


"""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


__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:    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     (snake_case__):





							'''simple docstring'''

							__lowercase:		str							      =    """blenderbot-small"""
							__lowercase:		Any							      =    ["""past_key_values"""]
							__lowercase:		Optional[int]							      =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



							def __init__(				self							:      Optional[Any]		,       UpperCAmelCase_							:      int=50_265		,       UpperCAmelCase_							:      Dict=512		,       UpperCAmelCase_							:      List[Any]=8		,       UpperCAmelCase_							:      List[Any]=2_048		,       UpperCAmelCase_							:      Optional[Any]=16		,       UpperCAmelCase_							:      List[str]=8		,       UpperCAmelCase_							:      int=2_048		,       UpperCAmelCase_							:      Any=16		,       UpperCAmelCase_							:      Tuple=0.0		,       UpperCAmelCase_							:      str=0.0		,       UpperCAmelCase_							:      Tuple=True		,       UpperCAmelCase_							:      Union[str, Any]=True		,       UpperCAmelCase_							:      List[str]="gelu"		,       UpperCAmelCase_							:      Any=512		,       UpperCAmelCase_							:      Union[str, Any]=0.1		,       UpperCAmelCase_							:      Dict=0.0		,       UpperCAmelCase_							:      Union[str, Any]=0.0		,       UpperCAmelCase_							:      Optional[Any]=0.02		,       UpperCAmelCase_							:      Any=1		,       UpperCAmelCase_							:      Any=False		,       UpperCAmelCase_							:      Optional[int]=0		,       UpperCAmelCase_							:      str=1		,       UpperCAmelCase_							:      List[Any]=2		,       UpperCAmelCase_							:      Optional[Any]=2		,       **UpperCAmelCase_							:      List[str]		,       )					->Optional[int]:



														"""simple docstring"""



														snake_case_           =		vocab_size
														snake_case_           =		max_position_embeddings
														snake_case_           =		d_model
														snake_case_           =		encoder_ffn_dim
														snake_case_           =		encoder_layers
														snake_case_           =		encoder_attention_heads
														snake_case_           =		decoder_ffn_dim
														snake_case_           =		decoder_layers
														snake_case_           =		decoder_attention_heads
														snake_case_           =		dropout
														snake_case_           =		attention_dropout
														snake_case_           =		activation_dropout
														snake_case_           =		activation_function
														snake_case_           =		init_std
														snake_case_           =		encoder_layerdrop
														snake_case_           =		decoder_layerdrop
														snake_case_           =		use_cache
														snake_case_           =		encoder_layers
														snake_case_           =		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_		,       forced_eos_token_id=UpperCAmelCase_		,       **UpperCAmelCase_		,       )





class      __A     (snake_case__):





							'''simple docstring'''

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



														"""simple docstring"""



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

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

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



														"""simple docstring"""



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



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



														"""simple docstring"""



														snake_case_           =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )

														# Generate decoder inputs
														snake_case_           =		seq_length if not self.use_past else 1
														snake_case_           =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														snake_case_           =		{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
														snake_case_           =		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
																					snake_case_       ,						snake_case_           =		common_inputs["""input_ids"""].shape
																					snake_case_           =		common_inputs["""decoder_input_ids"""].shape[1]
																					snake_case_       ,						snake_case_           =		self.num_attention_heads
																					snake_case_           =		(
																					    batch,
																					    num_encoder_attention_heads,
																					    encoder_seq_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)
																					snake_case_           =		decoder_seq_length + 3
																					snake_case_           =		(
																					    batch,
																					    num_decoder_attention_heads,
																					    decoder_past_length,
																					    self._config.hidden_size // num_decoder_attention_heads,
																					)

																					snake_case_           =		torch.cat(
																					    [common_inputs["""decoder_attention_mask"""], torch.ones(UpperCAmelCase_		,       UpperCAmelCase_       )]		,       dim=1       )

																					snake_case_           =		[]
																					# If the number of encoder and decoder layers are present in the model configuration, both are considered
																					snake_case_       ,						snake_case_           =		self.num_layers
																					snake_case_           =		min(UpperCAmelCase_		,       UpperCAmelCase_       )
																					snake_case_           =		max(UpperCAmelCase_		,       UpperCAmelCase_       ) - min_num_layers
																					snake_case_           =		"""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.
																					snake_case_           =		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



