






"""simple docstring"""


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


_UpperCamelCase    :   Optional[int]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   Union[str, Any]   			=						{
    '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 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''marian'''
    __magic_name__   					=					['''past_key_values''']
    __magic_name__   					=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





    def __init__(							self     : List[Any]    ,		A     : Any=5_8_1_0_1    ,		A     : Union[str, Any]=None    ,		A     : Any=1_0_2_4    ,		A     : Any=1_2    ,		A     : str=4_0_9_6    ,		A     : Optional[int]=1_6    ,		A     : Any=1_2    ,		A     : List[Any]=4_0_9_6    ,		A     : Optional[Any]=1_6    ,		A     : List[Any]=0.0    ,		A     : List[Any]=0.0    ,		A     : Dict=True    ,		A     : Tuple=True    ,		A     : Dict="gelu"    ,		A     : List[str]=1_0_2_4    ,		A     : Union[str, Any]=0.1    ,		A     : List[Any]=0.0    ,		A     : Dict=0.0    ,		A     : Union[str, Any]=0.02    ,		A     : Dict=5_8_1_0_0    ,		A     : Optional[int]=False    ,		A     : Dict=5_8_1_0_0    ,		A     : Any=0    ,		A     : Optional[int]=0    ,		A     : Dict=True    ,		**A     : Optional[Any]    ,		):


      '''simple docstring'''




      a					:      Optional[int]    =       vocab_size
      a					:      int    =       decoder_vocab_size or vocab_size
      a					:      List[Any]    =       max_position_embeddings
      a					:      Any    =       d_model
      a					:      Any    =       encoder_ffn_dim
      a					:      Optional[Any]    =       encoder_layers
      a					:      int    =       encoder_attention_heads
      a					:      Union[str, Any]    =       decoder_ffn_dim
      a					:      List[str]    =       decoder_layers
      a					:      int    =       decoder_attention_heads
      a					:      Optional[Any]    =       dropout
      a					:      Union[str, Any]    =       attention_dropout
      a					:      str    =       activation_dropout
      a					:      str    =       activation_function
      a					:      Optional[Any]    =       init_std
      a					:      List[str]    =       encoder_layerdrop
      a					:      Dict    =       decoder_layerdrop
      a					:      int    =       use_cache
      a					:      Any    =       encoder_layers
      a					:      Optional[Any]    =       scale_embedding  # scale factor will be sqrt(d_model) if True
      a					:      Optional[Any]    =       share_encoder_decoder_embeddings
      super().__init__(
          pad_token_id=A    ,		eos_token_id=A    ,		is_encoder_decoder=A    ,		decoder_start_token_id=A    ,		forced_eos_token_id=A    ,		**A    ,		)






class 						snake_case  (  UpperCAmelCase	):





    @property
    # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      if self.task in ["default", "seq2seq-lm"]:
        a					:      Optional[Any]    =       OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
            ]      )

        if self.use_past:
          a					:      Optional[Any]    =       {0: 'batch'}
          a					:      List[str]    =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
          a					:      Union[str, Any]    =       {0: 'batch', 1: 'decoder_sequence'}
          a					:      Union[str, Any]    =       {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
          self.fill_with_past_key_values_(A    ,		direction='inputs'      )
      elif self.task == "causal-lm":
        # TODO: figure this case out.
        a					:      str    =       OrderedDict(
            [
                ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
            ]      )
        if self.use_past:
          a,					a					:      Tuple    =       self.num_layers
          for i in range(A      ):
            a					:      List[Any]    =       {0: 'batch', 2: 'past_sequence + sequence'}
            a					:      Optional[int]    =       {0: 'batch', 2: 'past_sequence + sequence'}
      else:
        a					:      int    =       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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      if self.task in ["default", "seq2seq-lm"]:
        a					:      Optional[Any]    =       super().outputs
      else:
        a					:      Dict    =       super(A    ,		self      ).outputs
        if self.use_past:
          a,					a					:      Tuple    =       self.num_layers
          for i in range(A      ):
            a					:      List[Any]    =       {0: 'batch', 2: 'past_sequence + sequence'}
            a					:      Union[str, Any]    =       {0: 'batch', 2: 'past_sequence + sequence'}
      return common_outputs





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : PreTrainedTokenizer    ,		A     : int = -1    ,		A     : int = -1    ,		A     : bool = False    ,		A     : Optional[TensorType] = None    ,		):


      '''simple docstring'''




      a					:      int    =       self._generate_dummy_inputs_for_encoder_and_decoder(
          A    ,		A    ,		A    ,		A    ,		A      )

      # Generate decoder inputs
      a					:      Dict    =       seq_length if not self.use_past else 1
      a					:      Optional[Any]    =       self._generate_dummy_inputs_for_encoder_and_decoder(
          A    ,		A    ,		A    ,		A    ,		A      )
      a					:      Any    =       {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      a					:      int    =       dict(**A    ,		**A      )

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

        a					:      Tuple    =       torch.cat(
            [common_inputs['decoder_attention_mask'], torch.ones(A    ,		A      )]    ,		dim=1      )

        a					:      Tuple    =       []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        a,					a					:      List[Any]    =       self.num_layers
        a					:      Optional[int]    =       min(A    ,		A      )
        a					:      Dict    =       max(A    ,		A      ) - min_num_layers
        a					:      Optional[int]    =       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : PreTrainedTokenizer    ,		A     : int = -1    ,		A     : int = -1    ,		A     : bool = False    ,		A     : Optional[TensorType] = None    ,		):


