


"""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


a_											=     logging.get_logger(__name__)

a_											=     {
    "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   (	lowerCamelCase__):
		__UpperCamelCase							     =      'm2m_100'
		__UpperCamelCase							     =      ['past_key_values']
		__UpperCamelCase							     =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}







		def __init__(       self   :							int     , a__   :							Optional[Any]=12_81_12     , a__   :							str=10_24     , a__   :							Tuple=12     , a__   :							Any=40_96     , a__   :							str=16     , a__   :							List[str]=12     , a__   :							int=40_96     , a__   :							List[Any]=16     , a__   :							Tuple=0.0_5     , a__   :							Tuple=0.0_5     , a__   :							List[Any]=True     , a__   :							int=True     , a__   :							str="relu"     , a__   :							str=10_24     , a__   :							str=0.1     , a__   :							str=0.1     , a__   :							List[str]=0.0     , a__   :							List[str]=0.0_2     , a__   :							Any=2     , a__   :							List[str]=True     , a__   :							Tuple=1     , a__   :							Optional[Any]=0     , a__   :							Optional[Any]=2     , **a__   :							Optional[Any]     , )							->    Dict:





						'''simple docstring'''







						_A       	=      vocab_size
						_A       	=      max_position_embeddings
						_A       	=      d_model
						_A       	=      encoder_ffn_dim
						_A       	=      encoder_layers
						_A       	=      encoder_attention_heads
						_A       	=      decoder_ffn_dim
						_A       	=      decoder_layers
						_A       	=      decoder_attention_heads
						_A       	=      dropout
						_A       	=      attention_dropout
						_A       	=      activation_dropout
						_A       	=      activation_function
						_A       	=      init_std
						_A       	=      encoder_layerdrop
						_A       	=      decoder_layerdrop
						_A       	=      use_cache
						_A       	=      encoder_layers
						_A       	=      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   (	lowerCamelCase__):







		@property
		def 			a_			(       self   :							List[str]		)							->    Union[str, Any]:





						'''simple docstring'''







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

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

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







		def 			a_			(       self   :							Dict     , a__   :							PreTrainedTokenizer     , a__   :							int = -1     , a__   :							int = -1     , a__   :							bool = False     , a__   :							Optional[TensorType] = None     , )							->    Union[str, Any]:





						'''simple docstring'''







						_A       	=      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       	=      tokenizer.num_special_tokens_to_add(__A		)
						_A       	=      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       	=      [''' '''.join([tokenizer.unk_token]		) * seq_length] * batch_size
						_A       	=      dict(tokenizer(__A     , return_tensors=__A		)		)
						return common_inputs







		def 			a_			(       self   :							Any     , a__   :							PreTrainedTokenizer     , a__   :							int = -1     , a__   :							int = -1     , a__   :							bool = False     , a__   :							Optional[TensorType] = None     , )							->    int:





						'''simple docstring'''







						_A       	=      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    __A     , __A     , __A     , __A     , __A		)

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

										_A       	=      torch.cat(
										    [common_inputs["decoder_attention_mask"], torch.ones(__A     , __A		)]     , dim=1		)

										_A       	=      []
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										_A       	=      self.num_layers
										_A       	=      min(__A     , __A		)
										_A       	=      max(__A     , __A		) - min_num_layers
										_A       	=      '''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       	=      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
		__UpperCamelCase							     =      _generate_dummy_inputs_for_default_and_seqaseq_lm


import timeit





class    __UpperCAmelCase	:



   def __init__(				self				:	Dict,						__A				:	Union[str, Any]		):
     if isinstance(__A,						__A		):  # Initializes a matrix identical to the one provided.
       UpperCAmelCase			:				str										=					arg
       UpperCAmelCase			:				Optional[Any]										=					len(__A		)
     else:  # Initializes a square matrix of the given size and set values to zero.
       UpperCAmelCase			:				List[str]										=					arg
       UpperCAmelCase			:				List[Any]										=					[[0 for _ in range(self.n		)] for _ in range(self.n		)]





   def __mul__(				self				:	Dict,						__A				:	List[Any]		):
     UpperCAmelCase			:				Dict										=					Matrix(self.n		)
     for i in range(self.n		):
       for j in range(self.n		):
         for k in range(self.n		):
           matrix.t[i][j] += self.t[i][k] * b.t[k][j]
     return matrix






def      a__	(						UpperCAmelCase				:     List[str]							,					UpperCAmelCase				:     List[Any]     )    ->		str:
  UpperCAmelCase			:				Tuple										=					Matrix([[1, 0], [0, 1]]     )
  while b > 0:
    if b & 1:
      matrix *= a
    a *= a
    b >>= 1
  return matrix

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		str:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa
  UpperCAmelCase			:				List[str]										=					Matrix([[1, 1], [1, 0]]     )
  UpperCAmelCase			:				Union[str, Any]										=					modular_exponentiation(UpperCAmelCase							,					n - 2     )
  return fa * matrix.t[0][0] + fa * matrix.t[0][1]

def      a__	(						UpperCAmelCase				:     Any							,					UpperCAmelCase				:     int							,					UpperCAmelCase				:     List[Any]     )    ->		Optional[Any]:
  # Trivial Cases
  if n == 1:
    return fa
  elif n == 2:
    return fa

  UpperCAmelCase			:				str										=					fa
  UpperCAmelCase			:				List[str]										=					fa
  n -= 2

  while n > 0:
    UpperCAmelCase					,		UpperCAmelCase			:				str										=					fn_a + fn_a, fn_a
    n -= 1

  return fn_a

def      a__	(						)    ->		Any:
  UpperCAmelCase			:				int										=					'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
  UpperCAmelCase			:				Dict										=					'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				Tuple										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print('''With matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		int:
  UpperCAmelCase			:				str										=					'''
from random import randint
from __main__ import simple_fibonacci
'''
  UpperCAmelCase			:				List[str]										=					'''simple_fibonacci(randint(1,70000), 1, 1)'''
  UpperCAmelCase			:				List[str]										=					timeit.timeit(setup=UpperCAmelCase							,					stmt=UpperCAmelCase							,					number=100     )
  print(
      '''Without matrix exponentiation the average execution time is '''							,					exec_time / 100     )
  return exec_time

def      a__	(						)    ->		List[str]:
  matrix_exponentiation_time()
  simple_fibonacci_time()


if __name__ == "__main__":
     main()