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



														"""simple docstring"""



														snake_case_           =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       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
																					snake_case_       ,						snake_case_           =		common_inputs["""input_ids"""].shape
																					# Not using the same length for past_key_values
																					snake_case_           =		seqlen + 2
																					snake_case_       ,						snake_case_           =		self.num_layers
																					snake_case_       ,						snake_case_           =		self.num_attention_heads
																					snake_case_           =		(
																					    batch,
																					    num_encoder_attention_heads,
																					    past_key_values_length,
																					    self._config.hidden_size // num_encoder_attention_heads,
																					)

																					snake_case_           =		common_inputs["""attention_mask"""].dtype
																					snake_case_           =		torch.cat(
																					    [common_inputs["""attention_mask"""], torch.ones(UpperCAmelCase_		,       UpperCAmelCase_		,       dtype=UpperCAmelCase_       )]		,       dim=1       )
																					snake_case_           =		[
																					    (torch.zeros(UpperCAmelCase_       ), torch.zeros(UpperCAmelCase_       )) for _ in range(UpperCAmelCase_       )
																					]
														return common_inputs



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



														"""simple docstring"""



														snake_case_           =		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
														snake_case_           =		tokenizer.num_special_tokens_to_add(UpperCAmelCase_       )
														snake_case_           =		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
														snake_case_           =		[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
														snake_case_           =		dict(tokenizer(UpperCAmelCase_		,       return_tensors=UpperCAmelCase_       )       )
														return common_inputs



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



														"""simple docstring"""



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

														elif self.task == "causal-lm":
																					snake_case_           =		self._generate_dummy_inputs_for_causal_lm(
																					    UpperCAmelCase_		,       batch_size=UpperCAmelCase_		,       seq_length=UpperCAmelCase_		,       is_pair=UpperCAmelCase_		,       framework=UpperCAmelCase_       )
														else:
																					snake_case_           =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																					    UpperCAmelCase_		,       batch_size=UpperCAmelCase_		,       seq_length=UpperCAmelCase_		,       is_pair=UpperCAmelCase_		,       framework=UpperCAmelCase_       )

														return common_inputs



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



														"""simple docstring"""



														if self.task in ["default", "seq2seq-lm"]:
																					snake_case_           =		super()._flatten_past_key_values_(UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )
														else:
																					snake_case_           =		super(UpperCAmelCase_		,       self       )._flatten_past_key_values_(
																					    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_       )







"""simple docstring"""
from functools import reduce

__SCREAMING_SNAKE_CASE							:    Tuple		=    (
    '73167176531330624919225119674426574742355349194934'
    '96983520312774506326239578318016984801869478851843'
    '85861560789112949495459501737958331952853208805511'
    '12540698747158523863050715693290963295227443043557'
    '66896648950445244523161731856403098711121722383113'
    '62229893423380308135336276614282806444486645238749'
    '30358907296290491560440772390713810515859307960866'
    '70172427121883998797908792274921901699720888093776'
    '65727333001053367881220235421809751254540594752243'
    '52584907711670556013604839586446706324415722155397'
    '53697817977846174064955149290862569321978468622482'
    '83972241375657056057490261407972968652414535100474'
    '82166370484403199890008895243450658541227588666881'
    '16427171479924442928230863465674813919123162824586'
    '17866458359124566529476545682848912883142607690042'
    '24219022671055626321111109370544217506941658960408'
    '07198403850962455444362981230987879927244284909188'
    '84580156166097919133875499200524063689912560717606'
    '05886116467109405077541002256983155200055935729725'
    '71636269561882670428252483600823257530420752963450'
)







def 						_a				(      _SCREAMING_SNAKE_CASE = N				)    ->				int:
							return max(
							    # mypy cannot properly interpret reduce
							    int(reduce(lambda _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				: str(int(_SCREAMING_SNAKE_CASE				) * int(_SCREAMING_SNAKE_CASE				)				)				, n[i : i + 13]				)				)
							    for i in range(len(_SCREAMING_SNAKE_CASE				) - 12				)				)


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



