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# Copyright 2025 The HuggingFace Inc. team and Google LLC. 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.

from collections.abc import Callable
from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ...activations import ACT2FN
from ...integrations import use_kernel_func_from_hub, use_kernelized_func
from ...masking_utils import create_bidirectional_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, CausalLMOutput
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
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.generic import check_model_inputs, maybe_autocast
from .configuration_lasr import LasrCTCConfig, LasrEncoderConfig


class LasrEncoderSubsampling(nn.Module):
    def __init__(self, config: LasrEncoderConfig):
        super().__init__()
        self.dense_0 = nn.Linear(config.num_mel_bins, config.hidden_size)
        self.conv_0 = nn.Conv1d(
            config.hidden_size,
            config.hidden_size,
            kernel_size=config.subsampling_conv_kernel_size,
            stride=config.subsampling_conv_stride,
        )
        self.conv_1 = nn.Conv1d(
            config.hidden_size,
            config.subsampling_conv_channels,
            kernel_size=config.subsampling_conv_kernel_size,
            stride=config.subsampling_conv_stride,
        )
        self.dense_1 = nn.Linear(config.subsampling_conv_channels, config.hidden_size)
        self.act_fn = nn.ReLU()

    def forward(self, input_features: torch.Tensor) -> torch.Tensor:
        hidden_states = self.act_fn(self.dense_0(input_features))
        hidden_states = hidden_states.transpose(1, 2)
        hidden_states = self.act_fn(self.conv_0(hidden_states))
        hidden_states = self.act_fn(self.conv_1(hidden_states))
        hidden_states = hidden_states.transpose(1, 2)
        return self.dense_1(hidden_states)


class LasrEncoderRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: LasrEncoderConfig, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_parameters["rope_type"]
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

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

    @staticmethod
    def compute_default_rope_parameters(
        config: LasrEncoderConfig | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_parameters["rope_theta"]
        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @torch.no_grad()
    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


@use_kernel_func_from_hub("rotary_pos_emb")
def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


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: torch.Tensor | None,
    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


@use_kernelized_func(apply_rotary_pos_emb)
class LasrEncoderAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: LasrEncoderConfig, 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
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -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)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            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


class LasrEncoderConvolutionModule(nn.Module):
    def __init__(self, config: LasrEncoderConfig, module_config=None):
        """
        Args:
            config (LasrEncoderConfig): 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 `LasrEncoderEncoderConfig` in src/transformers/models/lasr_encoder/configuration_lasr_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 = "same"
        self.pointwise_conv1 = nn.Conv1d(
            channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias
        )
        self.depthwise_conv = nn.Conv1d(
            channels,
            channels,
            kernel_size,
            stride=1,
            padding=self.padding,
            groups=channels,
            bias=config.convolution_bias,
        )
        self.norm = nn.BatchNorm1d(config.hidden_size, momentum=config.batch_norm_momentum)
        self.pointwise_conv2 = nn.Conv1d(
            channels, channels, kernel_size=1, stride=1, padding=0, bias=config.convolution_bias
        )

    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, 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:
            if attention_mask.dtype == torch.bool:
                all_masked_rows = torch.all(~attention_mask, dim=2)
            else:
                all_masked_rows = torch.all(~(attention_mask == 0.0), dim=2)
            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)


class LasrEncoderFeedForward(nn.Module):
    def __init__(self, config: LasrEncoderConfig):
        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 LasrEncoderBlock(GradientCheckpointingLayer):
    def __init__(self, config: LasrEncoderConfig, layer_idx: int):
        super().__init__()
        self.gradient_checkpointing = False

        self.feed_forward1 = LasrEncoderFeedForward(config)
        self.self_attn = LasrEncoderAttention(config, layer_idx)
        self.conv = LasrEncoderConvolutionModule(config)
        self.feed_forward2 = LasrEncoderFeedForward(config)

        self.norm_feed_forward1 = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_self_att = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_conv = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_feed_forward2 = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)
        self.norm_out = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, bias=False)

        self.feed_forward_residual_weights = config.feed_forward_residual_weights
        self.conv_residual_weights = config.conv_residual_weights

