




'''simple docstring'''






import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


A_ 			=		{}
for task, _ in pipeline_test_mapping.items():
      A_ 			=		{"pt": None, "tf": None}


# DO **NOT** add item to this set (unless the reason is approved)
A_ 			=		{
    "tests/models/esm/test_modeling_esmfold.py",  # The pipeline test mapping is added to `test_modeling_esm.py`
}






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:

      if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
            return "pt"
      elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
            return "tf"
      elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
            return "flax"
      else:
            return None






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      # Use the cached results
      if PIPELINE_TEST_MAPPING[task].get(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		) is not None:
            return PIPELINE_TEST_MAPPING[task][framework]

      snake_case__					:      str  			=						pipeline_test_mapping[task]['test']
      snake_case__					:      int  			=						None

      if framework == "pt":
            snake_case__					:      Dict  			=						getattr(__SCREAMING_SNAKE_CASE							,			'model_mapping'							,			__SCREAMING_SNAKE_CASE		)
      elif framework == "tf":
            snake_case__					:      List[Any]  			=						getattr(__SCREAMING_SNAKE_CASE							,			'tf_model_mapping'							,			__SCREAMING_SNAKE_CASE		)

      if mapping is not None:
            snake_case__					:      List[str]  			=						dict(mapping.items()		)

      # cache the results
      snake_case__					:      Optional[int]  			=						mapping
      return mapping






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      str:
      snake_case__					:      Any  			=						get_framework(__SCREAMING_SNAKE_CASE		)
      if framework is None:
            return None
      snake_case__					:      str  			=						get_mapping_for_task(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
      if mapping is None:
            return None

      snake_case__					:      Union[str, Any]  			=						list({model_class.config_class for model_class in test_class.all_model_classes}		)
      if len(__SCREAMING_SNAKE_CASE		) != 1:
            raise ValueError('There should be exactly one configuration class from `test_class.all_model_classes`.'		)

      # This could be a list/tuple of model classes, but it's rare.
      snake_case__					:      Optional[int]  			=						mapping.get(config_classes[0]							,			__SCREAMING_SNAKE_CASE		)
      if isinstance(__SCREAMING_SNAKE_CASE							,			(tuple, list)		):
            snake_case__					:      str  			=						sorted(__SCREAMING_SNAKE_CASE							,			key=lambda __SCREAMING_SNAKE_CASE		: x.__name__		)

      return model_class






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      int:
      snake_case__					:      Tuple  			=						[(task, get_model_for_pipeline_test(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)) for task in pipeline_test_mapping]
      snake_case__					:      Tuple  			=						sorted([(task, model) for task, model in mapping if model is not None]							,			key=lambda __SCREAMING_SNAKE_CASE		: x[0]		)

      return dict(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      Union[str, Any]:
      snake_case__					:      Any  			=						get_framework(__SCREAMING_SNAKE_CASE		)
      if framework == "pt":
            snake_case__					:      Tuple  			=						'torch'
      snake_case__					:      Union[str, Any]  			=						'{}'

      snake_case__					:      int  			=						get_pipeline_model_mapping(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) == 0:
            return ""

      snake_case__					:      Optional[Any]  			=						[]
      for task, model_classes in mapping.items():
            if isinstance(__SCREAMING_SNAKE_CASE							,			(tuple, list)		):
                  # A list/tuple of model classes
                  snake_case__					:      Any  			=						'(' + ', '.join([x.__name__ for x in model_classes]		) + ')'
            else:
                  # A single model class
                  snake_case__					:      str  			=						model_classes.__name__
            texts.append(f"\"{task}\": {value}"		)
      snake_case__					:      Dict  			=						'{' + ', '.join(__SCREAMING_SNAKE_CASE		) + '}'
      snake_case__					:      Any  			=						f"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

      return text






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      int:
      snake_case__					:      Tuple  			=						{'ModelTesterMixin', 'TFModelTesterMixin', 'FlaxModelTesterMixin'}
      if not issubclass(__SCREAMING_SNAKE_CASE							,			unittest.TestCase		):
            return False
      return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]		)		) > 0






