





'''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


a__					:			Optional[int]          =     logging.get_logger(__name__)

a__					:			int          =     {
    '''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 				__snake_case     (					__magic_name__							):
	__lowerCAmelCase       						=  '''blenderbot-small'''
	__lowerCAmelCase       						=  ['''past_key_values''']
	__lowerCAmelCase       						=  {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





	def __init__(		self      ,       UpperCamelCase_=5_0265      ,       UpperCamelCase_=512      ,       UpperCamelCase_=8      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=16      ,       UpperCamelCase_=8      ,       UpperCamelCase_=2048      ,       UpperCamelCase_=16      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=512      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=1      ,       UpperCamelCase_=False      ,       UpperCamelCase_=0      ,       UpperCamelCase_=1      ,       UpperCamelCase_=2      ,       UpperCamelCase_=2      ,       **UpperCamelCase_      ,       )      ->		List[Any]:
		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 				__snake_case     (					__magic_name__							):





	@property
	def   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		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   _snake_case   (		self		)      ->		Mapping[str, Mapping[int, str]]:
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = None      ,       )      ->		Mapping[str, Any]:
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = None      ,       )      ->		Mapping[str, Any]:
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = None      ,       )      ->		Mapping[str, Any]:
		# Copied from OnnxConfig.generate_dummy_inputs
		# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = -1      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = None      ,       )      ->		Mapping[str, Any]:
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		List[str]:
		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 math import loga




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if a < 0:
		raise ValueError('Input value must be a positive integer'							)
	elif isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		raise TypeError('Input value must be a \'int\' type'							)
	return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



