




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        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 lowercase_				    =    ["image_processor", "tokenizer"]
 lowercase_				    =    "Pix2StructImageProcessor"
 lowercase_				    =    ("T5Tokenizer", "T5TokenizerFast")



 def __init__(							self  :		int				,				_lowerCAmelCase  :		List[Any]				,				_lowerCAmelCase  :		Any						):
       SCREAMING_SNAKE_CASE_							=			False
       super().__init__(_lowerCAmelCase				,				_lowerCAmelCase						)



 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  :		Tuple				,				):
       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:
             SCREAMING_SNAKE_CASE_							=			self.tokenizer
             SCREAMING_SNAKE_CASE_							=			self.tokenizer(
                 text=_lowerCAmelCase				,				add_special_tokens=_lowerCAmelCase				,				padding=_lowerCAmelCase				,				truncation=_lowerCAmelCase				,				max_length=_lowerCAmelCase				,				stride=_lowerCAmelCase				,				pad_to_multiple_of=_lowerCAmelCase				,				return_attention_mask=_lowerCAmelCase				,				return_overflowing_tokens=_lowerCAmelCase				,				return_special_tokens_mask=_lowerCAmelCase				,				return_offsets_mapping=_lowerCAmelCase				,				return_token_type_ids=_lowerCAmelCase				,				return_length=_lowerCAmelCase				,				verbose=_lowerCAmelCase				,				return_tensors=_lowerCAmelCase				,				**_lowerCAmelCase				,				)
             return text_encoding

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

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

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

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

       return encoding_image_processor



 def       lowerCAmelCase_  (							self  :		int				,				*_lowerCAmelCase  :		Any				,				**_lowerCAmelCase  :		Union[str, Any]						):
       return self.tokenizer.batch_decode(*_lowerCAmelCase				,				**_lowerCAmelCase						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				*_lowerCAmelCase  :		Any				,				**_lowerCAmelCase  :		List[str]						):
       return self.tokenizer.decode(*_lowerCAmelCase				,				**_lowerCAmelCase						)




 @property
 def       lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_							=			self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE_							=			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_torch_available, is_vision_available


lowerCamelCase__ :   str					=							{
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   int					=							['Mask2FormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__ :   Dict					=							[
       'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Mask2FormerForUniversalSegmentation',
       'Mask2FormerModel',
       'Mask2FormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_maskaformer import MaskaFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskaformer import (
          MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskaFormerForUniversalSegmentation,
          MaskaFormerModel,
          MaskaFormerPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__ :   Tuple					=							_LazyModule(__name__, globals()['__file__'], _import_structure)