import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 						snake_case_ (	__A ):
   __A			:	Optional[int]        =       ["image_processor", "tokenizer"]
   __A			:	Any        =       "AutoImageProcessor"
   __A			:	str        =       "AutoTokenizer"






   def __init__(   self       :  Union[str, Any]			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  Tuple						)      ->		Tuple:
       lowercase__				:     str					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     int					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     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__(lowercase_			,		lowercase_						)
       lowercase__				:     Union[str, Any]					      =       self.image_processor
       lowercase__				:     Dict					      =       False






   def __call__(   self       :  List[str]			,		*lowercase_       :  List[Any]			,		**lowercase_       :  Dict						)      ->		Dict:
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*lowercase_			,		**lowercase_						)

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

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






   def    __UpperCamelCase  (   self       :  str			,		*lowercase_       :  List[Any]			,		**lowercase_       :  List[Any]						)      ->		Optional[Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Union[str, Any]			,		**lowercase_       :  int						)      ->		Optional[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






   @contextmanager
   def    __UpperCamelCase  (   self       :  Tuple						)      ->		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."						)
       lowercase__				:     List[Any]					      =       True
       lowercase__				:     int					      =       self.tokenizer
       yield
       lowercase__				:     Union[str, Any]					      =       self.image_processor
       lowercase__				:     str					      =       False






   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		Dict:
       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,		lowercase_			,		)
       return self.image_processor_class







   @property
   def    __UpperCamelCase  (   self       :  Tuple						)      ->		Dict:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor




import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 						snake_case_ (	unittest.TestCase						,__A ):






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		List[Any]:
       lowercase__				:     List[Any]					      =       load_tool("document-question-answering"						)
       self.tool.setup()
       lowercase__				:     Optional[int]					      =       load_tool("document-question-answering"			,		remote=lowercase_						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[int]:
       lowercase__				:     List[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[Any]					      =       dataset[0]["image"]

       lowercase__				:     int					      =       self.tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     str					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     str					      =       dataset[0]["image"]

       lowercase__				:     List[str]					      =       self.remote_tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Dict:
       lowercase__				:     List[str]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[str]					      =       dataset[0]["image"]

       self.tool(document=lowercase_			,		question="When is the coffee break?"						)







   def    __UpperCamelCase  (   self       :  str						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     int					      =       dataset[0]["image"]

       lowercase__				:     Tuple					      =       self.remote_tool(document=lowercase_			,		question="When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)



