





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


__lowercase       :Any       =  logging.get_logger(__name__)

__lowercase       :str       =  {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}



class    _a     (				__a							):






							"""simple docstring"""

							snake_case_							 =						"blenderbot-small"
							snake_case_							 =						["past_key_values"]
							snake_case_							 =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

							def __init__(      self				:     int							,	a				:     Dict=5_02_65							,	a				:     Any=5_12							,	a				:     int=8							,	a				:     Optional[int]=20_48							,	a				:     Tuple=16							,	a				:     Optional[Any]=8							,	a				:     Optional[int]=20_48							,	a				:     str=16							,	a				:     Optional[Any]=0.0							,	a				:     int=0.0							,	a				:     str=True							,	a				:     int=True							,	a				:     Dict="gelu"							,	a				:     Any=5_12							,	a				:     str=0.1							,	a				:     int=0.0							,	a				:     str=0.0							,	a				:     Union[str, Any]=0.02							,	a				:     Dict=1							,	a				:     Union[str, Any]=False							,	a				:     Tuple=0							,	a				:     List[Any]=1							,	a				:     Union[str, Any]=2							,	a				:     int=2							,	**a				:     Union[str, Any]							,	)	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					vocab_size
													SCREAMING_SNAKE_CASE__     :				int   =					max_position_embeddings
													SCREAMING_SNAKE_CASE__     :				Dict   =					d_model
													SCREAMING_SNAKE_CASE__     :				Dict   =					encoder_ffn_dim
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					encoder_layers
													SCREAMING_SNAKE_CASE__     :				Any   =					encoder_attention_heads
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					decoder_ffn_dim
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					decoder_layers
													SCREAMING_SNAKE_CASE__     :				Dict   =					decoder_attention_heads
													SCREAMING_SNAKE_CASE__     :				List[str]   =					dropout
													SCREAMING_SNAKE_CASE__     :				List[str]   =					attention_dropout
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					activation_dropout
													SCREAMING_SNAKE_CASE__     :				Any   =					activation_function
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					init_std
													SCREAMING_SNAKE_CASE__     :				str   =					encoder_layerdrop
													SCREAMING_SNAKE_CASE__     :				Tuple   =					decoder_layerdrop
													SCREAMING_SNAKE_CASE__     :				Dict   =					use_cache
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					encoder_layers
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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_							,	forced_eos_token_id=lowerCAmelCase_							,	**lowerCAmelCase_							,	)







class    _a     (				__a							):






							"""simple docstring"""

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







													if self.task in ["default", "seq2seq-lm"]:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]    )

																			if self.use_past:
																									SCREAMING_SNAKE_CASE__     :				Dict   =					{0: "batch"}
																									SCREAMING_SNAKE_CASE__     :				Dict   =					{0: "batch", 1: "past_decoder_sequence + sequence"}
																			else:
																									SCREAMING_SNAKE_CASE__     :				Tuple   =					{0: "batch", 1: "decoder_sequence"}
																									SCREAMING_SNAKE_CASE__     :				List[str]   =					{0: "batch", 1: "decoder_sequence"}

																			if self.use_past:
																									self.fill_with_past_key_values_(lowerCAmelCase_							,	direction="inputs"    )
													elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]    )
																			if self.use_past:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				int   =					self.num_layers
																									for i in range(lowerCAmelCase_    ):
																															SCREAMING_SNAKE_CASE__     :				str   =					{0: "batch", 2: "past_sequence + sequence"}
																															SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					{0: "batch", 2: "past_sequence + sequence"}
													else:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
																			        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
																			    ]    )

													return common_inputs

							@property
							def      A_  (      self				:     List[Any]    )	->Mapping[str, Mapping[int, str]]:







													if self.task in ["default", "seq2seq-lm"]:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					super().outputs
													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					super(lowerCAmelCase_							,	self    ).outputs
																			if self.use_past:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.num_layers
																									for i in range(lowerCAmelCase_    ):
																															SCREAMING_SNAKE_CASE__     :				Any   =					{0: "batch", 2: "past_sequence + sequence"}
																															SCREAMING_SNAKE_CASE__     :				Any   =					{0: "batch", 2: "past_sequence + sequence"}
													return common_outputs

							def      A_  (      self				:     Dict							,	a				:     List[str]							,	a				:     str = -1							,	a				:     List[str] = -1							,	a				:     Optional[int] = False							,	a				:     List[Any] = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				str   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_    )

