





import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class      lowerCAmelCase						(					__lowerCAmelCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""image_processor""", """tokenizer"""]
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""AutoImageProcessor"""
     _SCREAMING_SNAKE_CASE     :     Dict		="""AutoTokenizer"""



     def __init__(					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      **lowerCAmelCase__       ):
            _A=	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				,      )
                   _A=	kwargs.pop('feature_extractor'       )

            _A=	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       )
            _A=	self.image_processor
            _A=	False



     def __call__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):


            if self._in_target_context_manager:
                   return self.current_processor(*_UpperCamelCase				,      **_UpperCamelCase       )

            _A=	kwargs.pop('images'				,      _UpperCamelCase       )
            _A=	kwargs.pop('text'				,      _UpperCamelCase       )
            if len(_UpperCamelCase       ) > 0:
                   _A=	args[0]
                   _A=	args[1:]

            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:
                   _A=	self.image_processor(_UpperCamelCase				,      *_UpperCamelCase				,      **_UpperCamelCase       )
            if text is not None:
                   _A=	self.tokenizer(_UpperCamelCase				,      **_UpperCamelCase       )

            if text is None:
                   return inputs
            elif images is None:
                   return encodings
            else:
                   _A=	encodings["""input_ids"""]
                   return inputs



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.tokenizer.batch_decode(*_UpperCamelCase				,      **_UpperCamelCase       )



     def 			a__  (					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            return self.tokenizer.decode(*_UpperCamelCase				,      **_UpperCamelCase       )



     @contextmanager
     def 			a__  (					self       ):
            warnings.warn(
                '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
                'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
                'your images inputs, or in a separate call.'       )
            _A=	True
            _A=	self.tokenizer
            yield
            _A=	self.image_processor
            _A=	False



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=False				,      lowerCAmelCase__=None       ):


            if added_vocab is None:
                   _A=	self.tokenizer.get_added_vocab()

            _A=	{}

            while tokens:
                   _A=	re.search(r'<s_(.*?)>'				,      _UpperCamelCase				,      re.IGNORECASE       )
                   if start_token is None:
                          break
                   _A=	start_token.group(1       )
                   _A=	re.search(rf"</s_{key}>"				,      _UpperCamelCase				,      re.IGNORECASE       )
                   _A=	start_token.group()
                   if end_token is None:
                          _A=	tokens.replace(_UpperCamelCase				,      ''       )
                   else:
                          _A=	end_token.group()
                          _A=	re.escape(_UpperCamelCase       )
                          _A=	re.escape(_UpperCamelCase       )
                          _A=	re.search(f"{start_token_escaped}(.*?){end_token_escaped}"				,      _UpperCamelCase				,      re.IGNORECASE       )
                          if content is not None:
                                 _A=	content.group(1       ).strip()
                                 if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                        _A=	self.tokenajson(_UpperCamelCase				,      is_inner_value=_UpperCamelCase				,      added_vocab=_UpperCamelCase       )
                                        if value:
                                               if len(_UpperCamelCase       ) == 1:
                                                      _A=	value[0]
                                               _A=	value
                                 else:  # leaf nodes
                                        _A=	[]
                                        for leaf in content.split(r'<sep/>'       ):
                                               _A=	leaf.strip()
                                               if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                                      _A=	leaf[1:-2]  # for categorical special tokens
                                               output[key].append(_UpperCamelCase       )
                                        if len(output[key]       ) == 1:
                                               _A=	output[key][0]

                          _A=	tokens[tokens.find(_UpperCamelCase       ) + len(_UpperCamelCase       ) :].strip()
                          if tokens[:6] == r"<sep/>":  # non-leaf nodes
                                 return [output] + self.tokenajson(tokens[6:]				,      is_inner_value=_UpperCamelCase				,      added_vocab=_UpperCamelCase       )

            if len(_UpperCamelCase       ):
                   return [output] if is_inner_value else output
            else:
                   return [] if is_inner_value else {"text_sequence": tokens}



     @property
     def 			a__  (					self       ):
            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,      _UpperCamelCase				,      )
            return self.image_processor_class





     @property
     def 			a__  (					self       ):
            warnings.warn(
                '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,      _UpperCamelCase				,      )
            return self.image_processor
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


UpperCAmelCase_								=							{
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_								=							[
          '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ErnieForCausalLM''',
          '''ErnieForMaskedLM''',
          '''ErnieForMultipleChoice''',
          '''ErnieForNextSentencePrediction''',
          '''ErnieForPreTraining''',
          '''ErnieForQuestionAnswering''',
          '''ErnieForSequenceClassification''',
          '''ErnieForTokenClassification''',
          '''ErnieModel''',
          '''ErniePreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_ernie import (
                ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
                ErnieForCausalLM,
                ErnieForMaskedLM,
                ErnieForMultipleChoice,
                ErnieForNextSentencePrediction,
                ErnieForPreTraining,
                ErnieForQuestionAnswering,
                ErnieForSequenceClassification,
                ErnieForTokenClassification,
                ErnieModel,
                ErniePreTrainedModel,
            )

else:
      import sys

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