
"""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_            =   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        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									="blenderbot"
		UpperCamelCase									=["past_key_values"]
		UpperCamelCase									={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
		def __init__(		self							,		UpperCamelCase_=80_08							,		UpperCamelCase_=1_28							,		UpperCamelCase_=2							,		UpperCamelCase_=1_02_40							,		UpperCamelCase_=32							,		UpperCamelCase_=24							,		UpperCamelCase_=1_02_40							,		UpperCamelCase_=32							,		UpperCamelCase_=0.0							,		UpperCamelCase_=0.0							,		UpperCamelCase_=True							,		UpperCamelCase_=True							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=25_60							,		UpperCamelCase_=0.1							,		UpperCamelCase_=0.0							,		UpperCamelCase_=0.0							,		UpperCamelCase_=0.0_2							,		UpperCamelCase_=1							,		UpperCamelCase_=False							,		UpperCamelCase_=0							,		UpperCamelCase_=1							,		UpperCamelCase_=2							,		UpperCamelCase_=3							,		UpperCamelCase_=2							,		**UpperCamelCase_							,		)		->   Tuple:
					__lowercase	:       Any				=   vocab_size
					__lowercase	:       int				=   max_position_embeddings
					__lowercase	:       Optional[int]				=   d_model
					__lowercase	:       Optional[int]				=   encoder_ffn_dim
					__lowercase	:       Dict				=   encoder_layers
					__lowercase	:       List[str]				=   encoder_attention_heads
					__lowercase	:       List[Any]				=   decoder_ffn_dim
					__lowercase	:       Any				=   decoder_layers
					__lowercase	:       List[str]				=   decoder_attention_heads
					__lowercase	:       int				=   dropout
					__lowercase	:       Tuple				=   attention_dropout
					__lowercase	:       Optional[int]				=   activation_dropout
					__lowercase	:       Any				=   activation_function
					__lowercase	:       Optional[int]				=   init_std
					__lowercase	:       str				=   encoder_layerdrop
					__lowercase	:       Union[str, Any]				=   decoder_layerdrop
					__lowercase	:       Tuple				=   use_cache
					__lowercase	:       str				=   encoder_layers
					__lowercase	:       Tuple				=   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_							,		encoder_no_repeat_ngram_size=UpperCamelCase_							,		forced_eos_token_id=UpperCamelCase_							,		**UpperCamelCase_							,		)


class        UpperCAmelCase_  (	snake_case						):
		@property
		def 					_lowerCamelCase    (		self   )		->   Mapping[str, Mapping[int, str]]:
					if self.task in ["default", "seq2seq-lm"]:
								__lowercase	:       Any				=   OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]   )
								if self.use_past:
											__lowercase	:       Any				=   {0: '''batch'''}
											__lowercase	:       Tuple				=   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
											__lowercase	:       Optional[int]				=   {0: '''batch''', 1: '''decoder_sequence'''}
											__lowercase	:       List[Any]				=   {0: '''batch''', 1: '''decoder_sequence'''}
								if self.use_past:
											self.fill_with_past_key_values_(UpperCamelCase_							,		direction='''inputs'''   )
					elif self.task == "causal-lm":
								__lowercase	:       List[Any]				=   OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]   )
								if self.use_past:
											__lowercase	:       Union[str, Any]				=   self.num_layers
											for i in range(UpperCamelCase_   ):
														__lowercase	:       int				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
														__lowercase	:       List[Any]				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
					else:
								__lowercase	:       List[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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def 					_lowerCamelCase    (		self   )		->   Mapping[str, Mapping[int, str]]:
					if self.task in ["default", "seq2seq-lm"]:
								__lowercase	:       str				=   super().outputs
					else:
								__lowercase	:       Any				=   super(UpperCamelCase_							,		self   ).outputs
								if self.use_past:
											__lowercase	:       Optional[Any]				=   self.num_layers
											for i in range(UpperCamelCase_   ):
														__lowercase	:       List[str]				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
														__lowercase	:       List[str]				=   {0: '''batch''', 2: '''past_sequence + sequence'''}
					return common_outputs
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = -1							,		UpperCamelCase_ = -1							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		)		->   Mapping[str, Any]:
					__lowercase	:       List[Any]				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					# Generate decoder inputs
					__lowercase	:       List[Any]				=   seq_length if not self.use_past else 1
					__lowercase	:       Tuple				=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					__lowercase	:       Optional[Any]				=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
					__lowercase	:       List[Any]				=   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
								__lowercase	:       Any				=   common_inputs['''input_ids'''].shape
								__lowercase	:       Tuple				=   common_inputs['''decoder_input_ids'''].shape[1]
								__lowercase	:       Optional[int]				=   self.num_attention_heads
								__lowercase	:       Dict				=   (
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								__lowercase	:       Dict				=   decoder_seq_length
								__lowercase	:       Tuple				=   (
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)
								__lowercase	:       str				=   torch.cat(
								    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase_							,		UpperCamelCase_   )]							,		dim=1   )
								__lowercase	:       str				=   []
								__lowercase	:       Optional[int]				=   self.num_layers

