



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor



class 							lowerCAmelCase_						(    unittest.TestCase				):
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_=7,     SCREAMING_SNAKE_CASE_=3,     SCREAMING_SNAKE_CASE_=18,     SCREAMING_SNAKE_CASE_=30,     SCREAMING_SNAKE_CASE_=400,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=None,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=False,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=True,     SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],     SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],     )	->						Dict:
          UpperCamelCase       :    Optional[Any]		      =      parent
          UpperCamelCase       :    List[str]		      =      batch_size
          UpperCamelCase       :    Dict		      =      num_channels
          UpperCamelCase       :    int		      =      image_size
          UpperCamelCase       :    int		      =      min_resolution
          UpperCamelCase       :    Optional[Any]		      =      max_resolution
          UpperCamelCase       :    Union[str, Any]		      =      do_resize
          UpperCamelCase       :    Tuple		      =      size if size is not None else {'height': 18, 'width': 20}
          UpperCamelCase       :    List[str]		      =      do_thumbnail
          UpperCamelCase       :    List[str]		      =      do_align_axis
          UpperCamelCase       :    Union[str, Any]		      =      do_pad
          UpperCamelCase       :    Any		      =      do_normalize
          UpperCamelCase       :    str		      =      image_mean
          UpperCamelCase       :    str		      =      image_std






       def    snake_case_			(     self			)	->						Any:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_thumbnail": self.do_thumbnail,
              "do_align_long_axis": self.do_align_axis,
              "do_pad": self.do_pad,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
          }






@require_torch
@require_vision
class 							lowerCAmelCase_						(    UpperCamelCase__			,       unittest.TestCase				):
       UpperCAmelCase__							:   Optional[Any]  	=    DonutImageProcessor if is_vision_available() else None
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Optional[int]		      =      DonutImageProcessingTester(self			)
       @property
       def    snake_case_			(     self			)	->						Union[str, Any]:
          return self.image_processor_tester.prepare_image_processor_dict()
       def    snake_case_			(     self			)	->						Dict:
          UpperCamelCase       :    List[str]		      =      self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(lowercase_,     'do_resize'			)			)
          self.assertTrue(hasattr(lowercase_,     'size'			)			)
          self.assertTrue(hasattr(lowercase_,     'do_thumbnail'			)			)
          self.assertTrue(hasattr(lowercase_,     'do_align_long_axis'			)			)
          self.assertTrue(hasattr(lowercase_,     'do_pad'			)			)
          self.assertTrue(hasattr(lowercase_,     'do_normalize'			)			)
          self.assertTrue(hasattr(lowercase_,     'image_mean'			)			)
          self.assertTrue(hasattr(lowercase_,     'image_std'			)			)
       def    snake_case_			(     self			)	->						Union[str, Any]:
          UpperCamelCase       :    Tuple		      =      self.image_processing_class.from_dict(self.image_processor_dict			)
          self.assertEqual(image_processor.size,     {'height': 18, 'width': 20}			)

          UpperCamelCase       :    str		      =      self.image_processing_class.from_dict(self.image_processor_dict,     size=42			)
          self.assertEqual(image_processor.size,     {'height': 42, 'width': 42}			)

          # Previous config had dimensions in (width, height) order
          UpperCamelCase       :    Union[str, Any]		      =      self.image_processing_class.from_dict(self.image_processor_dict,     size=(42, 84)			)
          self.assertEqual(image_processor.size,     {'height': 84, 'width': 42}			)
       def    snake_case_			(     self			)	->						Dict:
          pass
       @is_flaky()
       def    snake_case_			(     self			)	->						Tuple:
          # Initialize image_processing
          UpperCamelCase       :    Union[str, Any]		      =      self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          UpperCamelCase       :    Optional[Any]		      =      prepare_image_inputs(self.image_processor_tester,     equal_resolution=lowercase_			)
          for image in image_inputs:
             self.assertIsInstance(lowercase_,     Image.Image			)

