





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


_snake_case  :   Any						   =  logging.get_logger(__name__)

_snake_case  :   Tuple						   =  {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""marian"""
  a_      =			["""past_key_values"""]
  a_      =			{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






  def __init__(    self			:		Any ,  lowerCAmelCase_			:		Union[str, Any]=5_8_1_0_1 ,  lowerCAmelCase_			:		List[Any]=None ,  lowerCAmelCase_			:		List[Any]=1_0_2_4 ,  lowerCAmelCase_			:		int=1_2 ,  lowerCAmelCase_			:		Dict=4_0_9_6 ,  lowerCAmelCase_			:		Any=1_6 ,  lowerCAmelCase_			:		Dict=1_2 ,  lowerCAmelCase_			:		List[str]=4_0_9_6 ,  lowerCAmelCase_			:		Optional[int]=1_6 ,  lowerCAmelCase_			:		Optional[int]=0.0 ,  lowerCAmelCase_			:		Union[str, Any]=0.0 ,  lowerCAmelCase_			:		Dict=True ,  lowerCAmelCase_			:		Any=True ,  lowerCAmelCase_			:		List[str]="gelu" ,  lowerCAmelCase_			:		int=1_0_2_4 ,  lowerCAmelCase_			:		Optional[Any]=0.1 ,  lowerCAmelCase_			:		Union[str, Any]=0.0 ,  lowerCAmelCase_			:		Tuple=0.0 ,  lowerCAmelCase_			:		List[str]=0.02 ,  lowerCAmelCase_			:		List[Any]=5_8_1_0_0 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Tuple=5_8_1_0_0 ,  lowerCAmelCase_			:		List[Any]=0 ,  lowerCAmelCase_			:		Any=0 ,  lowerCAmelCase_			:		Optional[int]=True ,  **lowerCAmelCase_			:		List[str] ,  )							->						List[Any]:
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     decoder_vocab_size or vocab_size
     __lowerCAmelCase							     =     max_position_embeddings
     __lowerCAmelCase							     =     d_model
     __lowerCAmelCase							     =     encoder_ffn_dim
     __lowerCAmelCase							     =     encoder_layers
     __lowerCAmelCase							     =     encoder_attention_heads
     __lowerCAmelCase							     =     decoder_ffn_dim
     __lowerCAmelCase							     =     decoder_layers
     __lowerCAmelCase							     =     decoder_attention_heads
     __lowerCAmelCase							     =     dropout
     __lowerCAmelCase							     =     attention_dropout
     __lowerCAmelCase							     =     activation_dropout
     __lowerCAmelCase							     =     activation_function
     __lowerCAmelCase							     =     init_std
     __lowerCAmelCase							     =     encoder_layerdrop
     __lowerCAmelCase							     =     decoder_layerdrop
     __lowerCAmelCase							     =     use_cache
     __lowerCAmelCase							     =     encoder_layers
     __lowerCAmelCase							     =     scale_embedding  # scale factor will be sqrt(d_model) if True
     __lowerCAmelCase							     =     share_encoder_decoder_embeddings
     super().__init__(
         pad_token_id=lowerCAmelCase_ ,  eos_token_id=lowerCAmelCase_ ,  is_encoder_decoder=lowerCAmelCase_ ,  decoder_start_token_id=lowerCAmelCase_ ,  forced_eos_token_id=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )





class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def     lowercase		(    self			:		Any					)							->						Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase							     =     OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
            ]					)

        if self.use_past:
           __lowerCAmelCase							     =     {0: 'batch'}
           __lowerCAmelCase							     =     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
           __lowerCAmelCase							     =     {0: 'batch', 1: 'decoder_sequence'}
           __lowerCAmelCase							     =     {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
           self.fill_with_past_key_values_(lowerCAmelCase_ ,  direction='inputs'					)
     elif self.task == "causal-lm":
        # TODO: figure this case out.
        __lowerCAmelCase							     =     OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
            ]					)
        if self.use_past:
           __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.num_layers
           for i in range(lowerCAmelCase_					):
              __lowerCAmelCase							     =     {0: 'batch', 2: 'past_sequence + sequence'}
              __lowerCAmelCase							     =     {0: 'batch', 2: 'past_sequence + sequence'}
     else:
        __lowerCAmelCase							     =     OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
            ]					)

     return common_inputs






  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def     lowercase		(    self			:		int					)							->						Mapping[str, Mapping[int, str]]:
     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase							     =     super().outputs
     else:
        __lowerCAmelCase							     =     super(lowerCAmelCase_ ,  self					).outputs
        if self.use_past:
           __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.num_layers
           for i in range(lowerCAmelCase_					):
              __lowerCAmelCase							     =     {0: 'batch', 2: 'past_sequence + sequence'}
              __lowerCAmelCase							     =     {0: 'batch', 2: 'past_sequence + sequence'}
     return common_outputs






