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      :				Dict 				=    logging.get_logger(__name__)

_A      :				Tuple 				=    {
    '''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     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	List[Any]				     = 'm2m_100'
 _SCREAMING_SNAKE_CASE					:	Union[str, Any]				     = ['past_key_values']
 _SCREAMING_SNAKE_CASE					:	Union[str, Any]				     = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(				self       :     str				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=12_81_12				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=10_24				,	SCREAMING_SNAKE_CASE__       :     Any=12				,	SCREAMING_SNAKE_CASE__       :     Tuple=40_96				,	SCREAMING_SNAKE_CASE__       :     Any=16				,	SCREAMING_SNAKE_CASE__       :     Any=12				,	SCREAMING_SNAKE_CASE__       :     Dict=40_96				,	SCREAMING_SNAKE_CASE__       :     List[str]=16				,	SCREAMING_SNAKE_CASE__       :     List[Any]=0.0_5				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=0.0_5				,	SCREAMING_SNAKE_CASE__       :     List[Any]=True				,	SCREAMING_SNAKE_CASE__       :     List[Any]=True				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]="relu"				,	SCREAMING_SNAKE_CASE__       :     Optional[Any]=10_24				,	SCREAMING_SNAKE_CASE__       :     List[str]=0.1				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]=0.1				,	SCREAMING_SNAKE_CASE__       :     int=0.0				,	SCREAMING_SNAKE_CASE__       :     List[str]=0.0_2				,	SCREAMING_SNAKE_CASE__       :     Optional[int]=2				,	SCREAMING_SNAKE_CASE__       :     Tuple=True				,	SCREAMING_SNAKE_CASE__       :     List[str]=1				,	SCREAMING_SNAKE_CASE__       :     int=0				,	SCREAMING_SNAKE_CASE__       :     int=2				,	**SCREAMING_SNAKE_CASE__       :     Tuple				,	)  ->   Optional[Any]:
  __lowerCAmelCase    							=						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

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







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 @property
 def a (				self       :     Optional[Any]							)  ->   List[Any]:
  __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_(SCREAMING_SNAKE_CASE__				,	direction="""inputs"""							)
  return common_inputs
 def a (				self       :     Dict				,	SCREAMING_SNAKE_CASE__       :     PreTrainedTokenizer				,	SCREAMING_SNAKE_CASE__       :     int = -1				,	SCREAMING_SNAKE_CASE__       :     int = -1				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     Optional[TensorType] = None				,	)  ->   Any:
  __lowerCAmelCase    							=						compute_effective_axis_dimension(
      SCREAMING_SNAKE_CASE__				,	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(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						compute_effective_axis_dimension(
      SCREAMING_SNAKE_CASE__				,	fixed_dimension=OnnxConfig.default_fixed_sequence				,	num_token_to_add=SCREAMING_SNAKE_CASE__							)

  # Generate dummy inputs according to compute batch and sequence
  __lowerCAmelCase    							=						[" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
  __lowerCAmelCase    							=						dict(tokenizer(SCREAMING_SNAKE_CASE__				,	return_tensors=SCREAMING_SNAKE_CASE__							)							)
  return common_inputs
 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     PreTrainedTokenizer				,	SCREAMING_SNAKE_CASE__       :     int = -1				,	SCREAMING_SNAKE_CASE__       :     int = -1				,	SCREAMING_SNAKE_CASE__       :     bool = False				,	SCREAMING_SNAKE_CASE__       :     Optional[TensorType] = None				,	)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
      SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)

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

  if self.use_past:
   if not is_torch_available():
    raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""							)
   else:
    import torch
   __lowerCAmelCase    							=						common_inputs["input_ids"].shape
   __lowerCAmelCase    							=						common_inputs["decoder_input_ids"].shape[1]
   __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(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)]				,	dim=1							)

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

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





 _SCREAMING_SNAKE_CASE					:	str				     = _generate_dummy_inputs_for_default_and_seqaseq_lm







'''simple docstring'''

import functools
from math import sqrt
from time import time







def 					UpperCamelCase_						( snake_case_	:	Dict						,		*snake_case_	:	Union[str, Any]						,		**snake_case_	:	List[str]				) ->					List[str]:






 '''simple docstring'''

 __lowerCAmelCase    							=						time()
 __lowerCAmelCase    							=						func(*snake_case_						,		**snake_case_				)
 __lowerCAmelCase    							=						time()
 if int(end - start				) > 0:
  print(f"""{func.__name__} runtime: {(end - start):0.4f} s"""				)
 else:
  print(f"""{func.__name__} runtime: {(end - start) * 10_00:0.4f} ms"""				)
 return output







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 if n == 0:
  return [0]
 __lowerCAmelCase    							=						[0, 1]
 for _ in range(n - 1				):
  fib.append(fib[-1] + fib[-2]				)
 return fib







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 def fib_recursive_term(snake_case_	:	int				) -> int:
  if i < 0:
   raise Exception("""n is negative"""				)
  if i < 2:
   return i
  return fib_recursive_term(i - 1				) + fib_recursive_term(i - 2				)

 if n < 0:
  raise Exception("""n is negative"""				)
 return [fib_recursive_term(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 @functools.cache
 def fib_recursive_term(snake_case_	:	int				) -> int:
  if i < 0:
   raise Exception("""n is negative"""				)
  if i < 2:
   return i
  return fib_recursive_term(i - 1				) + fib_recursive_term(i - 2				)

 if n < 0:
  raise Exception("""n is negative"""				)
 return [fib_recursive_term(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 # Cache must be outside recursuive function
 # other it will reset every time it calls itself.
 __lowerCAmelCase    							=						{0: 0, 1: 1, 2: 1}  # Prefilled cache

 def rec_fn_memoized(snake_case_	:	int				) -> int:
  if num in cache:
   return cache[num]

  __lowerCAmelCase    							=						rec_fn_memoized(num - 1				) + rec_fn_memoized(num - 2				)
  __lowerCAmelCase    							=						value
  return value

 return [rec_fn_memoized(snake_case_				) for i in range(n + 1				)]







def 					UpperCamelCase_						( snake_case_	:	int				) ->					list[int]:






 '''simple docstring'''

 if n < 0:
  raise Exception("""n is negative"""				)
 if n >= 14_75:
  raise Exception("""n is too large"""				)
 __lowerCAmelCase    							=						sqrt(5				)
 __lowerCAmelCase    							=						(1 + sqrt_a) / 2
 return [round(phi**i / sqrt_a				) for i in range(n + 1				)]


if __name__ == "__main__":
 _A      :				Optional[Any] 				=    30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)






