

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}



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,)
