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 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			List[Any]												=     'blenderbot'
     lowerCamelCase__				:			Dict												=     ['past_key_values']
     lowerCamelCase__				:			List[Any]												=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
  
  
  
     def __init__(       self,					lowercase_=8008,					lowercase_=128,					lowercase_=2,					lowercase_=10240,					lowercase_=32,					lowercase_=24,					lowercase_=10240,					lowercase_=32,					lowercase_=0.0,					lowercase_=0.0,					lowercase_=True,					lowercase_=True,					lowercase_="gelu",					lowercase_=2560,					lowercase_=0.1,					lowercase_=0.0,					lowercase_=0.0,					lowercase_=0.02,					lowercase_=1,					lowercase_=False,					lowercase_=0,					lowercase_=1,					lowercase_=2,					lowercase_=3,					lowercase_=2,					**lowercase_,					)							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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=lowercase_,					bos_token_id=lowercase_,					eos_token_id=lowercase_,					is_encoder_decoder=lowercase_,					decoder_start_token_id=lowercase_,					encoder_no_repeat_ngram_size=lowercase_,					forced_eos_token_id=lowercase_,					**lowercase_,					)
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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_(lowercase_,					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(lowercase_ ):
             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 				_UpperCAmelCase				(       self )							->					Mapping[str, Mapping[int, str]]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.task in ["default", "seq2seq-lm"]:
         a__						=super().outputs
       else:
         a__						=super(lowercase_,					self ).outputs
         if self.use_past:
           a__,   a__						=self.num_layers
           for i in range(lowercase_ ):
             a__						={0: '''batch''', 2: '''past_sequence + sequence'''}
             a__						={0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = -1,					lowercase_ = -1,					lowercase_ = False,					lowercase_ = None,					)							->					Mapping[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )
       # Generate decoder inputs
       a__						=seq_length if not self.use_past else 1
       a__						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )
       a__						={F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       a__						=dict(**lowercase_,					**lowercase_ )
  
       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(lowercase_,					lowercase_ )],					dim=1 )
         a__						=[]
         a__,   a__						=self.num_layers
  
         for _ in range(lowercase_ ):
           common_inputs["past_key_values"].append(
               (
                   torch.zeros(lowercase_ ),
                   torch.zeros(lowercase_ ),
                   torch.zeros(lowercase_ ),
                   torch.zeros(lowercase_ ),
               ) )
       return common_inputs
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = -1,					lowercase_ = -1,					lowercase_ = False,					lowercase_ = None,					)							->					Mapping[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )
  
       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(lowercase_,					lowercase_,					dtype=lowercase_ )],					dim=1 )
         a__						=[
             (torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
         ]
       return common_inputs
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = -1,					lowercase_ = -1,					lowercase_ = False,					lowercase_ = None,					)							->					Mapping[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=compute_effective_axis_dimension(
           lowercase_,					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(lowercase_ )
       a__						=compute_effective_axis_dimension(
           lowercase_,					fixed_dimension=OnnxConfig.default_fixed_sequence,					num_token_to_add=lowercase_ )
  
       # Generate dummy inputs according to compute batch and sequence
       a__						=[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
       a__						=dict(tokenizer(lowercase_,					return_tensors=lowercase_ ) )
       return common_inputs
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = -1,					lowercase_ = -1,					lowercase_ = False,					lowercase_ = None,					)							->					Mapping[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.task in ["default", "seq2seq-lm"]:
         a__						=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             lowercase_,					batch_size=lowercase_,					seq_length=lowercase_,					is_pair=lowercase_,					framework=lowercase_ )
  
       elif self.task == "causal-lm":
         a__						=self._generate_dummy_inputs_for_causal_lm(
             lowercase_,					batch_size=lowercase_,					seq_length=lowercase_,					is_pair=lowercase_,					framework=lowercase_ )
       else:
         a__						=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             lowercase_,					batch_size=lowercase_,					seq_length=lowercase_,					is_pair=lowercase_,					framework=lowercase_ )
  
       return common_inputs
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.task in ["default", "seq2seq-lm"]:
         a__						=super()._flatten_past_key_values_(lowercase_,					lowercase_,					lowercase_,					lowercase_ )
       else:
         a__						=super(lowercase_,					self )._flatten_past_key_values_(
             lowercase_,					lowercase_,					lowercase_,					lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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(lowercase_ ):
         a__						={0: '''batch''', 2: decoder_sequence}
         a__						={0: '''batch''', 2: decoder_sequence}
         a__						={0: '''batch''', 2: encoder_sequence}
         a__						={0: '''batch''', 2: encoder_sequence}
  
style_context: 
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
         from .tokenization_xglm import XGLMTokenizer
  else:
         lowerCamelCase     =      None
  
  
  lowerCamelCase     =      logging.get_logger(__name__)
  
  lowerCamelCase     =      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  lowerCamelCase     =      {
      '''vocab_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
      },
      '''tokenizer_file''': {
          '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json''',
      },
  }
  
  lowerCamelCase     =      {
      '''facebook/xglm-564M''': 2048,
  }
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			int												=     VOCAB_FILES_NAMES
     lowerCamelCase__				:			List[str]												=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__				:			Dict												=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__				:			Any												=     ['input_ids', 'attention_mask']
     lowerCamelCase__				:			Any												=     XGLMTokenizer
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					lowercase_="<s>",					lowercase_="</s>",					lowercase_="</s>",					lowercase_="<s>",					lowercase_="<unk>",					lowercase_="<pad>",					**lowercase_,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=7
       a__						=[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
  
       a__						=kwargs.get('''additional_special_tokens''',					[] )
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_,					tokenizer_file=lowercase_,					bos_token=lowercase_,					eos_token=lowercase_,					sep_token=lowercase_,					cls_token=lowercase_,					unk_token=lowercase_,					pad_token=lowercase_,					**lowercase_,					)
  
       a__						=vocab_file
       a__						=False if not self.vocab_file else True
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if token_ids_a is None:
         return [self.sep_token_id] + token_ids_a
       a__						=[self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					List[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=[self.sep_token_id]
  
       if token_ids_a is None:
         return len(sep + token_ids_a ) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None )							->					Tuple[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.''' )
  
       if not os.path.isdir(lowercase_ ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory.""" )
         return
       a__						=os.path.join(
           lowercase_,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
       if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase_ ):
         copyfile(self.vocab_file,					lowercase_ )
  
       return (out_vocab_file,)
  