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_embeddings: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.feed_forward1(self.norm_feed_forward1(hidden_states))
        hidden_states = (
            self.feed_forward_residual_weights[0] * residual + self.feed_forward_residual_weights[1] * hidden_states
        )

        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 = self.conv_residual_weights[0] * hidden_states + self.conv_residual_weights[1] * conv_output

        residual = hidden_states
        hidden_states = self.feed_forward2(self.norm_feed_forward2(hidden_states))
        hidden_states = (
            self.feed_forward_residual_weights[0] * residual + self.feed_forward_residual_weights[1] * hidden_states
        )

        hidden_states = self.norm_out(hidden_states)

        return hidden_states


@auto_docstring
class LasrPreTrainedModel(PreTrainedModel):
    config: LasrCTCConfig
    base_model_prefix = "model"
    main_input_name = "input_features"
    input_modalities = "audio"
    supports_gradient_checkpointing = True
    _no_split_modules = ["LasrEncoderBlock"]
    _supports_flat_attention_mask = True
    _supports_sdpa = True
    # padding is incompatible with flex attention as the resulting mask cannot be used to apply padding
    _supports_flex_attn = False

    # 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": LasrEncoderBlock,
        "attentions": LasrEncoderAttention,
    }

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)

    def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
        encoder_config = self.config.encoder_config if isinstance(self.config, LasrCTCConfig) else self.config
        kernel_size = encoder_config.subsampling_conv_kernel_size
        stride = encoder_config.subsampling_conv_stride

        num_layers = 2
        for _ in range(num_layers):
            input_lengths = (input_lengths - kernel_size) // stride + 1

        return input_lengths

    def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length: int | None = 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 LasrEncoder model, based on the Conformer architecture](https://arxiv.org/abs/2005.08100).
    """
)
class LasrEncoder(LasrPreTrainedModel):
    config: LasrEncoderConfig
    base_model_prefix = "encoder"

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

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

        self.subsampler = LasrEncoderSubsampling(config)
        self.rotary_emb = LasrEncoderRotaryEmbedding(config)
        self.layers = nn.ModuleList(
            [LasrEncoderBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.out_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)

        self.post_init()

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

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

        >>> model_id = TODO
        >>> 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.subsampler(input_features)
        cos, sin = self.rotary_emb(
            hidden_states, torch.arange(hidden_states.shape[1], device=hidden_states.device).unsqueeze(0)
        )

        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
        cos = nn.functional.dropout(cos, p=self.dropout_positions, training=self.training)
        sin = nn.functional.dropout(sin, 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 = create_bidirectional_mask(
            config=self.config,
            input_embeds=hidden_states,
            attention_mask=attention_mask,
        )

        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=(cos, sin),
                    **kwargs,
                )

        hidden_states = self.out_norm(hidden_states)

        return BaseModelOutput(last_hidden_state=hidden_states)


@dataclass
class LasrGenerateOutput(ModelOutput):
    """
    Outputs of Lasr 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: tuple[torch.FloatTensor] | None = None
    attentions: tuple[tuple[torch.FloatTensor]] | None = None
    hidden_states: tuple[tuple[torch.FloatTensor]] | None = None


@auto_docstring(
    custom_intro="""
    Lasr Encoder with a Connectionist Temporal Classification (CTC) head.
    """
)
class LasrForCTC(LasrPreTrainedModel):
    config: LasrCTCConfig

    def __init__(self, config: LasrCTCConfig):
        super().__init__(config)
        self.encoder = LasrEncoder(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: torch.Tensor | None = None,
        labels: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutput:
        r"""
        Example:

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

        >>> model_id = "nvidia/lasr-ctc-1.1b"
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = LasrForCTC.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: torch.Tensor | None = None,
        return_dict_in_generate: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ) -> LasrGenerateOutput | torch.LongTensor:
        r"""
        Example:

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

        >>> model_id = TODO
        >>> processor = AutoProcessor.from_pretrained(model_id)
        >>> model = LasrForCTC.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 LasrGenerateOutput(
                sequences=sequences,
                logits=outputs.logits,
                attentions=outputs.attentions,
                hidden_states=outputs.hidden_states,
            )

        return sequences


__all__ = ["LasrForCTC", "LasrEncoder", "LasrPreTrainedModel"]