


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	:							List[Any]           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE	:							str           =  {
    "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}


class        __lowerCamelCase      (				_SCREAMING_SNAKE_CASE ):


				"""simple docstring"""







				a_:		Optional[Any]					      =	"blenderbot"
				a_:		Optional[Any]					      =	["past_key_values"]
				a_:		Union[str, Any]					      =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






				def __init__(				self     :    Dict	,							lowerCamelCase_     :    Union[str, Any]=8008	,							lowerCamelCase_     :    str=128	,							lowerCamelCase_     :    str=2	,							lowerCamelCase_     :    Optional[int]=1_0240	,							lowerCamelCase_     :    int=32	,							lowerCamelCase_     :    List[Any]=24	,							lowerCamelCase_     :    str=1_0240	,							lowerCamelCase_     :    Optional[Any]=32	,							lowerCamelCase_     :    Any=0.0	,							lowerCamelCase_     :    Any=0.0	,							lowerCamelCase_     :    Optional[Any]=True	,							lowerCamelCase_     :    Dict=True	,							lowerCamelCase_     :    Union[str, Any]="gelu"	,							lowerCamelCase_     :    Tuple=2560	,							lowerCamelCase_     :    List[Any]=0.1	,							lowerCamelCase_     :    Optional[Any]=0.0	,							lowerCamelCase_     :    str=0.0	,							lowerCamelCase_     :    Optional[int]=0.02	,							lowerCamelCase_     :    Any=1	,							lowerCamelCase_     :    Any=False	,							lowerCamelCase_     :    str=0	,							lowerCamelCase_     :    Tuple=1	,							lowerCamelCase_     :    Optional[int]=2	,							lowerCamelCase_     :    Optional[int]=3	,							lowerCamelCase_     :    Any=2	,							**lowerCamelCase_     :    List[Any]	,							):
								_lowerCAmelCase  						=vocab_size
								_lowerCAmelCase  						=max_position_embeddings
								_lowerCAmelCase  						=d_model
								_lowerCAmelCase  						=encoder_ffn_dim
								_lowerCAmelCase  						=encoder_layers
								_lowerCAmelCase  						=encoder_attention_heads
								_lowerCAmelCase  						=decoder_ffn_dim
								_lowerCAmelCase  						=decoder_layers
								_lowerCAmelCase  						=decoder_attention_heads
								_lowerCAmelCase  						=dropout
								_lowerCAmelCase  						=attention_dropout
								_lowerCAmelCase  						=activation_dropout
								_lowerCAmelCase  						=activation_function
								_lowerCAmelCase  						=init_std
								_lowerCAmelCase  						=encoder_layerdrop
								_lowerCAmelCase  						=decoder_layerdrop
								_lowerCAmelCase  						=use_cache
								_lowerCAmelCase  						=encoder_layers
								_lowerCAmelCase  						=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_	,							encoder_no_repeat_ngram_size=A_	,							forced_eos_token_id=A_	,							**A_	,							)


class        __lowerCamelCase      (				_SCREAMING_SNAKE_CASE ):


				"""simple docstring"""







				@property
				def        lowerCAmelCase__			(				self     :    Union[str, Any]					):
								if self.task in ["default", "seq2seq-lm"]:
												_lowerCAmelCase  						=OrderedDict(
												    [
												        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
												        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
												    ]					)
												if self.use_past:
																_lowerCAmelCase  						={0: """batch"""}
																_lowerCAmelCase  						={0: """batch""", 1: """past_decoder_sequence + sequence"""}
												else:
																_lowerCAmelCase  						={0: """batch""", 1: """decoder_sequence"""}
																_lowerCAmelCase  						={0: """batch""", 1: """decoder_sequence"""}
												if self.use_past:
																self.fill_with_past_key_values_(A_	,							direction="""inputs"""					)
								elif self.task == "causal-lm":
												_lowerCAmelCase  						=OrderedDict(
												    [
												        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
												        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
												    ]					)
												if self.use_past:
																_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_layers
																for i in range(A_					):
																				_lowerCAmelCase  						={0: """batch""", 2: """past_sequence + sequence"""}
																				_lowerCAmelCase  						={0: """batch""", 2: """past_sequence + sequence"""}
								else:
												_lowerCAmelCase  						=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
				# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
				def        lowerCAmelCase__			(				self     :    List[Any]					):
								if self.task in ["default", "seq2seq-lm"]:
												_lowerCAmelCase  						=super().outputs
								else:
												_lowerCAmelCase  						=super(A_	,							self					).outputs
												if self.use_past:
																_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_layers
																for i in range(A_					):
																				_lowerCAmelCase  						={0: """batch""", 2: """past_sequence + sequence"""}
																				_lowerCAmelCase  						={0: """batch""", 2: """past_sequence + sequence"""}
								return common_outputs






				def        lowerCAmelCase__			(				self     :    Optional[Any]	,							lowerCamelCase_     :    Union[str, Any]	,							lowerCamelCase_     :    int = -1	,							lowerCamelCase_     :    Optional[Any] = -1	,							lowerCamelCase_     :    Optional[Any] = False	,							lowerCamelCase_     :    int = None	,							):
								_lowerCAmelCase  						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    A_	,							A_	,							A_	,							A_	,							A_					)
								# Generate decoder inputs
								_lowerCAmelCase  						=seq_length if not self.use_past else 1
								_lowerCAmelCase  						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    A_	,							A_	,							A_	,							A_	,							A_					)
								_lowerCAmelCase  						={F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
								_lowerCAmelCase  						=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						,							_lowerCAmelCase  						=common_inputs["""input_ids"""].shape
												_lowerCAmelCase  						=common_inputs["""decoder_input_ids"""].shape[1]
												_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_attention_heads
												_lowerCAmelCase  						=(
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												_lowerCAmelCase  						=decoder_seq_length
												_lowerCAmelCase  						=(
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)
												_lowerCAmelCase  						=torch.cat(
												    [common_inputs["""decoder_attention_mask"""], torch.ones(A_	,							A_					)]	,							dim=1					)
												_lowerCAmelCase  						=[]
												_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_layers

