






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase       						=			{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			['RemBertTokenizer']

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			['RemBertTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			[
       'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'RemBertForCausalLM',
       'RemBertForMaskedLM',
       'RemBertForMultipleChoice',
       'RemBertForQuestionAnswering',
       'RemBertForSequenceClassification',
       'RemBertForTokenClassification',
       'RemBertLayer',
       'RemBertModel',
       'RemBertPreTrainedModel',
       'load_tf_weights_in_rembert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase       						=			[
       'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFRemBertForCausalLM',
       'TFRemBertForMaskedLM',
       'TFRemBertForMultipleChoice',
       'TFRemBertForQuestionAnswering',
       'TFRemBertForSequenceClassification',
       'TFRemBertForTokenClassification',
       'TFRemBertLayer',
       'TFRemBertModel',
       'TFRemBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert import RemBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_rembert_fast import RemBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rembert import (
          REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RemBertForCausalLM,
          RemBertForMaskedLM,
          RemBertForMultipleChoice,
          RemBertForQuestionAnswering,
          RemBertForSequenceClassification,
          RemBertForTokenClassification,
          RemBertLayer,
          RemBertModel,
          RemBertPreTrainedModel,
          load_tf_weights_in_rembert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rembert import (
          TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRemBertForCausalLM,
          TFRemBertForMaskedLM,
          TFRemBertForMultipleChoice,
          TFRemBertForQuestionAnswering,
          TFRemBertForSequenceClassification,
          TFRemBertForTokenClassification,
          TFRemBertLayer,
          TFRemBertModel,
          TFRemBertPreTrainedModel,
      )


else:
   import sys

   _lowercase       						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''simple docstring'''







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 			_lowercase						(       __a					):
     _UpperCAmelCase										=					'''char'''
     _UpperCAmelCase										=					'''bpe'''
     _UpperCAmelCase										=					'''wp'''




_lowercase       						=			(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					['''image_processor''', '''char_tokenizer''']
     _UpperCAmelCase										=					'''ViTImageProcessor'''
     _UpperCAmelCase										=					'''MgpstrTokenizer'''







     def __init__( self    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[Any]:
      snake_case				=			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__    ,   )
       snake_case				=			kwargs.pop('''feature_extractor'''    )

      snake_case				=			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				=			tokenizer
      snake_case				=			AutoTokenizer.from_pretrained('''gpt2'''    )
      snake_case				=			AutoTokenizer.from_pretrained('''bert-base-uncased'''    )

      super().__init__(A__    ,   A__    )







     def __call__( self    ,   A__=None    ,   A__=None    ,   A__=None    ,   **A__    ) ->	List[str]:
      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				=			self.image_processor(A__    ,   return_tensors=A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.char_tokenizer(A__    ,   return_tensors=A__    ,   **A__    )

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







     def     UpperCamelCase			( self    ,   A__    ) ->	Dict:
      snake_case				,		snake_case				,		snake_case				=			sequences
      snake_case				=			char_preds.size(0    )

      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''char'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''bpe'''    )
      snake_case				,		snake_case				=			self._decode_helper(A__    ,   '''wp'''    )

      snake_case				=			[]
      snake_case				=			[]
      for i in range(A__    ):
       snake_case				=			[char_scores[i], bpe_scores[i], wp_scores[i]]
       snake_case				=			[char_strs[i], bpe_strs[i], wp_strs[i]]
       snake_case				=			scores.index(max(A__    )    )
       final_strs.append(strs[max_score_index]    )
       final_scores.append(scores[max_score_index]    )

      snake_case				=			{}
      snake_case				=			final_strs
      snake_case				=			final_scores
      snake_case				=			char_strs
      snake_case				=			bpe_strs
      snake_case				=			wp_strs
      return out







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	Optional[Any]:
      if format == DecodeType.CHARACTER:
       snake_case				=			self.char_decode
       snake_case				=			1
       snake_case				=			'''[s]'''
      elif format == DecodeType.BPE:
       snake_case				=			self.bpe_decode
       snake_case				=			2
       snake_case				=			'''#'''
      elif format == DecodeType.WORDPIECE:
       snake_case				=			self.wp_decode
       snake_case				=			1_02
       snake_case				=			'''[SEP]'''
      else:
       raise ValueError(F"""Format {format} is not supported."""    )

      snake_case				,		snake_case				=			[], []
      snake_case				=			pred_logits.size(0    )
      snake_case				=			pred_logits.size(1    )
      snake_case				,		snake_case				=			pred_logits.topk(1    ,   dim=-1    ,   largest=A__    ,   sorted=A__    )
      snake_case				=			preds_index.view(-1    ,   A__    )[:, 1:]
      snake_case				=			decoder(A__    )
      snake_case				,		snake_case				=			torch.nn.functional.softmax(A__    ,   dim=2    ).max(dim=2    )
      snake_case				=			preds_max_prob[:, 1:]

      for index in range(A__    ):
       snake_case				=			preds_str[index].find(A__    )
       snake_case				=			preds_str[index][:pred_eos]
       snake_case				=			preds_index[index].cpu().tolist()
       snake_case				=			pred_index.index(A__    ) if eos_token in pred_index else -1
       snake_case				=			preds_max_prob[index][: pred_eos_index + 1]
       snake_case				=			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     UpperCamelCase			( self    ,   A__    ) ->	int:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.char_tokenizer.batch_decode(A__    )]
      return decode_strs







     def     UpperCamelCase			( self    ,   A__    ) ->	List[str]:
      return self.bpe_tokenizer.batch_decode(A__    )







     def     UpperCamelCase			( self    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			[seq.replace(''' '''    ,   ''''''    ) for seq in self.wp_tokenizer.batch_decode(A__    )]
      return decode_strs

