






"""simple docstring"""


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

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


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

_UpperCamelCase							:       str       					=		{
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''m2m_100'''
    __magic_name__       	=	['''past_key_values''']
    __magic_name__       	=	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





    def __init__(				self      :   str							,						A      :   int=1_2_8_1_1_2							,						A      :   Union[str, Any]=1_0_2_4							,						A      :   List[str]=1_2							,						A      :   Union[str, Any]=4_0_9_6							,						A      :   List[Any]=1_6							,						A      :   Optional[Any]=1_2							,						A      :   Any=4_0_9_6							,						A      :   Union[str, Any]=1_6							,						A      :   Any=0.05							,						A      :   Union[str, Any]=0.05							,						A      :   int=True							,						A      :   List[str]=True							,						A      :   Dict="relu"							,						A      :   Optional[Any]=1_0_2_4							,						A      :   Tuple=0.1							,						A      :   Tuple=0.1							,						A      :   Union[str, Any]=0.0							,						A      :   Optional[int]=0.02							,						A      :   Dict=2							,						A      :   str=True							,						A      :   Optional[int]=1							,						A      :   Tuple=0							,						A      :   Tuple=2							,						**A      :   Any							,						):


      '''simple docstring'''




      a	:     Dict							=  vocab_size
      a	:     List[Any]							=  max_position_embeddings
      a	:     str							=  d_model
      a	:     str							=  encoder_ffn_dim
      a	:     Union[str, Any]							=  encoder_layers
      a	:     Optional[Any]							=  encoder_attention_heads
      a	:     Tuple							=  decoder_ffn_dim
      a	:     str							=  decoder_layers
      a	:     Tuple							=  decoder_attention_heads
      a	:     Tuple							=  dropout
      a	:     Tuple							=  attention_dropout
      a	:     Union[str, Any]							=  activation_dropout
      a	:     Tuple							=  activation_function
      a	:     Any							=  init_std
      a	:     Any							=  encoder_layerdrop
      a	:     Tuple							=  decoder_layerdrop
      a	:     Optional[Any]							=  use_cache
      a	:     Any							=  encoder_layers
      a	:     str							=  scale_embedding  # scale factor will be sqrt(d_model) if True

      super().__init__(
          pad_token_id=A							,						bos_token_id=A							,						eos_token_id=A							,						is_encoder_decoder=A							,						decoder_start_token_id=A							,						**A							,						)






class 		snake_case    (    UpperCAmelCase			):





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


      '''simple docstring'''




      a	:     Tuple							=  OrderedDict(
          [
              ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
              ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
          ]     )

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

      if self.use_past:
        self.fill_with_past_key_values_(A							,						direction='inputs'     )
      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	:     Tuple							=  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	:     str							=  [' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
      a	:     Optional[Any]							=  dict(tokenizer(A							,						return_tensors=A     )     )
      return common_inputs





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


      '''simple docstring'''




      a	:     List[str]							=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          A							,						A							,						A							,						A							,						A     )

      # Generate decoder inputs
      a	:     str							=  seq_length if not self.use_past else 1
      a	:     List[str]							=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          A							,						A							,						A							,						A							,						A     )
      a	:     Optional[int]							=  {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
      a	:     Optional[Any]							=  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	:     str							=  common_inputs['input_ids'].shape
        a	:     List[str]							=  common_inputs['decoder_input_ids'].shape[1]
        a,	a	:     Union[str, Any]							=  self.num_attention_heads
        a	:     Dict							=  (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        a	:     Dict							=  decoder_seq_length + 3
        a	:     List[Any]							=  (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

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

        a	:     Dict							=  []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        a,	a	:     Tuple							=  self.num_layers
        a	:     Union[str, Any]							=  min(A							,						A     )
        a	:     Union[str, Any]							=  max(A							,						A     ) - min_num_layers
        a	:     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	:     Tuple							=  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




    __magic_name__       	=	_generate_dummy_inputs_for_default_and_seqaseq_lm












"""simple docstring"""


import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info




def      snake_case    (A_ :Union[str, Any] ):







  '''simple docstring'''

  class 		snake_case    :





      def __init__(				self      :   Tuple							,						A      :   Dict     ):


        '''simple docstring'''




        assert isinstance(A							,						(bytes, str)     )
        a	:     Union[str, Any]							=  content





      def 							lowerCamelCase__ (				self      :   str     ):


        '''simple docstring'''




        return json.loads(self.content     )

  def mock_response(*A_ :Union[str, Any]      ,     **A_ :Tuple ):
    assert args[0] == AUTHENTICATED_USER_ENDPOINT
    assert "Authorization" in kwargs["headers"]
    assert kwargs["headers"]["Authorization"].startswith('token ' )
    assert "Accept" in kwargs["headers"]
    return FakeResponse(B'{"login":"test","id":1}' )

  monkeypatch.setattr(A_      ,     'get'      ,     A_ )
  a	:     List[str]							=  fetch_github_info('token' )
  assert result["login"] == "test"
  assert result["id"] == 1




