code: 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 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   """blenderbot"""
  					__UpperCamelCase						=   ["""past_key_values"""]
  					__UpperCamelCase						=   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  
  
  
  					def __init__( self :					Optional[Any]      ,_lowerCAmelCase :					str=8_008      ,_lowerCAmelCase :					Union[str, Any]=128      ,_lowerCAmelCase :					Union[str, Any]=2      ,_lowerCAmelCase :					Optional[Any]=10_240      ,_lowerCAmelCase :					List[str]=32      ,_lowerCAmelCase :					Optional[int]=24      ,_lowerCAmelCase :					Optional[int]=10_240      ,_lowerCAmelCase :					List[str]=32      ,_lowerCAmelCase :					Any=0.0      ,_lowerCAmelCase :					Union[str, Any]=0.0      ,_lowerCAmelCase :					Optional[int]=True      ,_lowerCAmelCase :					List[str]=True      ,_lowerCAmelCase :					Optional[int]="gelu"      ,_lowerCAmelCase :					Union[str, Any]=2_560      ,_lowerCAmelCase :					Union[str, Any]=0.1      ,_lowerCAmelCase :					Tuple=0.0      ,_lowerCAmelCase :					int=0.0      ,_lowerCAmelCase :					Optional[int]=0.0_2      ,_lowerCAmelCase :					Optional[int]=1      ,_lowerCAmelCase :					str=False      ,_lowerCAmelCase :					str=0      ,_lowerCAmelCase :					str=1      ,_lowerCAmelCase :					List[Any]=2      ,_lowerCAmelCase :					str=3      ,_lowerCAmelCase :					Dict=2      ,**_lowerCAmelCase :					Any      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__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=_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 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					@property
  					def 							UpperCamelCase_						( self :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							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_(_lowerCAmelCase      ,direction="inputs"						)
  							elif self.task == "causal-lm":
  									__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(_lowerCAmelCase						):
  													__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
  					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  					def 							UpperCamelCase_						( self :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if self.task in ["default", "seq2seq-lm"]:
  									__snake_case		       =     super().outputs
  							else:
  									__snake_case		       =     super(_lowerCAmelCase      ,self						).outputs
  									if self.use_past:
  											__snake_case       ,       __snake_case		       =     self.num_layers
  											for i in range(_lowerCAmelCase						):
  													__snake_case		       =     {0: "batch", 2: "past_sequence + sequence"}
  													__snake_case		       =     {0: "batch", 2: "past_sequence + sequence"}
  							return common_outputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					PreTrainedTokenizer      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					bool = False      ,_lowerCAmelCase :					Optional[TensorType] = None      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
  							    _lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							# 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(
  							    _lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							__snake_case		       =     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
  							__snake_case		       =     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
  									__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
  									__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(_lowerCAmelCase      ,_lowerCAmelCase						)]      ,dim=1						)
  									__snake_case		       =     []
  									__snake_case       ,       __snake_case		       =     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 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					PreTrainedTokenizer      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					bool = False      ,_lowerCAmelCase :					Optional[TensorType] = None      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     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
  									__snake_case       ,       __snake_case		       =     common_inputs["input_ids"].shape
  									__snake_case		       =     seqlen
  									__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(_lowerCAmelCase      ,_lowerCAmelCase      ,dtype=_lowerCAmelCase						)]      ,dim=1						)
  									__snake_case		       =     [
  									    (torch.zeros(_lowerCAmelCase						), torch.zeros(_lowerCAmelCase						)) for _ in range(_lowerCAmelCase						)
  									]
  							return common_inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					PreTrainedTokenizer      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					bool = False      ,_lowerCAmelCase :					Optional[TensorType] = None      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     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
  							__snake_case		       =     tokenizer.num_special_tokens_to_add(_lowerCAmelCase						)
  							__snake_case		       =     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
  							__snake_case		       =     [" ".join([tokenizer.unk_token]						) * seq_length] * batch_size
  							__snake_case		       =     dict(tokenizer(_lowerCAmelCase      ,return_tensors=_lowerCAmelCase						)						)
  							return common_inputs
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					PreTrainedTokenizer      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					int = -1      ,_lowerCAmelCase :					bool = False      ,_lowerCAmelCase :					Optional[TensorType] = None      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							if self.task in ["default", "seq2seq-lm"]:
  									__snake_case		       =     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":
  									__snake_case		       =     self._generate_dummy_inputs_for_causal_lm(
  									    _lowerCAmelCase      ,batch_size=_lowerCAmelCase      ,seq_length=_lowerCAmelCase      ,is_pair=_lowerCAmelCase      ,framework=_lowerCAmelCase						)
  							else:
  									__snake_case		       =     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 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if self.task in ["default", "seq2seq-lm"]:
  									__snake_case		       =     super()._flatten_past_key_values_(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  							else:
  									__snake_case		       =     super(_lowerCAmelCase      ,self						)._flatten_past_key_values_(
  									    _lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						)
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					Mapping[str, Mapping[int, str]]      ,_lowerCAmelCase :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if direction not in ["inputs", "outputs"]:
  									raise ValueError(F"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""						)
  
  							__snake_case		       =     "past_key_values" if direction == "inputs" else "present"
  							__snake_case       ,       __snake_case		       =     self.num_layers
  
  							__snake_case		       =     "past_encoder_sequence"
  							__snake_case		       =     "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"
  
  							for i in range(_lowerCAmelCase						):
  									__snake_case		       =     {0: "batch", 2: decoder_sequence}
  									__snake_case		       =     {0: "batch", 2: decoder_sequence}
  									__snake_case		       =     {0: "batch", 2: encoder_sequence}
  									__snake_case		       =     {0: "batch", 2: encoder_sequence}
  
  
  
  
  
  
style_context: import os
  from string import ascii_letters
  
  lowerCamelCase__           =				ascii_letters + ' \t\n'
  
  
  
  def 							_lowerCamelCase(				)  ->   dict[str, None]:
  		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
  		__snake_case		       =     {}
  		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
  				for word in dictionary_file.read().split("\n"	):
  						__snake_case		       =     None
  		return english_words
  
  
  lowerCamelCase__           =				load_dictionary()
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   float:
  		__snake_case		       =     message.upper()
  		__snake_case		       =     remove_non_letters(__snake_case	)
  		__snake_case		       =     message.split()
  		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
  		return float(__snake_case	) / len(__snake_case	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
  		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
  		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
  		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
  		__snake_case		       =     message_letters_percentage >= letter_percentage
  		return words_match and letters_match
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  