.to(dtype=torch.float) + add_pad, stride) + 1.0
            lengths = torch.floor(lengths)

        return lengths.to(dtype=torch.int)

    def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: Optional[int] = None):
        """
        Convert the input attention mask to its subsampled form. `target_length` sets the desired output length, useful
        when the attention mask length differs from `sum(-1).max()` (i.e., when the longest sequence in the batch is padded)
        """
        output_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
        # Use target_length if provided, otherwise use max length in batch
        max_length = target_length if target_length is not None else output_lengths.max()
        attention_mask = torch.arange(max_length, device=attention_mask.device) < output_lengths[:, None]
        return attention_mask


@auto_docstring(
    custom_intro="""
    The Parakeet Encoder model, based on the [Fast Conformer architecture](https://huggingface.co/papers/2305.05084).
    """
)
class ParakeetEncoder(ParakeetPreTrainedModel):
    config: ParakeetEncoderConfig
    base_model_prefix = "encoder"

    def __init__(self, config: ParakeetEncoderConfig):
        super().__init__(config)
        self.config = config
        self.gradient_checkpointing = False

        self.dropout = config.dropout
        self.dropout_positions = config.dropout_positions
        self.layerdrop = config.layerdrop

        self.input_scale = math.sqrt(config.hidden_size) if config.scale_input else 1.0
        self.subsampling = ParakeetEncoderSubsamplingConv2D(config)
        self.encode_positions = ParakeetEncoderRelPositionalEncoding(config)

        self.layers = nn.ModuleList(
            [ParakeetEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )

        self.post_init()

    @auto_docstring
    @check_model_inputs()
    @can_return_tuple
    def forward(
        self,
        input_features: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutput:
        r"""
        Example:

        ```python
        >>> from transformers import AutoProcessor, ParakeetEncoder
        >>> from datasets import load_dataset, Audio

        >>> model_id = "nvidia/parakeet-ctc-1.1b"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> encoder = ParakeetEncoder.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

        >>> inputs = processor(ds[0]["audio"]["array"])
        >>> encoder_outputs = encoder(**inputs)

        >>> print(encoder_outputs.last_hidden_state.shape)
        ```
        """

        hidden_states = self.subsampling(input_features, attention_mask)
        hidden_states = hidden_states * self.input_scale
        position_embeddings = self.encode_positions(hidden_states)

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        position_embeddings = nn.functional.dropout(
            position_embeddings, p=self.dropout_positions, training=self.training
        )

        if attention_mask is not None:
            attention_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
            attention_mask = attention_mask.unsqueeze(1).expand(-1, hidden_states.shape[1], -1)
            attention_mask = attention_mask & attention_mask.transpose(1, 2)
            attention_mask = attention_mask.unsqueeze(1)

        for encoder_layer in self.layers:
            # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
            to_drop = False
            if self.training:
                dropout_probability = torch.rand([])
                if dropout_probability < self.layerdrop:  # skip the layer
                    to_drop = True

            if not to_drop:
                hidden_states = encoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_embeddings=position_embeddings,
                    **kwargs,
                )

        return BaseModelOutput(last_hidden_state=hidden_states)


@dataclass
class ParakeetGenerateOutput(ModelOutput):
    """
    Outputs of Parakeet models.

    Args:
        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            The generated sequences. The second dimension (sequence_length) is either equal to `max_length` or shorter
            if all batches finished early due to the `eos_token_id`.
        logits (`tuple(torch.FloatTensor)` *optional*, returned when `output_logits=True`):
            Unprocessed prediction scores of the language modeling head (scores for each vocabulary token before SoftMax)
            at each generation step. Tuple of `torch.FloatTensor` with up to `max_new_tokens` elements (one element for
            each generated token), with each tensor of shape `(batch_size, config.vocab_size)`.
        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_attentions=True`):
            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
            `torch.FloatTensor` of shape `(batch_size, num_heads, generated_length, sequence_length)`.
        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `output_hidden_states=True`):
            Tuple (one element for each generated token) of tuples (one element for each layer of the decoder) of
            `torch.FloatTensor` of shape `(batch_size, generated_length, hidden_size)`.
    """

    sequences: torch.LongTensor
    logits: Optional[tuple[torch.FloatTensor]] = None
    attentions: Optional[tuple[tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[tuple[tuple[torch.FloatTensor]]] = None