												for _ in range(A_					):
																common_inputs["past_key_values"].append(
																    (
																        torch.zeros(A_					),
																        torch.zeros(A_					),
																        torch.zeros(A_					),
																        torch.zeros(A_					),
																    )					)
								return common_inputs






				def        lowerCAmelCase__			(				self     :    Optional[int]	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    Tuple = -1	,							lowerCamelCase_     :    Union[str, Any] = -1	,							lowerCamelCase_     :    Any = False	,							lowerCamelCase_     :    str = None	,							):
								_lowerCAmelCase  						=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						,							_lowerCAmelCase  						=common_inputs["""input_ids"""].shape
												_lowerCAmelCase  						=seqlen
												_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_layers
												_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_attention_heads
												_lowerCAmelCase  						=(
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												_lowerCAmelCase  						=common_inputs["""attention_mask"""].dtype
												_lowerCAmelCase  						=torch.cat(
												    [common_inputs["""attention_mask"""], torch.ones(A_	,							A_	,							dtype=A_					)]	,							dim=1					)
												_lowerCAmelCase  						=[
												    (torch.zeros(A_					), torch.zeros(A_					)) for _ in range(A_					)
												]
								return common_inputs






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    List[str] = -1	,							lowerCamelCase_     :    Tuple = -1	,							lowerCamelCase_     :    List[str] = False	,							lowerCamelCase_     :    List[Any] = None	,							):
								_lowerCAmelCase  						=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  						=tokenizer.num_special_tokens_to_add(A_					)
								_lowerCAmelCase  						=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  						=[""" """.join([tokenizer.unk_token]					) * seq_length] * batch_size
								_lowerCAmelCase  						=dict(tokenizer(A_	,							return_tensors=A_					)					)
								return common_inputs






				def        lowerCAmelCase__			(				self     :    List[Any]	,							lowerCamelCase_     :    str	,							lowerCamelCase_     :    Optional[Any] = -1	,							lowerCamelCase_     :    List[Any] = -1	,							lowerCamelCase_     :    Tuple = False	,							lowerCamelCase_     :    Optional[int] = None	,							):
								if self.task in ["default", "seq2seq-lm"]:
												_lowerCAmelCase  						=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  						=self._generate_dummy_inputs_for_causal_lm(
												    A_	,							batch_size=A_	,							seq_length=A_	,							is_pair=A_	,							framework=A_					)
								else:
												_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    Tuple	,							lowerCamelCase_     :    Any	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Optional[int]					):
								if self.task in ["default", "seq2seq-lm"]:
												_lowerCAmelCase  						=super()._flatten_past_key_values_(A_	,							A_	,							A_	,							A_					)
								else:
												_lowerCAmelCase  						=super(A_	,							self					)._flatten_past_key_values_(
												    A_	,							A_	,							A_	,							A_					)






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    str					):
								if direction not in ["inputs", "outputs"]:
												raise ValueError(F"direction must either be \"inputs\" or \"outputs\", but {direction} was given"					)

								_lowerCAmelCase  						="""past_key_values""" if direction == """inputs""" else """present"""
								_lowerCAmelCase						,							_lowerCAmelCase  						=self.num_layers

								_lowerCAmelCase  						="""past_encoder_sequence"""
								_lowerCAmelCase  						="""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

								for i in range(A_					):
												_lowerCAmelCase  						={0: """batch""", 2: decoder_sequence}
												_lowerCAmelCase  						={0: """batch""", 2: decoder_sequence}
												_lowerCAmelCase  						={0: """batch""", 2: encoder_sequence}
												_lowerCAmelCase  						={0: """batch""", 2: encoder_sequence}



def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if not isinstance(lowercase       ,			lowercase					):
				UpperCamelCase            =	(
				    'operation can not be conducted on a object of type '
				    f'''{type(lowercase					).__name__}'''
				)
				raise TypeError(lowercase					)
		UpperCamelCase            =	''
		for _ in range(0       ,			32					):
				bit_string += str(number % 2					)
				UpperCamelCase            =	number >> 1
		return bit_string


def  A			(   lowercase					)  ->    str:
		'''simple docstring'''

		if number < 0:
				raise ValueError('the value of input must be positive'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('Input value must be a \'int\' type'					)
		elif isinstance(lowercase       ,			lowercase					):
				raise TypeError('\'<\' not supported between instances of \'str\' and \'int\''					)
		UpperCamelCase            =	0
		# iterator over [1 to 32],since we are dealing with 32 bit integer
		for _ in range(1       ,			33					):
				# left shift the bits by unity
				UpperCamelCase            =	result << 1
				# get the end bit
				UpperCamelCase            =	number % 2
				# right shift the bits by unity
				UpperCamelCase            =	number >> 1
				# add that bit to our ans
				UpperCamelCase            =	result | end_bit
		return get_reverse_bit_string(lowercase					)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