          # Test not batched input
          UpperCamelCase       :    Optional[int]		      =      image_processing(image_inputs[0],     return_tensors='pt'			).pixel_values
          self.assertEqual(
              encoded_images.shape,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              ),     )

          # Test batched
          UpperCamelCase       :    str		      =      image_processing(lowercase_,     return_tensors='pt'			).pixel_values
          self.assertEqual(
              encoded_images.shape,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              ),     )
       @is_flaky()
       def    snake_case_			(     self			)	->						Dict:
          # Initialize image_processing
          UpperCamelCase       :    Tuple		      =      self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          UpperCamelCase       :    int		      =      prepare_image_inputs(self.image_processor_tester,     equal_resolution=lowercase_,     numpify=lowercase_			)
          for image in image_inputs:
             self.assertIsInstance(lowercase_,     np.ndarray			)

          # Test not batched input
          UpperCamelCase       :    Any		      =      image_processing(image_inputs[0],     return_tensors='pt'			).pixel_values
          self.assertEqual(
              encoded_images.shape,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              ),     )

          # Test batched
          UpperCamelCase       :    int		      =      image_processing(lowercase_,     return_tensors='pt'			).pixel_values
          self.assertEqual(
              encoded_images.shape,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              ),     )






       @is_flaky()
       def    snake_case_			(     self			)	->						List[str]:
          # Initialize image_processing
          UpperCamelCase       :    Union[str, Any]		      =      self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          UpperCamelCase       :    Optional[int]		      =      prepare_image_inputs(self.image_processor_tester,     equal_resolution=lowercase_,     torchify=lowercase_			)
          for image in image_inputs:
             self.assertIsInstance(lowercase_,     torch.Tensor			)

          # Test not batched input
          UpperCamelCase       :    Optional[Any]		      =      image_processing(image_inputs[0],     return_tensors='pt'			).pixel_values
          self.assertEqual(
              encoded_images.shape,     (
                  1,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              ),     )

          # Test batched
          UpperCamelCase       :    str		      =      image_processing(lowercase_,     return_tensors='pt'			).pixel_values
          self.assertEqual(
              encoded_images.shape,     (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.size['height'],
                  self.image_processor_tester.size['width'],
              ),     )










'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							["""image_processor""", """tokenizer"""]
 __lowercase  						=							"""CLIPImageProcessor"""
 __lowercase  						=							("""CLIPTokenizer""", """CLIPTokenizerFast""")
 def __init__(							self :List[Any]		,			lowercase_ :List[str]=None		,			lowercase_ :Any=None		,			**lowercase_ :Union[str, Any]				)->				Optional[Any]:
     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."		,			lowercase_		,			)
         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__(lowercase_		,			lowercase_				)
 def __call__(							self :Optional[Any]		,			lowercase_ :Optional[int]=None		,			lowercase_ :Dict=None		,			lowercase_ :List[Any]=None		,			**lowercase_ :Optional[Any]				)->				Union[str, Any]:

     if text is None and images is None:
         raise ValueError("You have to specify either text or images. Both cannot be none."				)

     if text is not None:
         A__							=     self.tokenizer(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if images is not None:
         A__							=     self.image_processor(lowercase_		,			return_tensors=lowercase_		,			**lowercase_				)

     if text is not None and images is not None:
         A__							=     image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_				)		,			tensor_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :int		,			*lowercase_ :Tuple		,			**lowercase_ :Any				)->				Dict:
     return self.tokenizer.batch_decode(*lowercase_		,			**lowercase_				)
 def 	UpperCAmelCase_				(							self :Any		,			*lowercase_ :Any		,			**lowercase_ :Any				)->				List[str]:
     return self.tokenizer.decode(*lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Optional[int]:
     A__							=     self.tokenizer.model_input_names
     A__							=     self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
 @property
 def 	UpperCAmelCase_				(							self :List[Any]				)->				str:
     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 :List[str]				)->				List[Any]:
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,			lowercase_		,			)
     return self.image_processor