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		PreTrainedTokenizer ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[TensorType] = None ,  )							->						Mapping[str, Any]:
     __lowerCAmelCase							     =     self._generate_dummy_inputs_for_encoder_and_decoder(
         lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     # Generate decoder inputs
     __lowerCAmelCase							     =     seq_length if not self.use_past else 1
     __lowerCAmelCase							     =     self._generate_dummy_inputs_for_encoder_and_decoder(
         lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
     __lowerCAmelCase							     =     dict(**lowerCAmelCase_ ,  **lowerCAmelCase_					)

     if self.use_past:
        if not is_torch_available():
           raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
        else:
           import torch
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     common_inputs['input_ids'].shape
        __lowerCAmelCase							     =     common_inputs['decoder_input_ids'].shape[1]
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.num_attention_heads
        __lowerCAmelCase							     =     (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        __lowerCAmelCase							     =     decoder_seq_length + 3
        __lowerCAmelCase							     =     (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        __lowerCAmelCase							     =     torch.cat(
            [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase_ ,  lowerCAmelCase_					)] ,  dim=1					)

        __lowerCAmelCase							     =     []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.num_layers
        __lowerCAmelCase							     =     min(lowerCAmelCase_ ,  lowerCAmelCase_					)
        __lowerCAmelCase							     =     max(lowerCAmelCase_ ,  lowerCAmelCase_					) - min_num_layers
        __lowerCAmelCase							     =     'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

        for _ in range(lowerCAmelCase_					):
           common_inputs["past_key_values"].append(
               (
                   torch.zeros(lowerCAmelCase_					),
                   torch.zeros(lowerCAmelCase_					),
                   torch.zeros(lowerCAmelCase_					),
                   torch.zeros(lowerCAmelCase_					),
               )					)
        # TODO: test this.
        __lowerCAmelCase							     =     encoder_shape if remaining_side_name == 'encoder' else decoder_shape
        for _ in range(lowerCAmelCase_ ,  lowerCAmelCase_					):
           common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase_					), torch.zeros(lowerCAmelCase_					))					)
     return common_inputs






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		PreTrainedTokenizer ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[TensorType] = None ,  )							->						Mapping[str, Any]:
     __lowerCAmelCase							     =     self._generate_dummy_inputs_for_encoder_and_decoder(
         lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)

     if self.use_past:
        if not is_torch_available():
           raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'					)
        else:
           import torch
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __lowerCAmelCase							     =     seqlen + 2
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.num_layers
        __lowerCAmelCase     ,     __lowerCAmelCase							     =     self.num_attention_heads
        __lowerCAmelCase							     =     (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        __lowerCAmelCase							     =     common_inputs['attention_mask'].dtype
        __lowerCAmelCase							     =     torch.cat(
            [common_inputs['attention_mask'], torch.ones(lowerCAmelCase_ ,  lowerCAmelCase_ ,  dtype=lowerCAmelCase_					)] ,  dim=1					)
        __lowerCAmelCase							     =     [
            (torch.zeros(lowerCAmelCase_					), torch.zeros(lowerCAmelCase_					)) for _ in range(lowerCAmelCase_					)
        ]
     return common_inputs






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		PreTrainedTokenizer ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[TensorType] = None ,  )							->						Mapping[str, Any]:
     # Copied from OnnxConfig.generate_dummy_inputs
     # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
     # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
     __lowerCAmelCase							     =     compute_effective_axis_dimension(
         lowerCAmelCase_ ,  fixed_dimension=OnnxConfig.default_fixed_batch ,  num_token_to_add=0					)

     # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
     __lowerCAmelCase							     =     tokenizer.num_special_tokens_to_add(lowerCAmelCase_					)
     __lowerCAmelCase							     =     compute_effective_axis_dimension(
         lowerCAmelCase_ ,  fixed_dimension=OnnxConfig.default_fixed_sequence ,  num_token_to_add=lowerCAmelCase_					)

