





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        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Any      					="""char"""
  a_							:       Union[str, Any]      					="""bpe"""
  a_							:       Optional[Any]      					="""wp"""







lowerCAmelCase_						   =    (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       List[str]      					=["""image_processor""", """char_tokenizer"""]
  a_							:       Tuple      					="""ViTImageProcessor"""
  a_							:       Optional[int]      					="""MgpstrTokenizer"""







  def __init__(      self      :					Optional[Any]      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					Tuple=None      ,				**UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'      ,				UpperCamelCase      ,				)
      _snake_case   :				List[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				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`.'							)

    _snake_case   :				Tuple														=	tokenizer
    _snake_case   :				Union[str, Any]														=	AutoTokenizer.from_pretrained('gpt2'							)
    _snake_case   :				List[str]														=	AutoTokenizer.from_pretrained('bert-base-uncased'							)

    super().__init__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					int      ,				UpperCamelCase      :					Any=None      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					Any=None      ,				**UpperCamelCase      :					Optional[Any]							):


    '''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:
      _snake_case   :				Union[str, Any]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)
    if text is not None:
      _snake_case   :				Optional[Any]														=	self.char_tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _snake_case   :				Dict														=	encodings['input_ids']
      return inputs







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Any														=	sequences
    _snake_case   :				int														=	char_preds.size(0							)

    _snake_case   :				Optional[Any]														=	self._decode_helper(UpperCamelCase      ,				'char'							)
    _snake_case   :				List[Any]														=	self._decode_helper(UpperCamelCase      ,				'bpe'							)
    _snake_case   :				str														=	self._decode_helper(UpperCamelCase      ,				'wp'							)

    _snake_case   :				List[Any]														=	[]
    _snake_case   :				Union[str, Any]														=	[]
    for i in range(UpperCamelCase							):
      _snake_case   :				Optional[Any]														=	[char_scores[i], bpe_scores[i], wp_scores[i]]
      _snake_case   :				List[Any]														=	[char_strs[i], bpe_strs[i], wp_strs[i]]
      _snake_case   :				List[str]														=	scores.index(max(UpperCamelCase							)							)
      final_strs.append(strs[max_score_index]							)
      final_scores.append(scores[max_score_index]							)

    _snake_case   :				Optional[Any]														=	{}
    _snake_case   :				Optional[int]														=	final_strs
    _snake_case   :				List[str]														=	final_scores
    _snake_case   :				Tuple														=	char_strs
    _snake_case   :				Tuple														=	bpe_strs
    _snake_case   :				List[Any]														=	wp_strs
    return out







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    if format == DecodeType.CHARACTER:
      _snake_case   :				Optional[Any]														=	self.char_decode
      _snake_case   :				Dict														=	1
      _snake_case   :				Optional[Any]														=	'[s]'
    elif format == DecodeType.BPE:
      _snake_case   :				Any														=	self.bpe_decode
      _snake_case   :				int														=	2
      _snake_case   :				Union[str, Any]														=	'#'
    elif format == DecodeType.WORDPIECE:
      _snake_case   :				Optional[Any]														=	self.wp_decode
      _snake_case   :				int														=	1_02
      _snake_case   :				str														=	'[SEP]'
    else:
      raise ValueError(f"""Format {format} is not supported."""							)

    _snake_case   :				int														=	[], []
    _snake_case   :				Optional[int]														=	pred_logits.size(0							)
    _snake_case   :				List[str]														=	pred_logits.size(1							)
    _snake_case   :				Dict														=	pred_logits.topk(1      ,				dim=-1      ,				largest=UpperCamelCase      ,				sorted=UpperCamelCase							)
    _snake_case   :				str														=	preds_index.view(-1      ,				UpperCamelCase							)[:, 1:]
    _snake_case   :				Union[str, Any]														=	decoder(UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	torch.nn.functional.softmax(UpperCamelCase      ,				dim=2							).max(dim=2							)
    _snake_case   :				Any														=	preds_max_prob[:, 1:]

    for index in range(UpperCamelCase							):
      _snake_case   :				List[str]														=	preds_str[index].find(UpperCamelCase							)
      _snake_case   :				Tuple														=	preds_str[index][:pred_eos]
      _snake_case   :				str														=	preds_index[index].cpu().tolist()
      _snake_case   :				int														=	pred_index.index(UpperCamelCase							) if eos_token in pred_index else -1
      _snake_case   :				Tuple														=	preds_max_prob[index][: pred_eos_index + 1]
      _snake_case   :				int														=	pred_max_prob.cumprod(dim=0							)[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(UpperCamelCase							)
      conf_scores.append(UpperCamelCase							)

    return dec_strs, conf_scores







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				Optional[Any]														=	[seq.replace(' '      ,				''							) for seq in self.char_tokenizer.batch_decode(UpperCamelCase							)]
    return decode_strs







  def UpperCamelCase_ (      self      :					Dict      ,				UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    return self.bpe_tokenizer.batch_decode(UpperCamelCase							)







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	[seq.replace(' '      ,				''							) for seq in self.wp_tokenizer.batch_decode(UpperCamelCase							)]
    return decode_strs











import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       Union[str, Any]      					=["""image_processor""", """tokenizer"""]
  a_							:       Optional[int]      					="""CLIPImageProcessor"""
  a_							:       Optional[Any]      					=("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")







  def __init__(      self      :					List[str]      ,				UpperCamelCase      :					Optional[int]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    _snake_case   :				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.'      ,				UpperCamelCase      ,				)
      _snake_case   :				Optional[Any]														=	kwargs.pop('feature_extractor'							)

    _snake_case   :				Dict														=	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`.'							)

    super().__init__(UpperCamelCase      ,				UpperCamelCase							)







  def __call__(      self      :					Dict      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					Optional[int]=None      ,				**UpperCamelCase      :					Dict							):


    '''simple docstring'''





    if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'							)

    if text is not None:
      _snake_case   :				Optional[int]														=	self.tokenizer(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if images is not None:
      _snake_case   :				Optional[int]														=	self.image_processor(UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase							)

    if text is not None and images is not None:
      _snake_case   :				Optional[int]														=	image_features.pixel_values
      return encoding
    elif text is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**UpperCamelCase							)      ,				tensor_type=UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Union[str, Any]							):


    '''simple docstring'''





    return self.tokenizer.batch_decode(*UpperCamelCase      ,				**UpperCamelCase							)







  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				*UpperCamelCase      :					Union[str, Any]      ,				**UpperCamelCase      :					Optional[Any]							):


    '''simple docstring'''





    return self.tokenizer.decode(*UpperCamelCase      ,				**UpperCamelCase							)







  @property
  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.tokenizer.model_input_names
    _snake_case   :				List[Any]														=	self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




