

'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase									=     logging.get_logger(__name__)


class        A__   (   A__      ):
    lowercase						       =		["pixel_values"]



    def __init__(       self		,					UpperCamelCase__ = True		,					UpperCamelCase__ = None		,					UpperCamelCase__ = PILImageResampling.BICUBIC		,					UpperCamelCase__ = True		,					UpperCamelCase__ = True		,					UpperCamelCase__ = 1 / 255		,					UpperCamelCase__ = None		,					UpperCamelCase__ = True		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   Union[str, Any]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase_							)
           A_						       =   size if size is not None else {'''height''': 224, '''width''': 224}
           A_						       =   get_size_dict(UpperCamelCase_							)
           A_						       =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A_						       =   get_size_dict(UpperCamelCase_		,					default_to_square=UpperCamelCase_		,					param_name="""crop_size"""							)

           A_						       =   do_resize
           A_						       =   do_rescale
           A_						       =   do_normalize
           A_						       =   do_center_crop
           A_						       =   crop_size
           A_						       =   size
           A_						       =   resample
           A_						       =   rescale_factor
           A_						       =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           A_						       =   image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = PILImageResampling.BILINEAR		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   Tuple:





           '''simple docstring'''
           A_						       =   get_size_dict(UpperCamelCase_							)

           if "shortest_edge" in size:
                  A_						       =   get_resize_output_image_size(UpperCamelCase_		,					size=size["""shortest_edge"""]		,					default_to_square=UpperCamelCase_							)
                  # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
           elif "height" in size and "width" in size:
                  A_						       =   (size['''height'''], size['''width'''])
           else:
                  raise ValueError(f'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'''							)
           return resize(UpperCamelCase_		,					size=UpperCamelCase_		,					resample=UpperCamelCase_		,					data_format=UpperCamelCase_		,					**UpperCamelCase_							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   Any:





           '''simple docstring'''
           A_						       =   get_size_dict(UpperCamelCase_							)
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''							)
           return center_crop(UpperCamelCase_		,					size=(size["""height"""], size["""width"""])		,					data_format=UpperCamelCase_		,					**UpperCamelCase_							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					**UpperCamelCase__							)    ->   Optional[int]:





           '''simple docstring'''
           return rescale(UpperCamelCase_		,					scale=UpperCamelCase_		,					data_format=UpperCamelCase_		,					**UpperCamelCase_							)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					**UpperCamelCase__		,					)    ->   Optional[Any]:





           '''simple docstring'''
           return normalize(UpperCamelCase_		,					mean=UpperCamelCase_		,					std=UpperCamelCase_		,					data_format=UpperCamelCase_		,					**UpperCamelCase_							)







    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = None		,					UpperCamelCase__ = ChannelDimension.FIRST		,					**UpperCamelCase__		,					)    ->   int:





           '''simple docstring'''
           A_						       =   do_resize if do_resize is not None else self.do_resize
           A_						       =   do_rescale if do_rescale is not None else self.do_rescale
           A_						       =   do_normalize if do_normalize is not None else self.do_normalize
           A_						       =   do_center_crop if do_center_crop is not None else self.do_center_crop
           A_						       =   crop_size if crop_size is not None else self.crop_size
           A_						       =   get_size_dict(UpperCamelCase_		,					param_name="""crop_size"""		,					default_to_square=UpperCamelCase_							)
           A_						       =   resample if resample is not None else self.resample
           A_						       =   rescale_factor if rescale_factor is not None else self.rescale_factor
           A_						       =   image_mean if image_mean is not None else self.image_mean
           A_						       =   image_std if image_std is not None else self.image_std

           A_						       =   size if size is not None else self.size
           A_						       =   get_size_dict(UpperCamelCase_							)

           if not is_batched(UpperCamelCase_							):
                  A_						       =   [images]

           if not valid_images(UpperCamelCase_							):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""							)

           if do_resize and size is None:
                  raise ValueError("""Size must be specified if do_resize is True."""							)

           if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

           if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

           # All transformations expect numpy arrays.
           A_						       =   [to_numpy_array(UpperCamelCase_							) for image in images]

           if do_resize:
                  A_						       =   [self.resize(image=UpperCamelCase_		,					size=UpperCamelCase_		,					resample=UpperCamelCase_							) for image in images]

           if do_center_crop:
                  A_						       =   [self.center_crop(image=UpperCamelCase_		,					size=UpperCamelCase_							) for image in images]

           if do_rescale:
                  A_						       =   [self.rescale(image=UpperCamelCase_		,					scale=UpperCamelCase_							) for image in images]

           if do_normalize:
                  A_						       =   [self.normalize(image=UpperCamelCase_		,					mean=UpperCamelCase_		,					std=UpperCamelCase_							) for image in images]

