


"""simple docstring"""







import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase       :							List[str]	=       ["""image_processor""", """tokenizer"""]
    UpperCAmelCase       :							str	=       """AutoImageProcessor"""
    UpperCAmelCase       :							Optional[int]	=       """AutoTokenizer"""






    def __init__(					self				:    str				, __UpperCAmelCase				:    Any=None				, __UpperCAmelCase				:    str=None				, **__UpperCAmelCase				:    Dict):
      a						:		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				, )
        a						:		Tuple      							=			kwargs.pop("feature_extractor")

      a						:		Optional[int]      							=			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						:		Any      							=			self.image_processor
      a						:		Any      							=			False






    def __call__(					self				:    Optional[int]				, *__UpperCAmelCase				:    List[Any]				, **__UpperCAmelCase				:    int):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*__UpperCAmelCase				, **__UpperCAmelCase)

      a						:		List[Any]      							=			kwargs.pop("images"				, __UpperCAmelCase)
      a						:		Dict      							=			kwargs.pop("text"				, __UpperCAmelCase)
      if len(__UpperCAmelCase) > 0:
        a						:		Tuple      							=			args[0]
        a						:		List[str]      							=			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						:		List[Any]      							=			self.image_processor(__UpperCAmelCase				, *__UpperCAmelCase				, **__UpperCAmelCase)
      if text is not None:
        a						:		List[str]      							=			self.tokenizer(__UpperCAmelCase				, **__UpperCAmelCase)

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






    def 	__snake_case							(					self				:    Optional[int]				, *__UpperCAmelCase				:    List[str]				, **__UpperCAmelCase				:    Optional[int]):
      return self.tokenizer.batch_decode(*__UpperCAmelCase				, **__UpperCAmelCase)






    def 	__snake_case							(					self				:    str				, *__UpperCAmelCase				:    Optional[int]				, **__UpperCAmelCase				:    Dict):
      return self.tokenizer.decode(*__UpperCAmelCase				, **__UpperCAmelCase)






    @contextmanager
    def 	__snake_case							(					self				:    List[str]):
      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						:		Dict      							=			True
      a						:		Any      							=			self.tokenizer
      yield
      a						:		Optional[Any]      							=			self.image_processor
      a						:		List[Any]      							=			False






    def 	__snake_case							(					self				:    int				, __UpperCAmelCase				:    List[str]				, __UpperCAmelCase				:    Union[str, Any]=False				, __UpperCAmelCase				:    str=None):
      if added_vocab is None:
        a						:		Optional[int]      							=			self.tokenizer.get_added_vocab()

      a						:		List[Any]      							=			{}

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

          a						:		List[Any]      							=			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 	__snake_case							(					self				:    str):
      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 	__snake_case							(					self				:    int):
      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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
 from ..ta.tokenization_ta import TaTokenizer
else:
 from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCamelCase											=       TaTokenizer

if is_tokenizers_available():
 from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
 from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCamelCase											=       TaTokenizerFast

lowerCamelCase											=       {'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       [
     'MT5EncoderModel',
     'MT5ForConditionalGeneration',
     'MT5ForQuestionAnswering',
     'MT5Model',
     'MT5PreTrainedModel',
     'MT5Stack',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase											=       ['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
 from .configuration_mta import MTaConfig, MTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mta import (
      MTaEncoderModel,
      MTaForConditionalGeneration,
      MTaForQuestionAnswering,
      MTaModel,
      MTaPreTrainedModel,
      MTaStack,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
 import sys

 lowerCamelCase											=       _LazyModule(
     __name__,
     globals()['__file__'],
     _import_structure,
     extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
     module_spec=__spec__,
 )


