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  :					Tuple          =  logging.get_logger(__name__)

__a  :					Optional[int]          =  {
    """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        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			List[Any]      			=						'''m2m_100'''
			__a  :			Union[str, Any]      			=						['''past_key_values''']
			__a  :			Any      			=						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(       self				,       lowerCAmelCase__=12_81_12				,       lowerCAmelCase__=10_24				,       lowerCAmelCase__=12				,       lowerCAmelCase__=40_96				,       lowerCAmelCase__=16				,       lowerCAmelCase__=12				,       lowerCAmelCase__=40_96				,       lowerCAmelCase__=16				,       lowerCAmelCase__=0.05				,       lowerCAmelCase__=0.05				,       lowerCAmelCase__=True				,       lowerCAmelCase__=True				,       lowerCAmelCase__="relu"				,       lowerCAmelCase__=10_24				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.0				,       lowerCAmelCase__=0.02				,       lowerCAmelCase__=2				,       lowerCAmelCase__=True				,       lowerCAmelCase__=1				,       lowerCAmelCase__=0				,       lowerCAmelCase__=2				,       **lowerCAmelCase__				,       )					-> int:

							'''simple docstring'''


							__lowercase			      =							vocab_size
							__lowercase			      =							max_position_embeddings
							__lowercase			      =							d_model
							__lowercase			      =							encoder_ffn_dim
							__lowercase			      =							encoder_layers
							__lowercase			      =							encoder_attention_heads
							__lowercase			      =							decoder_ffn_dim
							__lowercase			      =							decoder_layers
							__lowercase			      =							decoder_attention_heads
							__lowercase			      =							dropout
							__lowercase			      =							attention_dropout
							__lowercase			      =							activation_dropout
							__lowercase			      =							activation_function
							__lowercase			      =							init_std
							__lowercase			      =							encoder_layerdrop
							__lowercase			      =							decoder_layerdrop
							__lowercase			      =							use_cache
							__lowercase			      =							encoder_layers
							__lowercase			      =							scale_embedding  # scale factor will be sqrt(d_model) if True

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




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


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

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

							if self.use_past:
											self.fill_with_past_key_values_(lowerCAmelCase__				,       direction='''inputs'''						)
							return common_inputs





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							compute_effective_axis_dimension(
							    lowerCAmelCase__				,       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
							__lowercase			      =							tokenizer.num_special_tokens_to_add(lowerCAmelCase__						)
							__lowercase			      =							compute_effective_axis_dimension(
							    lowerCAmelCase__				,       fixed_dimension=OnnxConfig.default_fixed_sequence				,       num_token_to_add=lowerCAmelCase__						)

							# Generate dummy inputs according to compute batch and sequence
							__lowercase			      =							[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
							__lowercase			      =							dict(tokenizer(lowerCAmelCase__				,       return_tensors=lowerCAmelCase__						)						)
							return common_inputs





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = -1				,       lowerCAmelCase__ = False				,       lowerCAmelCase__ = None				,       )					-> Mapping[str, Any]:

							'''simple docstring'''


							__lowercase			      =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)

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

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

											__lowercase			      =							torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase__				,       lowerCAmelCase__						)]				,       dim=1						)

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

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



			__a  :			Union[str, Any]      			=						_generate_dummy_inputs_for_default_and_seqaseq_lm
import timeit




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							if isinstance(lowerCAmelCase__				,       lowerCAmelCase__						):  # Initializes a matrix identical to the one provided.
											__lowercase			      =							arg
											__lowercase			      =							len(lowerCAmelCase__						)
							else:  # Initializes a square matrix of the given size and set values to zero.
											__lowercase			      =							arg
											__lowercase			      =							[[0 for _ in range(self.n						)] for _ in range(self.n						)]





			def __mul__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							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     UpperCAmelCase						(   lowercase							,      lowercase							):







				"""simple docstring"""



				__lowercase			      =							Matrix([[1, 0], [0, 1]]							)
				while b > 0:
								if b & 1:
												matrix *= a
								a *= a
								b >>= 1
				return matrix





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa
				__lowercase			      =							Matrix([[1, 1], [1, 0]]							)
				__lowercase			      =							modular_exponentiation(lowercase							,      n - 2							)
				return fa * matrix.t[0][0] + fa * matrix.t[0][1]





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase							):







				"""simple docstring"""



				if n == 1:
								return fa
				elif n == 2:
								return fa

				__lowercase			      =							fa
				__lowercase			      =							fa
				n -= 2

				while n > 0:
								__lowercase      ,					__lowercase			      =							fn_a + fn_a, fn_a
								n -= 1

				return fn_a





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
				__lowercase			      =							'''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print('''With matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							'''
from random import randint
from __main__ import simple_fibonacci
'''
				__lowercase			      =							'''simple_fibonacci(randint(1,70000), 1, 1)'''
				__lowercase			      =							timeit.timeit(setup=lowercase							,      stmt=lowercase							,      number=100							)
				print(
				    '''Without matrix exponentiation the average execution time is '''							,      exec_time / 100							)
				return exec_time





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				matrix_exponentiation_time()
				simple_fibonacci_time()


if __name__ == "__main__":
		main()