
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
  import torch

class __snake_case						( SCREAMING_SNAKE_CASE_	):
    _a						:		Dict=							"char"
    _a						:		Optional[Any]=							"bpe"
    _a						:		Any=							"wp"






lowercase    :   Tuple		       =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)

class __snake_case						( SCREAMING_SNAKE_CASE_	):
    _a						:		Any=							["image_processor", "char_tokenizer"]
    _a						:		List[Any]=							"ViTImageProcessor"
    _a						:		Any=							"MgpstrTokenizer"
    def __init__(    self						,snake_case=None						,snake_case=None						,**snake_case ):



           '''simple docstring'''







           lowercase				:	int			      = None
           if "feature_extractor" in kwargs:
                  warnings.warn(
                      """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                      """ instead."""						,_a						,)
                  lowercase				:	List[str]			      = kwargs.pop("""feature_extractor""" )

           lowercase				:	Union[str, Any]			      = image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
                  raise ValueError("""You need to specify an `image_processor`.""" )
           if tokenizer is None:
                  raise ValueError("""You need to specify a `tokenizer`.""" )

           lowercase				:	Optional[int]			      = tokenizer
           lowercase				:	List[str]			      = AutoTokenizer.from_pretrained("""gpt2""" )
           lowercase				:	Tuple			      = AutoTokenizer.from_pretrained("""bert-base-uncased""" )

           super().__init__(_a						,_a )
    def __call__(    self						,snake_case=None						,snake_case=None						,snake_case=None						,**snake_case ):



           '''simple docstring'''







           if images is None and text is None:
                  raise ValueError("""You need to specify either an `images` or `text` input to process.""" )

           if images is not None:
                  lowercase				:	int			      = self.image_processor(_a						,return_tensors=_a						,**_a )
           if text is not None:
                  lowercase				:	Union[str, Any]			      = self.char_tokenizer(_a						,return_tensors=_a						,**_a )

           if text is None:
                  return inputs
           elif images is None:
                  return encodings
           else:
                  lowercase				:	List[str]			      = encodings['input_ids']
                  return inputs
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Union[str, Any]			      = sequences
           lowercase				:	List[Any]			      = char_preds.size(0 )

           lowercase				:	Tuple			      = self._decode_helper(_a						,"""char""" )
           lowercase				:	str			      = self._decode_helper(_a						,"""bpe""" )
           lowercase				:	Dict			      = self._decode_helper(_a						,"""wp""" )

           lowercase				:	Dict			      = []
           lowercase				:	str			      = []
           for i in range(_a ):
                  lowercase				:	str			      = [char_scores[i], bpe_scores[i], wp_scores[i]]
                  lowercase				:	Tuple			      = [char_strs[i], bpe_strs[i], wp_strs[i]]
                  lowercase				:	Dict			      = scores.index(max(_a ) )
                  final_strs.append(strs[max_score_index] )
                  final_scores.append(scores[max_score_index] )

           lowercase				:	str			      = {}
           lowercase				:	Tuple			      = final_strs
           lowercase				:	Dict			      = final_scores
           lowercase				:	Optional[Any]			      = char_strs
           lowercase				:	Any			      = bpe_strs
           lowercase				:	Union[str, Any]			      = wp_strs
           return out
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case						,snake_case ):



           '''simple docstring'''







           if format == DecodeType.CHARACTER:
                  lowercase				:	Union[str, Any]			      = self.char_decode
                  lowercase				:	Any			      = 1
                  lowercase				:	Tuple			      = '[s]'
           elif format == DecodeType.BPE:
                  lowercase				:	Optional[int]			      = self.bpe_decode
                  lowercase				:	Optional[int]			      = 2
                  lowercase				:	Tuple			      = '#'
           elif format == DecodeType.WORDPIECE:
                  lowercase				:	Optional[int]			      = self.wp_decode
                  lowercase				:	Union[str, Any]			      = 102
                  lowercase				:	List[Any]			      = '[SEP]'
           else:
                  raise ValueError(f"Format {format} is not supported." )

