




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 MobileViTImageProcessor
class        __snake_case  (unittest.TestCase		):



				def __init__(      self      :		Union[str, Any]		,	_UpperCAmelCase      :		List[Any]		,	_UpperCAmelCase      :		Union[str, Any]=7		,	_UpperCAmelCase      :		Optional[Any]=3		,	_UpperCAmelCase      :		str=18		,	_UpperCAmelCase      :		str=30		,	_UpperCAmelCase      :		List[Any]=400		,	_UpperCAmelCase      :		List[str]=True		,	_UpperCAmelCase      :		Dict=None		,	_UpperCAmelCase      :		Dict=True		,	_UpperCAmelCase      :		List[Any]=None		,	_UpperCAmelCase      :		List[Any]=True		,	)  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      size if size is not None else {"""shortest_edge""": 20}
								_lowerCAmelCase    :    Tuple										=      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								_lowerCAmelCase    :    Any										=      parent
								_lowerCAmelCase    :    int										=      batch_size
								_lowerCAmelCase    :    List[str]										=      num_channels
								_lowerCAmelCase    :    Any										=      image_size
								_lowerCAmelCase    :    Optional[int]										=      min_resolution
								_lowerCAmelCase    :    List[str]										=      max_resolution
								_lowerCAmelCase    :    Dict										=      do_resize
								_lowerCAmelCase    :    List[str]										=      size
								_lowerCAmelCase    :    Union[str, Any]										=      do_center_crop
								_lowerCAmelCase    :    Union[str, Any]										=      crop_size
								_lowerCAmelCase    :    Tuple										=      do_flip_channel_order



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_flip_channel_order": self.do_flip_channel_order,
								}






@require_torch
@require_vision
class        __snake_case  (_a						,	unittest.TestCase		):
				lowerCAmelCase__				=     MobileViTImageProcessor if is_vision_available() else None



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      MobileViTImageProcessingTester(self      )



				@property
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    List[Any]:





								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(_UpperCAmelCase		,	"""do_resize"""      )      )
								self.assertTrue(hasattr(_UpperCAmelCase		,	"""size"""      )      )
								self.assertTrue(hasattr(_UpperCAmelCase		,	"""do_center_crop"""      )      )
								self.assertTrue(hasattr(_UpperCAmelCase		,	"""center_crop"""      )      )
								self.assertTrue(hasattr(_UpperCAmelCase		,	"""do_flip_channel_order"""      )      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    List[str]										=      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}      )

								_lowerCAmelCase    :    str										=      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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Dict:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Tuple:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_lowerCAmelCase    :    List[Any]										=      prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_UpperCAmelCase      )
								for image in image_inputs:
												self.assertIsInstance(_UpperCAmelCase		,	Image.Image      )

								# Test not batched input
								_lowerCAmelCase    :    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.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )		,	)

								# Test batched
								_lowerCAmelCase    :    Tuple										=      image_processing(_UpperCAmelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Union[str, Any]:





								'''simple docstring'''




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

								# Test not batched input
								_lowerCAmelCase    :    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.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )		,	)

								# Test batched
								_lowerCAmelCase    :    Optional[Any]										=      image_processing(_UpperCAmelCase		,	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       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_lowerCAmelCase    :    List[str]										=      prepare_image_inputs(self.image_processor_tester		,	equal_resolution=_UpperCAmelCase		,	torchify=_UpperCAmelCase      )
								for image in image_inputs:
												self.assertIsInstance(_UpperCAmelCase		,	torch.Tensor      )

								# Test not batched input
								_lowerCAmelCase    :    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.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )		,	)

								# Test batched
								_lowerCAmelCase    :    str										=      image_processing(_UpperCAmelCase		,	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 							_UpperCAmelCase  (UpperCamelCase_ : str      ,    UpperCamelCase_ : bool = False       ):
				'''simple docstring'''

				if not isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Tuple										=      F"Expected string as input, found {type(UpperCamelCase_       )}"
								raise ValueError(UpperCamelCase_       )
				if not isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								_lowerCAmelCase    :    Optional[Any]										=      F"Expected boolean as use_pascal parameter, found {type(UpperCamelCase_       )}"
								raise ValueError(UpperCamelCase_       )

				_lowerCAmelCase    :    Any										=      input_str.split("""_"""       )

				_lowerCAmelCase    :    str										=      0 if use_pascal else 1

				_lowerCAmelCase    :    Any										=      words[start_index:]

				_lowerCAmelCase    :    List[str]										=      [word[0].upper() + word[1:] for word in words_to_capitalize]

				_lowerCAmelCase    :    Any										=      """""" if use_pascal else words[0]

				return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
	from doctest import testmod

	testmod()

