






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    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    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."""       )
				lowercase__     				=    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."""       )

				lowercase__     				=    components[:-1] + [test_fn.replace('''.py'''	,     ''''''       )]
				lowercase__     				=    '''.'''.join(UpperCamelCase__       )

				return test_module_path


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_module_path(UpperCamelCase__       )
				lowercase__     				=    importlib.import_module(UpperCamelCase__       )

				return test_module


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []
				lowercase__     				=    get_test_module(UpperCamelCase__       )
				for attr in dir(UpperCamelCase__       ):
								if attr.endswith('''ModelTester'''       ):
												tester_classes.append(getattr(UpperCamelCase__	,     UpperCamelCase__       )       )

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


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []
				lowercase__     				=    get_test_module(UpperCamelCase__       )
				for attr in dir(UpperCamelCase__       ):
								lowercase__     				=    getattr(UpperCamelCase__	,     UpperCamelCase__       )
								# (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).
								lowercase__     				=    getattr(UpperCamelCase__	,     '''all_model_classes'''	,     []       )
								if len(UpperCamelCase__       ) > 0:
												test_classes.append(UpperCamelCase__       )

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


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_test_classes(UpperCamelCase__       )
				lowercase__     				=    set()
				for test_class in test_classes:
								model_classes.update(test_class.all_model_classes       )

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


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    test_class()
				if hasattr(UpperCamelCase__	,     '''setUp'''       ):
								test.setUp()

				lowercase__     				=    None
				if hasattr(UpperCamelCase__	,     '''model_tester'''       ):
								# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
								if test.model_tester is not None:
												lowercase__     				=    test.model_tester.__class__

				return model_tester


def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_test_classes(UpperCamelCase__       )

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

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


def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_test_classes_for_model(UpperCamelCase__	,     UpperCamelCase__       )

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

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


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_test_classes(UpperCamelCase__       )
				lowercase__     				=    {test_class: get_model_tester_from_test_class(UpperCamelCase__       ) for test_class in test_classes}
				return test_tester_mapping


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_model_classes(UpperCamelCase__       )
				lowercase__     				=    {
				    model_class: get_test_classes_for_model(UpperCamelCase__	,     UpperCamelCase__       ) for model_class in model_classes
				}
				return model_test_mapping


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    get_model_classes(UpperCamelCase__       )
				lowercase__     				=    {
				    model_class: get_tester_classes_for_model(UpperCamelCase__	,     UpperCamelCase__       ) for model_class in model_classes
				}
				return model_to_tester_mapping




def 						a    (		lowerCamelCase_       ):






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












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 MobileNetVaImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					Any,						lowerCamelCase		:					Tuple=7,						lowerCamelCase		:					str=3,						lowerCamelCase		:					Tuple=18,						lowerCamelCase		:					int=30,						lowerCamelCase		:					Tuple=400,						lowerCamelCase		:					Any=True,						lowerCamelCase		:					Any=None,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Union[str, Any]=None,						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''shortest_edge''': 20}
								lowercase__     				=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_center_crop
								lowercase__     				=    crop_size




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= MobileNetVaImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    MobileNetVaImageProcessingTester(self						)




				@property
				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_center_crop'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''crop_size'''						)						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 20}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 18, '''width''': 18}						)

								lowercase__     				=    self.image_processing_class.from_dict(self.image_processor_dict,						size=42,						crop_size=84						)
								self.assertEqual(image_processor.size,						{'''shortest_edge''': 42}						)
								self.assertEqual(image_processor.crop_size,						{'''height''': 84, '''width''': 84}						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_processor_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processing(lowerCamelCase,						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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    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
								lowercase__     				=    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processing(lowerCamelCase,						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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								# Initialize image_processing
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    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
								lowercase__     				=    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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processing(lowerCamelCase,						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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),						)




