




"""simple docstring"""






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


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    '''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__			(       UpperCamelCase_  ):
						snake_case__             = '''blenderbot-small'''
						snake_case__             = ['''past_key_values''']
						snake_case__             = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





						def __init__(							self      :	Tuple     ,a__      :	List[Any]=5_0265     ,a__      :	Tuple=512     ,a__      :	Optional[int]=8     ,a__      :	Union[str, Any]=2048     ,a__      :	str=16     ,a__      :	Optional[Any]=8     ,a__      :	Any=2048     ,a__      :	List[str]=16     ,a__      :	Dict=0.0     ,a__      :	List[Any]=0.0     ,a__      :	str=True     ,a__      :	Optional[Any]=True     ,a__      :	Dict="gelu"     ,a__      :	int=512     ,a__      :	Union[str, Any]=0.1     ,a__      :	Union[str, Any]=0.0     ,a__      :	Union[str, Any]=0.0     ,a__      :	Union[str, Any]=0.02     ,a__      :	int=1     ,a__      :	Any=False     ,a__      :	List[str]=0     ,a__      :	Optional[Any]=1     ,a__      :	List[str]=2     ,a__      :	Dict=2     ,**a__      :	List[Any]     ,)						->       str:




											"""simple docstring"""







											_lowerCAmelCase:Tuple									=vocab_size
											_lowerCAmelCase:str									=max_position_embeddings
											_lowerCAmelCase:List[Any]									=d_model
											_lowerCAmelCase:Optional[int]									=encoder_ffn_dim
											_lowerCAmelCase:Tuple									=encoder_layers
											_lowerCAmelCase:Tuple									=encoder_attention_heads
											_lowerCAmelCase:Dict									=decoder_ffn_dim
											_lowerCAmelCase:Optional[int]									=decoder_layers
											_lowerCAmelCase:List[str]									=decoder_attention_heads
											_lowerCAmelCase:Union[str, Any]									=dropout
											_lowerCAmelCase:Optional[int]									=attention_dropout
											_lowerCAmelCase:Dict									=activation_dropout
											_lowerCAmelCase:List[str]									=activation_function
											_lowerCAmelCase:str									=init_std
											_lowerCAmelCase:List[str]									=encoder_layerdrop
											_lowerCAmelCase:Tuple									=decoder_layerdrop
											_lowerCAmelCase:Optional[Any]									=use_cache
											_lowerCAmelCase:str									=encoder_layers
											_lowerCAmelCase:int									=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__     ,forced_eos_token_id=a__     ,**a__     ,)




class   a__			(       UpperCamelCase_  ):





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




											"""simple docstring"""







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

																if self.use_past:
																					_lowerCAmelCase:List[str]									={0: '''batch'''}
																					_lowerCAmelCase:Any									={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																					_lowerCAmelCase:str									={0: '''batch''', 1: '''decoder_sequence'''}
																					_lowerCAmelCase:List[str]									={0: '''batch''', 1: '''decoder_sequence'''}

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




											"""simple docstring"""







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





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




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a__     ,a__     ,a__     ,a__     ,a__)

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

																_lowerCAmelCase:Any									=torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(a__     ,a__)]     ,dim=1)

																_lowerCAmelCase:List[str]									=[]
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_lowerCAmelCase:Union[str, Any]									=self.num_layers
																_lowerCAmelCase:List[str]									=min(a__     ,a__)
																_lowerCAmelCase:str									=max(a__     ,a__) - min_num_layers
																_lowerCAmelCase:List[Any]									='''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.
																_lowerCAmelCase:Optional[Any]									=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





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




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    a__     ,a__     ,a__     ,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
																_lowerCAmelCase:List[str]									=common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																_lowerCAmelCase:str									=seqlen + 2
																_lowerCAmelCase:Tuple									=self.num_layers
																_lowerCAmelCase:str									=self.num_attention_heads
																_lowerCAmelCase:List[Any]									=(
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																_lowerCAmelCase:List[Any]									=common_inputs['''attention_mask'''].dtype
																_lowerCAmelCase:int									=torch.cat(
																    [common_inputs['''attention_mask'''], torch.ones(a__     ,a__     ,dtype=a__)]     ,dim=1)
																_lowerCAmelCase:List[str]									=[
																    (torch.zeros(a__), torch.zeros(a__)) for _ in range(a__)
																]
											return common_inputs





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




											"""simple docstring"""







											_lowerCAmelCase:Union[str, Any]									=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
											_lowerCAmelCase:Optional[Any]									=tokenizer.num_special_tokens_to_add(a__)
											_lowerCAmelCase:Dict									=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
											_lowerCAmelCase:Union[str, Any]									=[''' '''.join([tokenizer.unk_token]) * seq_length] * batch_size
											_lowerCAmelCase:int									=dict(tokenizer(a__     ,return_tensors=a__))
											return common_inputs





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




											"""simple docstring"""







											if self.task in ["default", "seq2seq-lm"]:
																_lowerCAmelCase:str									=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    a__     ,batch_size=a__     ,seq_length=a__     ,is_pair=a__     ,framework=a__)

											elif self.task == "causal-lm":
																_lowerCAmelCase:Dict									=self._generate_dummy_inputs_for_causal_lm(
																    a__     ,batch_size=a__     ,seq_length=a__     ,is_pair=a__     ,framework=a__)
											else:
																_lowerCAmelCase:Tuple									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    a__     ,batch_size=a__     ,seq_length=a__     ,is_pair=a__     ,framework=a__)

											return common_inputs







						def       __UpperCamelCase     (							self      :	List[str]     ,a__      :	List[Any]     ,a__      :	str     ,a__      :	Optional[int]     ,a__      :	Any)						->       List[Any]:




											"""simple docstring"""







											if self.task in ["default", "seq2seq-lm"]:
																_lowerCAmelCase:Dict									=super()._flatten_past_key_values_(a__     ,a__     ,a__     ,a__)
											else:
																_lowerCAmelCase:List[str]									=super(a__     ,self)._flatten_past_key_values_(
																    a__     ,a__     ,a__     ,a__)












"""simple docstring"""






def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return x if y == 0 else greatest_common_divisor(snake_case      ,       x % y    )





def 							UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					int    ):
					return (x * y) // greatest_common_divisor(snake_case      ,       snake_case    )





def 							UpperCAmelCase		(		snake_case			:					int = 20    ):
					_lowerCAmelCase:List[Any]									=  1
					for i in range(1      ,       n + 1    ):
										_lowerCAmelCase:List[str]									=  lcm(snake_case      ,       snake_case    )
					return g


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






