


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


_a									=				logging.get_logger(__name__)

_a									=				{
    "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  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						"""blenderbot"""
			lowerCAmelCase_												=						["""past_key_values"""]
			lowerCAmelCase_												=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





			def __init__(   self	,  __lowerCAmelCase=8_0_0_8	,  __lowerCAmelCase=1_2_8	,  __lowerCAmelCase=2	,  __lowerCAmelCase=1_0_2_4_0	,  __lowerCAmelCase=3_2	,  __lowerCAmelCase=2_4	,  __lowerCAmelCase=1_0_2_4_0	,  __lowerCAmelCase=3_2	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=True	,  __lowerCAmelCase=True	,  __lowerCAmelCase="gelu"	,  __lowerCAmelCase=2_5_6_0	,  __lowerCAmelCase=0.1	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.0	,  __lowerCAmelCase=0.02	,  __lowerCAmelCase=1	,  __lowerCAmelCase=False	,  __lowerCAmelCase=0	,  __lowerCAmelCase=1	,  __lowerCAmelCase=2	,  __lowerCAmelCase=3	,  __lowerCAmelCase=2	,  **__lowerCAmelCase	,  ):



				'''simple docstring'''







				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=__lowerCAmelCase	,  bos_token_id=__lowerCAmelCase	,  eos_token_id=__lowerCAmelCase	,  is_encoder_decoder=__lowerCAmelCase	,  decoder_start_token_id=__lowerCAmelCase	,  encoder_no_repeat_ngram_size=__lowerCAmelCase	,  forced_eos_token_id=__lowerCAmelCase	,  **__lowerCAmelCase	,  )






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				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_(__lowerCAmelCase	,  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(__lowerCAmelCase							):
							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							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				# Generate decoder inputs
				lowerCamelCase__        =		seq_length if not self.use_past else 1
				lowerCamelCase__        =		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				lowerCamelCase__        =		{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
				lowerCamelCase__        =		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
					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(__lowerCAmelCase	,  __lowerCAmelCase							)]	,  dim=1							)
					lowerCamelCase__        =		[]
					lowerCamelCase__    ,     lowerCamelCase__        =		self.num_layers

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		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
					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(__lowerCAmelCase	,  __lowerCAmelCase	,  dtype=__lowerCAmelCase							)]	,  dim=1							)
					lowerCamelCase__        =		[
					    (torch.zeros(__lowerCAmelCase							), torch.zeros(__lowerCAmelCase							)) for _ in range(__lowerCAmelCase							)
					]
				return common_inputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		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
				lowerCamelCase__        =		tokenizer.num_special_tokens_to_add(__lowerCAmelCase							)
				lowerCamelCase__        =		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
				lowerCamelCase__        =		[''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
				lowerCamelCase__        =		dict(tokenizer(__lowerCAmelCase	,  return_tensors=__lowerCAmelCase							)							)
				return common_inputs





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = -1	,  __lowerCAmelCase = False	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					lowerCamelCase__        =		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":
					lowerCamelCase__        =		self._generate_dummy_inputs_for_causal_lm(
					    __lowerCAmelCase	,  batch_size=__lowerCAmelCase	,  seq_length=__lowerCAmelCase	,  is_pair=__lowerCAmelCase	,  framework=__lowerCAmelCase							)
				else:
					lowerCamelCase__        =		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 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				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(__lowerCAmelCase							):
					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 lowerCAmelCase__(__snake_case							)    ->				int:







	'''simple docstring'''







	lowerCamelCase__        =		str(__snake_case							).strip()
	if not bin_string:
		raise ValueError('''Empty string was passed to the function'''							)
	lowerCamelCase__        =		bin_string[0] == '''-'''
	if is_negative:
		lowerCamelCase__        =		bin_string[1:]
	if not all(char in '''01''' for char in bin_string							):
		raise ValueError('''Non-binary value was passed to the function'''							)
	lowerCamelCase__        =		0
	for char in bin_string:
		lowerCamelCase__        =		2 * decimal_number + int(__snake_case							)
	return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
			from doctest import testmod

			testmod()