           lowercase				:	Tuple			      = [], []
           lowercase				:	str			      = pred_logits.size(0 )
           lowercase				:	int			      = pred_logits.size(1 )
           lowercase				:	List[Any]			      = pred_logits.topk(1						,dim=-1						,largest=_a						,sorted=_a )
           lowercase				:	int			      = preds_index.view(-1						,_a )[:, 1:]
           lowercase				:	List[Any]			      = decoder(_a )
           lowercase				:	List[Any]			      = torch.nn.functional.softmax(_a						,dim=2 ).max(dim=2 )
           lowercase				:	List[str]			      = preds_max_prob[:, 1:]

           for index in range(_a ):
                  lowercase				:	Optional[int]			      = preds_str[index].find(_a )
                  lowercase				:	Union[str, Any]			      = preds_str[index][:pred_eos]
                  lowercase				:	Any			      = preds_index[index].cpu().tolist()
                  lowercase				:	Optional[Any]			      = pred_index.index(_a ) if eos_token in pred_index else -1
                  lowercase				:	Any			      = preds_max_prob[index][: pred_eos_index + 1]
                  lowercase				:	str			      = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
                  dec_strs.append(_a )
                  conf_scores.append(_a )

           return dec_strs, conf_scores
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Dict			      = [seq.replace(""" """						,"""""" ) for seq in self.char_tokenizer.batch_decode(_a )]
           return decode_strs
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           return self.bpe_tokenizer.batch_decode(_a )
    def  _SCREAMING_SNAKE_CASE			(    self						,snake_case ):



           '''simple docstring'''







           lowercase				:	Tuple			      = [seq.replace(""" """						,"""""" ) for seq in self.wp_tokenizer.batch_decode(_a )]
           return decode_strs





'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""pegasus"""
  a_				   =["""past_key_values"""]
  a_				   ={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




  def __init__(					self :      Any     ,      _a :      int=5_0265     ,      _a :      Tuple=1024     ,      _a :      Optional[Any]=12     ,      _a :      int=4096     ,      _a :      str=16     ,      _a :      Union[str, Any]=12     ,      _a :      Optional[Any]=4096     ,      _a :      Optional[int]=16     ,      _a :      Union[str, Any]=0.0     ,      _a :      str=0.0     ,      _a :      List[Any]=True     ,      _a :      Optional[int]=True     ,      _a :      Tuple="gelu"     ,      _a :      Dict=1024     ,      _a :      int=0.1     ,      _a :      Any=0.0     ,      _a :      Union[str, Any]=0.0     ,      _a :      Any=0.02     ,      _a :      Optional[Any]=0     ,      _a :      str=False     ,      _a :      Tuple=0     ,      _a :      Optional[int]=1     ,      _a :      Union[str, Any]=1     ,      **_a :      Optional[int]     ,      )   ->   Any:
    __lowerCamelCase			:			Union[str, Any]       			=					vocab_size
    __lowerCamelCase			:			int       			=					max_position_embeddings
    __lowerCamelCase			:			Union[str, Any]       			=					d_model
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_ffn_dim
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_layers
    __lowerCamelCase			:			List[Any]       			=					encoder_attention_heads
    __lowerCamelCase			:			Optional[int]       			=					decoder_ffn_dim
    __lowerCamelCase			:			int       			=					decoder_layers
    __lowerCamelCase			:			Tuple       			=					decoder_attention_heads
    __lowerCamelCase			:			Optional[Any]       			=					dropout
    __lowerCamelCase			:			Tuple       			=					attention_dropout
    __lowerCamelCase			:			int       			=					activation_dropout
    __lowerCamelCase			:			Dict       			=					activation_function
    __lowerCamelCase			:			List[Any]       			=					init_std
    __lowerCamelCase			:			List[str]       			=					encoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					decoder_layerdrop
    __lowerCamelCase			:			Union[str, Any]       			=					use_cache
    __lowerCamelCase			:			Tuple       			=					encoder_layers
    __lowerCamelCase			:			int       			=					scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=_a     ,      eos_token_id=_a     ,      is_encoder_decoder=_a     ,      decoder_start_token_id=_a     ,      forced_eos_token_id=_a     ,      **_a     ,      )




  @property
  def 		_lowercase	(					self :      Optional[int]  )   ->   int:
    return self.encoder_attention_heads




  @property
  def 		_lowercase	(					self :      Any  )   ->   int:
    return self.d_model