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__					:      List[Any]  			=						[x for x in get_test_classes(__SCREAMING_SNAKE_CASE		) if is_valid_test_class(__SCREAMING_SNAKE_CASE		)]

      snake_case__					:      int  			=						None
      for test_class in test_classes:
            # If a test class has defined `pipeline_model_mapping`, let's take it
            if getattr(__SCREAMING_SNAKE_CASE							,			'pipeline_model_mapping'							,			__SCREAMING_SNAKE_CASE		) is not None:
                  snake_case__					:      Any  			=						test_class
                  break
    # Take the test class with the shortest name (just a heuristic)
      if target_test_class is None and len(__SCREAMING_SNAKE_CASE		) > 0:
            snake_case__					:      Optional[int]  			=						sorted(__SCREAMING_SNAKE_CASE							,			key=lambda __SCREAMING_SNAKE_CASE		: (len(x.__name__		), x.__name__)		)[0]

      return target_test_class






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Any:
      snake_case__					:      int  			=						start_idx
      for idx, line in enumerate(lines[start_idx:]		):
            snake_case__					:      str  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
            if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
                  snake_case__					:      Tuple  			=						start_idx + idx
            elif idx > 0 and indent <= indent_level:
                  # Outside the definition block of `pipeline_model_mapping`
                  break

      return end_idx






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False		)						->      Optional[int]:
      if getattr(__SCREAMING_SNAKE_CASE							,			'pipeline_model_mapping'							,			__SCREAMING_SNAKE_CASE		) is not None:
            if not overwrite:
                  return "", -1

      snake_case__					:      Optional[Any]  			=						get_pipeline_model_mapping_string(__SCREAMING_SNAKE_CASE		)
      if len(__SCREAMING_SNAKE_CASE		) == 0:
            return "", -1
      snake_case__					:      str  			=						line_to_add + '\n'

      # The code defined the class `test_class`
      snake_case__			,       snake_case__					:      Tuple  			=						inspect.getsourcelines(__SCREAMING_SNAKE_CASE		)
      # `inspect` gives the code for an object, including decorator(s) if any.
      # We (only) need the exact line of the class definition.
      for idx, line in enumerate(__SCREAMING_SNAKE_CASE		):
            if line.lstrip().startswith('class '		):
                  snake_case__					:      int  			=						class_lines[idx:]
                  class_start_line_no += idx
                  break
      snake_case__					:      List[Any]  			=						class_start_line_no + len(__SCREAMING_SNAKE_CASE		) - 1

      # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
      # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
      # block that appears in a `test_class`.
      snake_case__					:      Optional[int]  			=						None
      # The indent level of the line at `class_lines[start_idx]` (if defined)
      snake_case__					:      Tuple  			=						0
      # To record if `pipeline_model_mapping` is found in `test_class`.
      snake_case__					:      str  			=						None
      for idx, line in enumerate(__SCREAMING_SNAKE_CASE		):
            if line.strip().startswith('all_model_classes = '		):
                  snake_case__					:      str  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
                  snake_case__					:      int  			=						idx
            elif line.strip().startswith('all_generative_model_classes = '		):
                  snake_case__					:      Union[str, Any]  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
                  snake_case__					:      Dict  			=						idx
            elif line.strip().startswith('pipeline_model_mapping = '		):
                  snake_case__					:      Dict  			=						len(__SCREAMING_SNAKE_CASE		) - len(line.lstrip()		)
                  snake_case__					:      int  			=						idx
                  snake_case__					:      List[str]  			=						line
                  break

      if start_idx is None:
            return "", -1
      # Find the ending index (inclusive) of the above found block.
      snake_case__					:      Union[str, Any]  			=						find_block_ending(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

      # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
      # `is_timm_available()` instead of `is_torch_available()`.
      # Keep leading and trailing whitespaces
      snake_case__					:      Union[str, Any]  			=						re.compile(r'\s(is_\S+?_available\(\))\s'		)
      for line in class_lines[start_idx : end_idx + 1]:
            snake_case__					:      Optional[Any]  			=						r.search(__SCREAMING_SNAKE_CASE		)
            if backend_condition is not None:
                  # replace the leading and trailing whitespaces to the space character " ".
                  snake_case__					:      Optional[int]  			=						' ' + backend_condition[0][1:-1] + ' '
                  snake_case__					:      List[str]  			=						r.sub(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)
                  break

