

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     					=  logging.get_logger(__name__)

__UpperCAmelCase     					=  {
    '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 A__     (			A			):



 """simple docstring"""







 _lowercase  :       Dict  			=    '''m2m_100'''
 _lowercase  :       List[Any]  			=    ['''past_key_values''']
 _lowercase  :       int  			=    {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

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






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     vocab_size
      _lowerCAmelCase :       Optional[Any]	   =     max_position_embeddings
      _lowerCAmelCase :       List[str]	   =     d_model
      _lowerCAmelCase :       str	   =     encoder_ffn_dim
      _lowerCAmelCase :       List[Any]	   =     encoder_layers
      _lowerCAmelCase :       List[Any]	   =     encoder_attention_heads
      _lowerCAmelCase :       Tuple	   =     decoder_ffn_dim
      _lowerCAmelCase :       Dict	   =     decoder_layers
      _lowerCAmelCase :       Union[str, Any]	   =     decoder_attention_heads
      _lowerCAmelCase :       Any	   =     dropout
      _lowerCAmelCase :       Optional[Any]	   =     attention_dropout
      _lowerCAmelCase :       Optional[Any]	   =     activation_dropout
      _lowerCAmelCase :       Optional[Any]	   =     activation_function
      _lowerCAmelCase :       Optional[int]	   =     init_std
      _lowerCAmelCase :       Tuple	   =     encoder_layerdrop
      _lowerCAmelCase :       Dict	   =     decoder_layerdrop
      _lowerCAmelCase :       str	   =     use_cache
      _lowerCAmelCase :       List[str]	   =     encoder_layers
      _lowerCAmelCase :       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 A__     (			A			):



 """simple docstring"""







 @property
 def  __magic_name__							(		self			:						Any							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     OrderedDict(
          [
              ("input_ids", {0: "batch", 1: "encoder_sequence"}),
              ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
          ]							)

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

      if self.use_past:
           self.fill_with_past_key_values_(A_  ,    direction="inputs"							)
      return common_inputs

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






      '''simple docstring'''




      _lowerCAmelCase :       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
      _lowerCAmelCase :       str	   =     tokenizer.num_special_tokens_to_add(A_							)
      _lowerCAmelCase :       int	   =     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
      _lowerCAmelCase :       Dict	   =     [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
      _lowerCAmelCase :       Any	   =     dict(tokenizer(A_  ,    return_tensors=A_							)							)
      return common_inputs

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






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
          A_  ,    A_  ,    A_  ,    A_  ,    A_							)

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

           _lowerCAmelCase :       Optional[Any]	   =     torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(A_  ,    A_							)]  ,    dim=1							)

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


 _lowercase  :       str  			=    _generate_dummy_inputs_for_default_and_seqaseq_lm









import unittest
from timeit import timeit







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     first_num if first_num >= second_num else second_num
     _lowerCAmelCase :       List[str]	   =     max_num
     while (common_mult % first_num > 0) or (common_mult % second_num > 0):
          common_mult += max_num
     return common_mult







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return b if a == 0 else greatest_common_divisor(b % a		,				SCREAMING_SNAKE_CASE					)







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     return first_num // greatest_common_divisor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					) * second_num







def 						_snake_case		(     )							->   str:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     (
         "from __main__ import least_common_multiple_slow, least_common_multiple_fast"
     )
     print(
         "least_common_multiple_slow():"		,				timeit("least_common_multiple_slow(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)
     print(
         "least_common_multiple_fast():"		,				timeit("least_common_multiple_fast(1000, 999)"		,				setup=SCREAMING_SNAKE_CASE					)		,				)





class A__     (			unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       Union[str, Any]  			=    (
     (10, 20),
     (13, 15),
     (4, 31),
     (10, 42),
     (43, 34),
     (5, 12),
     (12, 25),
     (10, 25),
     (6, 9),
 )
 _lowercase  :       Dict  			=    (20, 195, 124, 210, 1_462, 60, 300, 50, 18)

 def  __magic_name__							(		self			:						int							):






      '''simple docstring'''




      for i, (first_num, second_num) in enumerate(self.test_inputs							):
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_slow(A_  ,    A_							)
           _lowerCAmelCase :       Optional[int]	   =     least_common_multiple_fast(A_  ,    A_							)
           with self.subTest(i=A_							):
                self.assertEqual(A_  ,    self.expected_results[i]							)
                self.assertEqual(A_  ,    self.expected_results[i]							)


if __name__ == "__main__":
  benchmark()
  unittest.main()