           A_						       =   [to_channel_dimension_format(UpperCamelCase_		,					UpperCamelCase_							) for image in images]

           A_						       =   {'''pixel_values''': images}
           return BatchFeature(data=UpperCamelCase_		,					tensor_type=UpperCamelCase_							)




'''simple docstring'''
import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					test_file.split(os.path.sep  )
					if components[0:2] != ["tests", "models"]:
										raise ValueError(
										    '''`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '''
										    f'''{test_file} instead.'''  )
					UpperCamelCase__  :List[Any]				   =					components[-1]
					if not test_fn.endswith('''py'''  ):
										raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''  )
					if not test_fn.startswith('''test_modeling_'''  ):
										raise ValueError(
										    f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''  )

					UpperCamelCase__  :Optional[Any]				   =					components[:-1] + [test_fn.replace('''.py'''					,   ''''''  )]
					UpperCamelCase__  :str				   =					'''.'''.join(__a  )

					return test_module_path


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					get_module_path(__a  )
					UpperCamelCase__  :List[str]				   =					importlib.import_module(__a  )

					return test_module


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Dict				   =					[]
					UpperCamelCase__  :Optional[Any]				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										if attr.endswith('''ModelTester'''  ):
															tester_classes.append(getattr(__a					,   __a  )  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :List[Any]				   =					[]
					UpperCamelCase__  :Dict				   =					get_test_module(__a  )
					for attr in dir(__a  ):
										UpperCamelCase__  :Dict				   =					getattr(__a					,   __a  )
										# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
										# `all_model_classes` is not empty (which also excludes other special classes).
										UpperCamelCase__  :Optional[int]				   =					getattr(__a					,   '''all_model_classes'''					,   []  )
										if len(__a  ) > 0:
															test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :int				   =					get_test_classes(__a  )
					UpperCamelCase__  :Any				   =					set()
					for test_class in test_classes:
										model_classes.update(test_class.all_model_classes  )

					# sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					test_class()
					if hasattr(__a					,   '''setUp'''  ):
										test.setUp()

					UpperCamelCase__  :Union[str, Any]				   =					None
					if hasattr(__a					,   '''model_tester'''  ):
										# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
										if test.model_tester is not None:
															UpperCamelCase__  :List[str]				   =					test.model_tester.__class__

					return model_tester


def      a (     __a					,   __a  )    ->      Any:



					'''simple docstring'''






					UpperCamelCase__  :str				   =					get_test_classes(__a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										if model_class in test_class.all_model_classes:
															target_test_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a					,   __a  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					get_test_classes_for_model(__a					,   __a  )

					UpperCamelCase__  :List[str]				   =					[]
					for test_class in test_classes:
										UpperCamelCase__  :int				   =					get_model_tester_from_test_class(__a  )
										if tester_class is not None:
															tester_classes.append(__a  )

    # sort with class names
					return sorted(__a					,   key=lambda __a  : x.__name__  )


def      a (     __a  )    ->      int:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_test_classes(__a  )
					UpperCamelCase__  :Dict				   =					{test_class: get_model_tester_from_test_class(__a  ) for test_class in test_classes}
					return test_tester_mapping


def      a (     __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Any				   =					get_model_classes(__a  )
					UpperCamelCase__  :Tuple				   =					{
					    model_class: get_test_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_test_mapping


def      a (     __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					get_model_classes(__a  )
					UpperCamelCase__  :Union[str, Any]				   =					{
					    model_class: get_tester_classes_for_model(__a					,   __a  ) for model_class in model_classes
					}
					return model_to_tester_mapping







def      a (     __a  )    ->      List[Any]:



					'''simple docstring'''






					if isinstance(__a					,   __a  ):
										return o
					elif isinstance(__a					,   __a  ):
										return o.__name__
					elif isinstance(__a					,   (list, tuple)  ):
										return [to_json(__a  ) for x in o]
					elif isinstance(__a					,   __a  ):
										return {to_json(__a  ): to_json(__a  ) for k, v in o.items()}
					else:
										return o