								for _ in range(UpperCamelCase_   ):
											common_inputs["past_key_values"].append(
											    (
											        torch.zeros(UpperCamelCase_   ),
											        torch.zeros(UpperCamelCase_   ),
											        torch.zeros(UpperCamelCase_   ),
											        torch.zeros(UpperCamelCase_   ),
											    )   )
					return common_inputs
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = -1							,		UpperCamelCase_ = -1							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		)		->   Mapping[str, Any]:
					__lowercase	:       int				=   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
								__lowercase	:       Union[str, Any]				=   common_inputs['''input_ids'''].shape
								__lowercase	:       Any				=   seqlen
								__lowercase	:       str				=   self.num_layers
								__lowercase	:       Tuple				=   self.num_attention_heads
								__lowercase	:       str				=   (
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								__lowercase	:       Optional[Any]				=   common_inputs['''attention_mask'''].dtype
								__lowercase	:       List[Any]				=   torch.cat(
								    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase_							,		UpperCamelCase_							,		dtype=UpperCamelCase_   )]							,		dim=1   )
								__lowercase	:       Any				=   [
								    (torch.zeros(UpperCamelCase_   ), torch.zeros(UpperCamelCase_   )) for _ in range(UpperCamelCase_   )
								]
					return common_inputs
		def 					_lowerCamelCase    (		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
					__lowercase	:       Optional[Any]				=   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
					__lowercase	:       int				=   tokenizer.num_special_tokens_to_add(UpperCamelCase_   )
					__lowercase	:       List[str]				=   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
					__lowercase	:       Dict				=   [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
					__lowercase	:       List[Any]				=   dict(tokenizer(UpperCamelCase_							,		return_tensors=UpperCamelCase_   )   )
					return common_inputs
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_ = -1							,		UpperCamelCase_ = -1							,		UpperCamelCase_ = False							,		UpperCamelCase_ = None							,		)		->   Mapping[str, Any]:
					if self.task in ["default", "seq2seq-lm"]:
								__lowercase	:       Any				=   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":
								__lowercase	:       List[str]				=   self._generate_dummy_inputs_for_causal_lm(
								    UpperCamelCase_							,		batch_size=UpperCamelCase_							,		seq_length=UpperCamelCase_							,		is_pair=UpperCamelCase_							,		framework=UpperCamelCase_   )
					else:
								__lowercase	:       List[Any]				=   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							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Union[str, Any]:
					if self.task in ["default", "seq2seq-lm"]:
								__lowercase	:       str				=   super()._flatten_past_key_values_(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					else:
								__lowercase	:       Optional[Any]				=   super(UpperCamelCase_							,		self   )._flatten_past_key_values_(
								    UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Tuple:
					if direction not in ["inputs", "outputs"]:
								raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""   )

					__lowercase	:       List[Any]				=   '''past_key_values''' if direction == '''inputs''' else '''present'''
					__lowercase	:       Optional[Any]				=   self.num_layers

					__lowercase	:       Any				=   '''past_encoder_sequence'''
					__lowercase	:       int				=   '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

					for i in range(UpperCamelCase_   ):
								__lowercase	:       List[Any]				=   {0: '''batch''', 2: decoder_sequence}
								__lowercase	:       List[Any]				=   {0: '''batch''', 2: decoder_sequence}
								__lowercase	:       List[str]				=   {0: '''batch''', 2: encoder_sequence}
								__lowercase	:       Dict				=   {0: '''batch''', 2: encoder_sequence}









"""simple docstring"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
			# Adjacency List of graph
			print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



