






'''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


__lowerCAmelCase							=logging.get_logger(__name__)

__lowerCAmelCase							={
    "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    _snake_case   (				snake_case					):

					"""simple docstring"""





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




					def __init__(     self   , UpperCAmelCase__=8008   , UpperCAmelCase__=128   , UpperCAmelCase__=2   , UpperCAmelCase__=1_0240   , UpperCAmelCase__=32   , UpperCAmelCase__=24   , UpperCAmelCase__=1_0240   , UpperCAmelCase__=32   , UpperCAmelCase__=0.0   , UpperCAmelCase__=0.0   , UpperCAmelCase__=True   , UpperCAmelCase__=True   , UpperCAmelCase__="gelu"   , UpperCAmelCase__=2560   , 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__   , )      ->	Dict:
										a_									=   vocab_size
										a_									=   max_position_embeddings
										a_									=   d_model
										a_									=   encoder_ffn_dim
										a_									=   encoder_layers
										a_									=   encoder_attention_heads
										a_									=   decoder_ffn_dim
										a_									=   decoder_layers
										a_									=   decoder_attention_heads
										a_									=   dropout
										a_									=   attention_dropout
										a_									=   activation_dropout
										a_									=   activation_function
										a_									=   init_std
										a_									=   encoder_layerdrop
										a_									=   decoder_layerdrop
										a_									=   use_cache
										a_									=   encoder_layers
										a_									=   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    _snake_case   (				snake_case					):

					"""simple docstring"""





					@property
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Mapping[str, Mapping[int, str]]:
										if self.task in ["default", "seq2seq-lm"]:
															a_									=   OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															    ]     )
															if self.use_past:
																				a_									=   {0: 'batch'}
																				a_									=   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
															else:
																				a_									=   {0: 'batch', 1: 'decoder_sequence'}
																				a_									=   {0: 'batch', 1: 'decoder_sequence'}
															if self.use_past:
																				self.fill_with_past_key_values_(UpperCAmelCase__   , direction='inputs'     )
										elif self.task == "causal-lm":
															a_									=   OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															    ]     )
															if self.use_past:
																				a_							,					a_									=   self.num_layers
																				for i in range(UpperCAmelCase__     ):
																									a_									=   {0: 'batch', 2: 'past_sequence + sequence'}
																									a_									=   {0: 'batch', 2: 'past_sequence + sequence'}
										else:
															a_									=   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  __SCREAMING_SNAKE_CASE (     self     )      ->	Mapping[str, Mapping[int, str]]:
										if self.task in ["default", "seq2seq-lm"]:
															a_									=   super().outputs
										else:
															a_									=   super(UpperCAmelCase__   , self     ).outputs
															if self.use_past:
																				a_							,					a_									=   self.num_layers
																				for i in range(UpperCAmelCase__     ):
																									a_									=   {0: 'batch', 2: 'past_sequence + sequence'}
																									a_									=   {0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__ = -1   , UpperCAmelCase__ = -1   , UpperCAmelCase__ = False   , UpperCAmelCase__ = None   , )      ->	Mapping[str, Any]:
										a_									=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )
										# Generate decoder inputs
										a_									=   seq_length if not self.use_past else 1
										a_									=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )
										a_									=   {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										a_									=   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
															a_							,					a_									=   common_inputs['input_ids'].shape
															a_									=   common_inputs['decoder_input_ids'].shape[1]
															a_							,					a_									=   self.num_attention_heads
															a_									=   (
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															a_									=   decoder_seq_length
															a_									=   (
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)
															a_									=   torch.cat(
															    [common_inputs['decoder_attention_mask'], torch.ones(UpperCAmelCase__   , UpperCAmelCase__     )]   , dim=1     )
															a_									=   []
															a_							,					a_									=   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  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__ = -1   , UpperCAmelCase__ = -1   , UpperCAmelCase__ = False   , UpperCAmelCase__ = None   , )      ->	Mapping[str, Any]:
										a_									=   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
															a_							,					a_									=   common_inputs['input_ids'].shape
															a_									=   seqlen
															a_							,					a_									=   self.num_layers
															a_							,					a_									=   self.num_attention_heads
															a_									=   (
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															a_									=   common_inputs['attention_mask'].dtype
															a_									=   torch.cat(
															    [common_inputs['attention_mask'], torch.ones(UpperCAmelCase__   , UpperCAmelCase__   , dtype=UpperCAmelCase__     )]   , dim=1     )
															a_									=   [
															    (torch.zeros(UpperCAmelCase__     ), torch.zeros(UpperCAmelCase__     )) for _ in range(UpperCAmelCase__     )
															]
										return common_inputs




					def  __SCREAMING_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
										a_									=   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
										a_									=   tokenizer.num_special_tokens_to_add(UpperCAmelCase__     )
										a_									=   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
										a_									=   [' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
										a_									=   dict(tokenizer(UpperCAmelCase__   , return_tensors=UpperCAmelCase__     )     )
										return common_inputs




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__ = -1   , UpperCAmelCase__ = -1   , UpperCAmelCase__ = False   , UpperCAmelCase__ = None   , )      ->	Mapping[str, Any]:
										if self.task in ["default", "seq2seq-lm"]:
															a_									=   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":
															a_									=   self._generate_dummy_inputs_for_causal_lm(
															    UpperCAmelCase__   , batch_size=UpperCAmelCase__   , seq_length=UpperCAmelCase__   , is_pair=UpperCAmelCase__   , framework=UpperCAmelCase__     )
										else:
															a_									=   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  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	int:
										if self.task in ["default", "seq2seq-lm"]:
															a_									=   super()._flatten_past_key_values_(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )
										else:
															a_									=   super(UpperCAmelCase__   , self     )._flatten_past_key_values_(
															    UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__   , UpperCAmelCase__     )      ->	Union[str, Any]:
										if direction not in ["inputs", "outputs"]:
															raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''     )

										a_									=   'past_key_values' if direction == 'inputs' else 'present'
										a_							,					a_									=   self.num_layers

										a_									=   'past_encoder_sequence'
										a_									=   'past_decoder_sequence' if direction == 'inputs' else 'past_decoder_sequence + sequence'

										for i in range(UpperCAmelCase__     ):
															a_									=   {0: 'batch', 2: decoder_sequence}
															a_									=   {0: 'batch', 2: decoder_sequence}
															a_									=   {0: 'batch', 2: encoder_sequence}
															a_									=   {0: 'batch', 2: encoder_sequence}









'''simple docstring'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()