      '''simple docstring'''




      a					:      Union[str, Any]    =       self._generate_dummy_inputs_for_encoder_and_decoder(
          A    ,		A    ,		A    ,		A    ,		A      )

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

        a					:      List[str]    =       common_inputs['attention_mask'].dtype
        a					:      str    =       torch.cat(
            [common_inputs['attention_mask'], torch.ones(A    ,		A    ,		dtype=A      )]    ,		dim=1      )
        a					:      Any    =       [
            (torch.zeros(A      ), torch.zeros(A      )) for _ in range(A      )
        ]
      return common_inputs





    def     lowerCamelCase__				(							self     : Dict    ,		A     : PreTrainedTokenizer    ,		A     : int = -1    ,		A     : int = -1    ,		A     : bool = False    ,		A     : Optional[TensorType] = None    ,		):


      '''simple docstring'''




      a					:      List[str]    =       compute_effective_axis_dimension(
          A    ,		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
      a					:      Optional[int]    =       tokenizer.num_special_tokens_to_add(A      )
      a					:      Dict    =       compute_effective_axis_dimension(
          A    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=A      )

      # Generate dummy inputs according to compute batch and sequence
      a					:      List[Any]    =       [' '.join([tokenizer.unk_token]      ) * seq_length] * batch_size
      a					:      List[str]    =       dict(tokenizer(A    ,		return_tensors=A      )      )
      return common_inputs





    def     lowerCamelCase__				(							self     : int    ,		A     : PreTrainedTokenizer    ,		A     : int = -1    ,		A     : int = -1    ,		A     : bool = False    ,		A     : Optional[TensorType] = None    ,		):


      '''simple docstring'''




      if self.task in ["default", "seq2seq-lm"]:
        a					:      Union[str, Any]    =       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A    ,		batch_size=A    ,		seq_length=A    ,		is_pair=A    ,		framework=A      )

      else:
        a					:      str    =       self._generate_dummy_inputs_for_causal_lm(
            A    ,		batch_size=A    ,		seq_length=A    ,		is_pair=A    ,		framework=A      )

      return common_inputs





    def     lowerCamelCase__				(							self     : Optional[Any]    ,		A     : int    ,		A     : Dict    ,		A     : Dict    ,		A     : Any      ):


      '''simple docstring'''




      if self.task in ["default", "seq2seq-lm"]:
        a					:      Optional[Any]    =       super()._flatten_past_key_values_(A    ,		A    ,		A    ,		A      )
      else:
        a					:      Optional[int]    =       super(A    ,		self      )._flatten_past_key_values_(
            A    ,		A    ,		A    ,		A      )





    @property
    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      return 1E-4












"""simple docstring"""


import argparse
from collections import defaultdict

import yaml


_UpperCamelCase    :   int   			=						'docs/source/en/_toctree.yml'




def       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

  a					:      List[Any]    =       defaultdict(A_				)
  for doc in model_doc:
    counts[doc["local"]] += 1
  a					:      Optional[Any]    =       [key for key, value in counts.items() if value > 1]

  a					:      List[str]    =       []
  for duplicate_key in duplicates:
    a					:      int    =       list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
    if len(A_				) > 1:
      raise ValueError(
          f'''{duplicate_key} is present several times in the documentation table of content at '''
          '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
          'others.'				)
    # Only add this once
    new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

  # Add none duplicate-keys
  new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

  # Sort
  return sorted(A_     ,      key=lambda A_				: s["title"].lower()				)




def       snake_case  (A_				:List[str]=False				):







  '''simple docstring'''

  with open(A_     ,      encoding='utf-8'				) as f:
    a					:      Dict    =       yaml.safe_load(f.read()				)

  # Get to the API doc
  a					:      Optional[Any]    =       0
  while content[api_idx]["title"] != "API":
    api_idx += 1
  a					:      List[str]    =       content[api_idx]['sections']

  # Then to the model doc
  a					:      Optional[int]    =       0
  while api_doc[model_idx]["title"] != "Models":
    model_idx += 1

  a					:      Optional[Any]    =       api_doc[model_idx]['sections']

  a					:      Dict    =       [(idx, section) for idx, section in enumerate(A_				) if 'sections' in section]
  a					:      List[str]    =       False
  for idx, modality_doc in modalities_docs:
    a					:      str    =       modality_doc['sections']
    a					:      str    =       clean_model_doc_toc(A_				)

    if old_modality_doc != new_modality_doc:
      a					:      str    =       True
      if overwrite:
        a					:      Any    =       new_modality_doc

  if diff:
    if overwrite:
      a					:      Any    =       model_doc
      a					:      str    =       api_doc
      with open(A_     ,      'w'     ,      encoding='utf-8'				) as f:
        f.write(yaml.dump(A_     ,      allow_unicode=A_				)				)
    else:
      raise ValueError(
          'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
   _UpperCamelCase    :   Optional[int]   			=						argparse.ArgumentParser()
   parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
   _UpperCamelCase    :   Any   			=						parser.parse_args()

   check_model_doc(args.fix_and_overwrite)