     # Generate dummy inputs according to compute batch and sequence
     __lowerCAmelCase							     =     [' '.join([tokenizer.unk_token]					) * seq_length] * batch_size
     __lowerCAmelCase							     =     dict(tokenizer(lowerCAmelCase_ ,  return_tensors=lowerCAmelCase_					)					)
     return common_inputs






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		PreTrainedTokenizer ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		int = -1 ,  lowerCAmelCase_			:		bool = False ,  lowerCAmelCase_			:		Optional[TensorType] = None ,  )							->						Mapping[str, Any]:
     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase							     =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            lowerCAmelCase_ ,  batch_size=lowerCAmelCase_ ,  seq_length=lowerCAmelCase_ ,  is_pair=lowerCAmelCase_ ,  framework=lowerCAmelCase_					)

     else:
        __lowerCAmelCase							     =     self._generate_dummy_inputs_for_causal_lm(
            lowerCAmelCase_ ,  batch_size=lowerCAmelCase_ ,  seq_length=lowerCAmelCase_ ,  is_pair=lowerCAmelCase_ ,  framework=lowerCAmelCase_					)

     return common_inputs






  def     lowercase		(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		int					)							->						Dict:
     if self.task in ["default", "seq2seq-lm"]:
        __lowerCAmelCase							     =     super()._flatten_past_key_values_(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     super(lowerCAmelCase_ ,  self					)._flatten_past_key_values_(
            lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		Dict					)							->						float:
     return 1e-4








import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case  :   List[str]						   =  logging.get_logger(__name__)

_snake_case  :   Dict						   =  {
    'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
    'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}



class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""encodec"""






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0] ,  lowerCAmelCase_			:		Optional[Any]=2_4_0_0_0 ,  lowerCAmelCase_			:		int=1 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[Any]=1_2_8 ,  lowerCAmelCase_			:		List[Any]=3_2 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		Optional[int]=[8, 5, 4, 2] ,  lowerCAmelCase_			:		Optional[Any]="weight_norm" ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		Optional[int]=7 ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[Any]=2 ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Dict="reflect" ,  lowerCAmelCase_			:		List[Any]=2 ,  lowerCAmelCase_			:		int=2 ,  lowerCAmelCase_			:		Dict=1.0 ,  lowerCAmelCase_			:		str=1_0_2_4 ,  lowerCAmelCase_			:		Dict=None ,  lowerCAmelCase_			:		List[Any]=True ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						Union[str, Any]:
     __lowerCAmelCase							     =     target_bandwidths
     __lowerCAmelCase							     =     sampling_rate
     __lowerCAmelCase							     =     audio_channels
     __lowerCAmelCase							     =     normalize
     __lowerCAmelCase							     =     chunk_length_s
     __lowerCAmelCase							     =     overlap
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_filters
     __lowerCAmelCase							     =     num_residual_layers
     __lowerCAmelCase							     =     upsampling_ratios
     __lowerCAmelCase							     =     norm_type
     __lowerCAmelCase							     =     kernel_size
     __lowerCAmelCase							     =     last_kernel_size
     __lowerCAmelCase							     =     residual_kernel_size
     __lowerCAmelCase							     =     dilation_growth_rate
     __lowerCAmelCase							     =     use_causal_conv
     __lowerCAmelCase							     =     pad_mode
     __lowerCAmelCase							     =     compress
     __lowerCAmelCase							     =     num_lstm_layers
     __lowerCAmelCase							     =     trim_right_ratio
     __lowerCAmelCase							     =     codebook_size
     __lowerCAmelCase							     =     codebook_dim if codebook_dim is not None else hidden_size
     __lowerCAmelCase							     =     use_conv_shortcut

     if self.norm_type not in ["weight_norm", "time_group_norm"]:
        raise ValueError(
            f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}"""					)

     super().__init__(**lowerCAmelCase_					)






  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Optional[int]:
     if self.chunk_length_s is None:
        return None
     else:
        return int(self.chunk_length_s * self.sampling_rate					)






  @property
  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     if self.chunk_length_s is None or self.overlap is None:
        return None
     else:
        return max(1 ,  int((1.0 - self.overlap) * self.chunk_length					)					)






  @property
  def     lowercase		(    self			:		Any					)							->						int:
     __lowerCAmelCase							     =     np.prod(self.upsampling_ratios					)
     return math.ceil(self.sampling_rate / hop_length					)






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)					)