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

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					torch.cat(
																			    [common_inputs["decoder_attention_mask"], torch.ones(lowerCAmelCase_							,	lowerCAmelCase_    )]							,	dim=1    )

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

							def      A_  (      self				:     Optional[Any]							,	a				:     List[Any]							,	a				:     str = -1							,	a				:     List[str] = -1							,	a				:     List[Any] = False							,	a				:     Dict = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	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
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					seqlen + 2
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					self.num_layers
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					self.num_attention_heads
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					(
																			    batch,
																			    num_encoder_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)

																			SCREAMING_SNAKE_CASE__     :				List[str]   =					common_inputs["attention_mask"].dtype
																			SCREAMING_SNAKE_CASE__     :				Dict   =					torch.cat(
																			    [common_inputs["attention_mask"], torch.ones(lowerCAmelCase_							,	lowerCAmelCase_							,	dtype=lowerCAmelCase_    )]							,	dim=1    )
																			SCREAMING_SNAKE_CASE__     :				Dict   =					[
																			    (torch.zeros(lowerCAmelCase_    ), torch.zeros(lowerCAmelCase_    )) for _ in range(lowerCAmelCase_    )
																			]
													return common_inputs

							def      A_  (      self				:     Optional[int]							,	a				:     List[Any]							,	a				:     List[str] = -1							,	a				:     Union[str, Any] = -1							,	a				:     Union[str, Any] = False							,	a				:     List[Any] = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				int   =					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
													SCREAMING_SNAKE_CASE__     :				Dict   =					tokenizer.num_special_tokens_to_add(lowerCAmelCase_    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					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
													SCREAMING_SNAKE_CASE__     :				Tuple   =					[" ".join([tokenizer.unk_token]    ) * seq_length] * batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					dict(tokenizer(lowerCAmelCase_							,	return_tensors=lowerCAmelCase_    )    )
													return common_inputs

							def      A_  (      self				:     Dict							,	a				:     Tuple							,	a				:     int = -1							,	a				:     List[Any] = -1							,	a				:     Optional[int] = False							,	a				:     Optional[Any] = None							,	)	->Mapping[str, Any]:







													if self.task in ["default", "seq2seq-lm"]:
																			SCREAMING_SNAKE_CASE__     :				int   =					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    lowerCAmelCase_							,	batch_size=lowerCAmelCase_							,	seq_length=lowerCAmelCase_							,	is_pair=lowerCAmelCase_							,	framework=lowerCAmelCase_    )

													elif self.task == "causal-lm":
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self._generate_dummy_inputs_for_causal_lm(
																			    lowerCAmelCase_							,	batch_size=lowerCAmelCase_							,	seq_length=lowerCAmelCase_							,	is_pair=lowerCAmelCase_							,	framework=lowerCAmelCase_    )
													else:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																			    lowerCAmelCase_							,	batch_size=lowerCAmelCase_							,	seq_length=lowerCAmelCase_							,	is_pair=lowerCAmelCase_							,	framework=lowerCAmelCase_    )

													return common_inputs






							def      A_  (      self				:     Optional[int]							,	a				:     Optional[int]							,	a				:     Optional[Any]							,	a				:     List[Any]							,	a				:     str    )	->Tuple:







													if self.task in ["default", "seq2seq-lm"]:
																			SCREAMING_SNAKE_CASE__     :				int   =					super()._flatten_past_key_values_(lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_    )
													else:
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					super(lowerCAmelCase_							,	self    )._flatten_past_key_values_(
																			    lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_							,	lowerCAmelCase_    )










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 1_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Dict   =					-1
						SCREAMING_SNAKE_CASE__     :				str   =					0
						for a in range(1			,   n // 3     ):
												# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
												SCREAMING_SNAKE_CASE__     :				Tuple   =					(n * n - 2 * a * n) // (2 * n - 2 * a)
												SCREAMING_SNAKE_CASE__     :				Dict   =					n - a - b
												if c * c == (a * a + b * b):
																		SCREAMING_SNAKE_CASE__     :				str   =					a * b * c
																		if candidate >= product:
																								SCREAMING_SNAKE_CASE__     :				List[str]   =					candidate
						return product


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