      if def_line is None:
            # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
            # `all_model_classes` or `all_generative_model_classes`.
            snake_case__					:      int  			=						end_idx
      else:
            # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
            snake_case__					:      List[Any]  			=						start_idx - 1
            # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
            for idx in range(__SCREAMING_SNAKE_CASE							,			end_idx + 1		):
                  # These lines are going to be removed before writing to the test file.
                  snake_case__					:      Optional[int]  			=						None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
      snake_case__					:      List[Any]  			=						[x.__name__ for x in test_class.__bases__]
      if "PipelineTesterMixin" not in parent_classes:
            # Put `PipelineTesterMixin` just before `unittest.TestCase`
            snake_case__					:      Dict  			=						[x for x in parent_classes if x != 'TestCase'] + ['PipelineTesterMixin']
            if "TestCase" in parent_classes:
                  # Here we **assume** the original string is always with `unittest.TestCase`.
                  _parent_classes.append('unittest.TestCase'		)
            snake_case__					:      Optional[int]  			=						', '.join(_parent_classes		)
            for idx, line in enumerate(__SCREAMING_SNAKE_CASE		):
                  # Find the ending of the declaration of `test_class`
                  if line.strip().endswith('):'		):
                        # mark the lines of the declaration of `test_class` to be removed
                        for _idx in range(idx + 1		):
                              snake_case__					:      Any  			=						None  # noqa
                        break
        # Add the new, one-line, class declaration for `test_class`
            snake_case__					:      int  			=						f"class {test_class.__name__}({parent_classes}):\n"

      # Add indentation
      snake_case__					:      Optional[int]  			=						' ' * indent_level + line_to_add
      # Insert `pipeline_model_mapping` to `class_lines`.
      # (The line at `target_idx` should be kept by definition!)
      snake_case__					:      Any  			=						class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
      # Remove the lines that are marked to be removed
      snake_case__					:      Optional[Any]  			=						[x for x in class_lines if x is not None]

      # Move from test class to module (in order to write to the test file)
      snake_case__					:      Union[str, Any]  			=						inspect.getsourcelines(inspect.getmodule(__SCREAMING_SNAKE_CASE		)		)[0]
      # Be careful with the 1-off between line numbers and array indices
      snake_case__					:      int  			=						module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
      snake_case__					:      int  			=						''.join(__SCREAMING_SNAKE_CASE		)

      snake_case__					:      Optional[Any]  			=						inspect.getsourcefile(__SCREAMING_SNAKE_CASE		)
      with open(__SCREAMING_SNAKE_CASE							,			'w'							,			encoding='UTF-8'							,			newline='\n'		) as fp:
            fp.write(__SCREAMING_SNAKE_CASE		)

      return line_to_add






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False		)						->      Dict:
      snake_case__					:      Optional[int]  			=						find_test_class(__SCREAMING_SNAKE_CASE		)
      if test_class:
            add_pipeline_model_mapping(__SCREAMING_SNAKE_CASE							,			overwrite=__SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      A_ 			=		argparse.ArgumentParser()
      parser.add_argument(
          "--test_file", type=str, help="A path to the test file, starting with the repository's `tests` directory."
      )
      parser.add_argument(
          "--all",
          action="store_true",
          help="If to check and modify all test files.",
      )
      parser.add_argument(
          "--overwrite",
          action="store_true",
          help="If to overwrite a test class if it has already defined `pipeline_model_mapping`.",
      )
      A_ 			=		parser.parse_args()

      if not args.all and not args.test_file:
            raise ValueError("Please specify either `test_file` or pass `--all` to check/modify all test files.")
      elif args.all and args.test_file:
            raise ValueError("Only one of `--test_file` and `--all` could be specified.")

      A_ 			=		[]
      if args.test_file:
            A_ 			=		[args.test_file]
      else:
            A_ 			=		os.path.join("tests", "models", "**", "test_modeling_*.py")
            for test_file in glob.glob(pattern):
                  # `Flax` is not concerned at this moment
                  if not test_file.startswith("test_modeling_flax_"):
                        test_files.append(test_file)

      for test_file in test_files:
            if test_file in TEST_FILE_TO_IGNORE:
                  print(F'[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.')
                  continue
            add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)






'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import 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 ViltImageProcessor






class 					lowercase_			(	unittest.TestCase       ):





     def __init__(    self    :				Optional[Any]    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Optional[Any]=7    ,       __lowerCamelCase    :				Optional[Any]=3    ,       __lowerCamelCase    :				int=18    ,       __lowerCamelCase    :				List[str]=30    ,       __lowerCamelCase    :				List[str]=400    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				Any=None    ,       __lowerCamelCase    :				List[str]=2    ,       __lowerCamelCase    :				Optional[Any]=True    ,       __lowerCamelCase    :				List[Any]=[0.5, 0.5, 0.5]    ,       __lowerCamelCase    :				Any=[0.5, 0.5, 0.5]    ,       ):
           snake_case__					:      Dict  			=						size if size is not None else {'shortest_edge': 30}
           snake_case__					:      Any  			=						parent
           snake_case__					:      int  			=						batch_size
           snake_case__					:      List[str]  			=						num_channels
           snake_case__					:      str  			=						image_size
           snake_case__					:      List[Any]  			=						min_resolution
           snake_case__					:      Dict  			=						max_resolution
           snake_case__					:      str  			=						do_resize
           snake_case__					:      str  			=						size
           snake_case__					:      List[str]  			=						size_divisor
           snake_case__					:      List[Any]  			=						do_normalize
           snake_case__					:      Optional[Any]  			=						image_mean
           snake_case__					:      Union[str, Any]  			=						image_std