@auto_docstring(
    custom_intro="""
    Parakeet Encoder with a Connectionist Temporal Classification (CTC) head.
    """
)
class ParakeetForCTC(ParakeetPreTrainedModel):
    config: ParakeetCTCConfig

    def __init__(self, config: ParakeetCTCConfig):
        super().__init__(config)
        self.encoder = ParakeetEncoder(config.encoder_config)
        # Conv rather than linear to be consistent with NeMO decoding layer
        self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)

        self.post_init()

    @auto_docstring
    @can_return_tuple
    def forward(
        self,
        input_features: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutput:
        r"""
        Example:

        ```python
        >>> from transformers import AutoProcessor, ParakeetForCTC
        >>> from datasets import load_dataset, Audio

        >>> model_id = "nvidia/parakeet-ctc-1.1b"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = ParakeetForCTC.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

        >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
        >>> outputs = model(**inputs)

        >>> print(outputs.loss)
        ```"""

        encoder_outputs = self.encoder(
            input_features=input_features,
            attention_mask=attention_mask,
            **kwargs,
        )

        hidden_states = encoder_outputs.last_hidden_state
        logits = self.ctc_head(hidden_states.transpose(1, 2)).transpose(1, 2)

        loss = None
        if labels is not None:
            # retrieve loss input_lengths from attention_mask
            attention_mask = (
                attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long)
            )
            input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))

            # assuming that padded tokens are filled with -100
            # when not being attended to
            labels_mask = labels != self.config.pad_token_id
            target_lengths = labels_mask.sum(-1)
            flattened_targets = labels.masked_select(labels_mask)

            # ctc_loss doesn't support fp16
            log_probs = nn.functional.log_softmax(logits, dim=-1, dtype=torch.float32).transpose(0, 1)

            with torch.backends.cudnn.flags(enabled=False):
                loss = nn.functional.ctc_loss(
                    log_probs,
                    flattened_targets,
                    input_lengths,
                    target_lengths,
                    blank=self.config.pad_token_id,
                    reduction=self.config.ctc_loss_reduction,
                    zero_infinity=self.config.ctc_zero_infinity,
                )

        return CausalLMOutput(
            loss=loss,
            logits=logits,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

    @torch.no_grad()
    def generate(
        self,
        input_features: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        return_dict_in_generate: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Union[ParakeetGenerateOutput, torch.LongTensor]:
        r"""
        Example:

        ```python
        >>> from transformers import AutoProcessor, ParakeetForCTC
        >>> from datasets import load_dataset, Audio

        >>> model_id = "nvidia/parakeet-ctc-1.1b"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = ParakeetForCTC.from_pretrained(model_id)

        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))

        >>> inputs = processor(ds[0]["audio"]["array"], text=ds[0]["text"])
        >>> predicted_ids = model.generate(**inputs)
        >>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

        >>> print(transcription)
        ```
        """
        kwargs["return_dict"] = True
        outputs: CausalLMOutput = self.forward(
            input_features=input_features,
            attention_mask=attention_mask,
            **kwargs,
        )

        # greedy decoding
        sequences = outputs.logits.argmax(dim=-1)

        # mask out padded tokens
        if attention_mask is not None:
            attention_mask = self._get_output_attention_mask(attention_mask, target_length=sequences.shape[1])
            sequences[~attention_mask] = self.config.pad_token_id

        if return_dict_in_generate:
            return ParakeetGenerateOutput(
                sequences=sequences,
                logits=outputs.logits,
                attentions=outputs.attentions,
                hidden_states=outputs.hidden_states,
            )

        return sequences


__all__ = ["ParakeetForCTC", "ParakeetEncoder", "ParakeetPreTrainedModel"]