from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 			UpperCAmelCase__    (		__SCREAMING_SNAKE_CASE					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= ['image_processor', 'tokenizer']
  lowerCAmelCase__		:	Any    		= 'Pix2StructImageProcessor'
  lowerCAmelCase__		:	Tuple    		= ('T5Tokenizer', 'T5TokenizerFast')






  def __init__(				self:			Union[str, Any]				,				__lowerCAmelCase:			List[str]				,				__lowerCAmelCase:			int   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       False
    super().__init__(_a				,				_a   )






  def __call__(				self:			Tuple				,				__lowerCAmelCase:			Tuple=None				,				__lowerCAmelCase:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			Union[bool, str, PaddingStrategy] = False				,				__lowerCAmelCase:			Union[bool, str, TruncationStrategy] = None				,				__lowerCAmelCase:			Optional[int] = None				,				__lowerCAmelCase:			Optional[int] = 2_048				,				__lowerCAmelCase:			int = 0				,				__lowerCAmelCase:			Optional[int] = None				,				__lowerCAmelCase:			Optional[bool] = None				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = False				,				__lowerCAmelCase:			bool = True				,				__lowerCAmelCase:			Optional[Union[str, TensorType]] = None				,				**__lowerCAmelCase:			Dict				,				) -> List[Any]:




    '''simple docstring'''




    if images is None and text is None:
      raise ValueError("You have to specify either images or text."   )

    # Get only text
    if images is None and not self.image_processor.is_vqa:
      __UpperCAmelCase						    =       self.tokenizer
      __UpperCAmelCase						    =       self.tokenizer(
          text=_a				,				add_special_tokens=_a				,				padding=_a				,				truncation=_a				,				max_length=_a				,				stride=_a				,				pad_to_multiple_of=_a				,				return_attention_mask=_a				,				return_overflowing_tokens=_a				,				return_special_tokens_mask=_a				,				return_offsets_mapping=_a				,				return_token_type_ids=_a				,				return_length=_a				,				verbose=_a				,				return_tensors=_a				,				**_a				,				)
      return text_encoding

    if not self.image_processor.is_vqa:
      # add pixel_values
      __UpperCAmelCase						    =       self.image_processor(
          _a				,				return_tensors=_a				,				max_patches=_a				,				**_a   )
    else:
      # add pixel_values and bbox
      __UpperCAmelCase						    =       self.image_processor(
          _a				,				return_tensors=_a				,				max_patches=_a				,				header_text=_a				,				**_a   )

    if text is not None and not self.image_processor.is_vqa:
      __UpperCAmelCase						    =       self.tokenizer(
          text=_a				,				add_special_tokens=_a				,				padding=_a				,				truncation=_a				,				max_length=_a				,				stride=_a				,				pad_to_multiple_of=_a				,				return_attention_mask=_a				,				return_overflowing_tokens=_a				,				return_special_tokens_mask=_a				,				return_offsets_mapping=_a				,				return_token_type_ids=_a				,				return_length=_a				,				verbose=_a				,				return_tensors=_a				,				**_a				,				)

      if "attention_mask" in text_encoding:
        __UpperCAmelCase						    =       text_encoding.pop("attention_mask"   )
      if "input_ids" in text_encoding:
        __UpperCAmelCase						    =       text_encoding.pop("input_ids"   )
    else:
      __UpperCAmelCase						    =       None

    if text_encoding is not None:
      encoding_image_processor.update(_a   )

    return encoding_image_processor






  def 	_UpperCAmelCase				(				self:			Optional[int]				,				*__lowerCAmelCase:			List[str]				,				**__lowerCAmelCase:			str   ) -> List[str]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Tuple				,				*__lowerCAmelCase:			Optional[int]				,				**__lowerCAmelCase:			Union[str, Any]   ) -> List[Any]:




    '''simple docstring'''




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






  @property
  def 	_UpperCAmelCase				(				self:			str   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.tokenizer.model_input_names
    __UpperCAmelCase						    =       self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )




from typing import TYPE_CHECKING

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


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""SqueezeBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



