




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  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '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(					_A					):



	"""simple docstring"""






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

	def __init__(					self	,				lowerCamelCase=128112	,				lowerCamelCase=1024	,				lowerCamelCase=12	,				lowerCamelCase=4096	,				lowerCamelCase=16	,				lowerCamelCase=12	,				lowerCamelCase=4096	,				lowerCamelCase=16	,				lowerCamelCase=0.05	,				lowerCamelCase=0.05	,				lowerCamelCase=True	,				lowerCamelCase=True	,				lowerCamelCase="relu"	,				lowerCamelCase=1024	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=0.0	,				lowerCamelCase=0.02	,				lowerCamelCase=2	,				lowerCamelCase=True	,				lowerCamelCase=1	,				lowerCamelCase=0	,				lowerCamelCase=2	,				**lowerCamelCase	,				) ->      Union[str, Any]:





								"""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=UpperCamelCase__	,				bos_token_id=UpperCamelCase__	,				eos_token_id=UpperCamelCase__	,				is_encoder_decoder=UpperCamelCase__	,				decoder_start_token_id=UpperCamelCase__	,				**UpperCamelCase__	,				)







class 				UpperCAmelCase(					_A					):



	"""simple docstring"""






	@property
	def    __a     (					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_(UpperCamelCase__	,				direction="inputs"    )
								return common_inputs

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





								"""simple docstring"""


								lowercase__        =							compute_effective_axis_dimension(
								    UpperCamelCase__	,				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(UpperCamelCase__    )
								lowercase__        =							compute_effective_axis_dimension(
								    UpperCamelCase__	,				fixed_dimension=OnnxConfig.default_fixed_sequence	,				num_token_to_add=UpperCamelCase__    )

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

	def    __a     (					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(
								    UpperCamelCase__	,				UpperCamelCase__	,				UpperCamelCase__	,				UpperCamelCase__	,				UpperCamelCase__    )

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

								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(UpperCamelCase__	,				UpperCamelCase__    )]	,				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(UpperCamelCase__	,				UpperCamelCase__    )
															lowercase__        =							max(UpperCamelCase__	,				UpperCamelCase__    ) - min_num_layers
															lowercase__        =							"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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







	a     : Optional[Any]	  =					_generate_dummy_inputs_for_default_and_seqaseq_lm





def  snake_case_					(		)      ->			int:

							for a in range(3_00    ):
														for b in range(a + 1    ,4_00    ):
																					for c in range(b + 1    ,5_00    ):
																												if (a + b + c) == 10_00 and (a**2) + (b**2) == (c**2):
																																			return a * b * c

							return -1
def  snake_case_					(		)      ->			int:

							for a in range(3_00    ):
														for b in range(4_00    ):
																					lowercase__  :	Dict        =							10_00 - a - b
																					if a < b < c and (a**2) + (b**2) == (c**2):
																												return a * b * c

							return -1
def  snake_case_					(		)      ->			None:
							import timeit

							print(
							    timeit.timeit("solution()"    ,setup="from __main__ import solution"    ,number=10_00    )    )
							print(
							    timeit.timeit(
							        "solution_fast()"    ,setup="from __main__ import solution_fast"    ,number=10_00    )    )


if __name__ == "__main__":
						print(f'{solution() = }')