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Union[str, Any]=False ):
           if not batched:
                 snake_case__					:      int  			=						self.size['shortest_edge']
                 snake_case__					:      Union[str, Any]  			=						image_inputs[0]
                 if isinstance(__lowerCamelCase    ,       Image.Image ):
                       snake_case__			,       snake_case__					:      Any  			=						image.size
                 else:
                       snake_case__			,       snake_case__					:      Optional[Any]  			=						image.shape[1], image.shape[2]
                 snake_case__					:      int  			=						size / min(__lowerCamelCase    ,       __lowerCamelCase )
                 if h < w:
                       snake_case__			,       snake_case__					:      Tuple  			=						size, scale * w
                 else:
                       snake_case__			,       snake_case__					:      List[str]  			=						scale * h, size

                 snake_case__					:      List[Any]  			=						int((1333 / 800) * size )
                 if max(__lowerCamelCase    ,       __lowerCamelCase ) > max_size:
                       snake_case__					:      Dict  			=						max_size / max(__lowerCamelCase    ,       __lowerCamelCase )
                       snake_case__					:      List[Any]  			=						newh * scale
                       snake_case__					:      Optional[Any]  			=						neww * scale

                 snake_case__			,       snake_case__					:      str  			=						int(newh + 0.5 ), int(neww + 0.5 )
                 snake_case__			,       snake_case__					:      Any  			=						(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 snake_case__					:      int  			=						[]
                 for image in image_inputs:
                       snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 snake_case__					:      Any  			=						max(__lowerCamelCase    ,       key=lambda __lowerCamelCase : item[0] )[0]
                 snake_case__					:      List[Any]  			=						max(__lowerCamelCase    ,       key=lambda __lowerCamelCase : item[1] )[1]

           return expected_height, expected_width






@require_torch
@require_vision
class 					lowercase_			(	lowerCAmelCase_	,					unittest.TestCase       ):
     A_       							= ViltImageProcessor if is_vision_available() else None





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      int  			=						ViltImageProcessingTester(self )





     @property
     def  _lowerCAmelCase			(    self    :				List[Any] ):
           return self.image_processor_tester.prepare_image_processor_dict()





     def  _lowerCAmelCase			(    self    :				Optional[int] ):
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_mean' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'image_std' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_normalize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'do_resize' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size' ) )
           self.assertTrue(hasattr(__lowerCamelCase    ,       'size_divisor' ) )





     def  _lowerCAmelCase			(    self    :				Tuple ):
           snake_case__					:      Optional[Any]  			=						self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 30} )

           snake_case__					:      List[str]  			=						self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42 )
           self.assertEqual(image_processor.size    ,       {'shortest_edge': 42} )





     def  _lowerCAmelCase			(    self    :				str ):
           pass





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           # Initialize image_processing
           snake_case__					:      int  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           snake_case__					:      str  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       Image.Image )

           # Test not batched input
           snake_case__					:      int  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           # Initialize image_processing
           snake_case__					:      List[Any]  			=						self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       numpify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       np.ndarray )

           # Test not batched input
           snake_case__					:      List[Any]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Tuple  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Union[str, Any]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )





     def  _lowerCAmelCase			(    self    :				str ):
           # Initialize image_processing
           snake_case__					:      str  			=						self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           snake_case__					:      Dict  			=						prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=__lowerCamelCase    ,       torchify=__lowerCamelCase )
           for image in image_inputs:
                 self.assertIsInstance(__lowerCamelCase    ,       torch.Tensor )

           # Test not batched input
           snake_case__					:      Optional[int]  			=						image_processing(image_inputs[0]    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Optional[int]  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,       )

           # Test batched
           snake_case__					:      Optional[int]  			=						image_processing(__lowerCamelCase    ,       return_tensors='pt' ).pixel_values

           snake_case__			,       snake_case__					:      Tuple  			=						self.image_processor_tester.get_expected_values(__lowerCamelCase    ,       batched=__lowerCamelCase )
           self.assertEqual(
               encoded_images.shape    ,       (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    ,       )
