


"""simple docstring"""
SCREAMING_SNAKE_CASE					:       List[Any]							   =							1000






def 						lowercase				(	_snake_case		: float	)	->float:
	"""simple docstring"""





	return x * x






def 						lowercase				(	_snake_case		: Union[str, Any]	,  _snake_case		: float	,  _snake_case		: float	,  _snake_case		: int = 4	)	->float:
	"""simple docstring"""





	assert callable(
	    _snake_case	), f"""the function(object) passed should be callable your input : {function}"""
	assert isinstance(_snake_case	,  (float, int)	), f"""a should be float or integer your input : {a}"""
	assert isinstance(function(_snake_case	)	,  (float, int)	), (
	    "the function should return integer or float return type of your function, "
	    f"""{type(_snake_case	)}"""
	)
	assert isinstance(_snake_case	,  (float, int)	), f"""b should be float or integer your input : {b}"""
	assert (
	    isinstance(_snake_case	,  _snake_case	) and precision > 0
	), f"""precision should be positive integer your input : {precision}"""

	# just applying the formula of simpson for approximate integration written in
	# mentioned article in first comment of this file and above this function

	__snake_case      :		Tuple         =							(b - a) / N_STEPS
	__snake_case      :		Union[str, Any]         =							function(_snake_case	) + function(_snake_case	)

	for i in range(1	,  _snake_case	):
		__snake_case      :		List[str]         =							a + h * i
		result += function(_snake_case	) * (4 if i % 2 else 2)

	result *= h / 3
	return round(_snake_case	,  _snake_case	)


if __name__ == "__main__":
			import doctest

			doctest.testmod()











"""simple docstring"""
import json
import os
import tempfile

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


if is_torch_available():
    import numpy as np
    import torch

if is_vision_available():
    from PIL import Image






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Any  ,	__lowerCAmelCase			:		Dict=False  ,	__lowerCAmelCase			:		Optional[Any]=False  ,	__lowerCAmelCase			:		int=False						)    ->       List[str]:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 _UpperCamelCase      :	Optional[int]					      =  []
 for i in range(image_processor_tester.batch_size						):
  if equal_resolution:
   _UpperCamelCase      :	List[str]					      =  image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   _UpperCamelCase      :	int					      =  image_processor_tester.min_resolution
   if getattr(__lowerCAmelCase  ,	"size_divisor"  ,	__lowerCAmelCase						):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    _UpperCamelCase      :	Optional[int]					      =  max(image_processor_tester.size_divisor  ,	__lowerCAmelCase						)
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[Any]					      =  np.random.choice(np.arange(__lowerCAmelCase  ,	image_processor_tester.max_resolution						)  ,	2						)
  image_inputs.append(
      np.random.randint(255  ,	size=(image_processor_tester.num_channels, width, height)  ,	dtype=np.uinta						)						)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _UpperCamelCase      :	Optional[Any]					      =  [Image.fromarray(np.moveaxis(__lowerCAmelCase  ,	0  ,	-1						)						) for image in image_inputs]

 if torchify:
  _UpperCamelCase      :	str					      =  [torch.from_numpy(__lowerCAmelCase						) for image in image_inputs]

 return image_inputs






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		Optional[Any]  ,	__lowerCAmelCase			:		Tuple=10  ,	__lowerCAmelCase			:		str=10  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		List[Any]=False						)    ->       Optional[int]:
 _UpperCamelCase      :	Optional[int]					      =  []
 for i in range(image_processor_tester.num_frames						):
  video.append(np.random.randint(255  ,	size=(image_processor_tester.num_channels, width, height)  ,	dtype=np.uinta						)						)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _UpperCamelCase      :	Optional[int]					      =  [Image.fromarray(np.moveaxis(__lowerCAmelCase  ,	0  ,	-1						)						) for frame in video]

 if torchify:
  _UpperCamelCase      :	int					      =  [torch.from_numpy(__lowerCAmelCase						) for frame in video]

 return video






def 				__lowerCAmelCase					(					__lowerCAmelCase			:		List[Any]  ,	__lowerCAmelCase			:		Union[str, Any]=False  ,	__lowerCAmelCase			:		Any=False  ,	__lowerCAmelCase			:		Dict=False						)    ->       Any:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 _UpperCamelCase      :	Optional[Any]					      =  []
 for i in range(image_processor_tester.batch_size						):
  if equal_resolution:
   _UpperCamelCase      :	List[Any]					      =  image_processor_tester.max_resolution
  else:
   _UpperCamelCase      ,	_UpperCamelCase      :	Optional[int]					      =  np.random.choice(
       np.arange(image_processor_tester.min_resolution  ,	image_processor_tester.max_resolution						)  ,	2						)
   _UpperCamelCase      :	List[Any]					      =  prepare_video(
       image_processor_tester=__lowerCAmelCase  ,	width=__lowerCAmelCase  ,	height=__lowerCAmelCase  ,	numpify=__lowerCAmelCase  ,	torchify=__lowerCAmelCase  ,	)
  video_inputs.append(__lowerCAmelCase						)

 return video_inputs







class  _SCREAMING_SNAKE_CASE       :



 '''simple docstring'''





 __UpperCAmelCase		   =							None






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	str					      =  self.image_processing_class(**self.image_processor_dict   )
  _UpperCamelCase      :	Optional[int]					      =  json.loads(image_processor.to_json_string()   )
  for key, value in self.image_processor_dict.items():
   self.assertEqual(obj[key]					,     lowerCAmelCase__   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Union[str, Any]					      =  self.image_processing_class(**self.image_processor_dict   )

  with tempfile.TemporaryDirectory() as tmpdirname:
   _UpperCamelCase      :	Tuple					      =  os.path.join(lowerCAmelCase__					,     "image_processor.json"   )
   image_processor_first.to_json_file(lowerCAmelCase__   )
   _UpperCamelCase      :	Tuple					      =  self.image_processing_class.from_json_file(lowerCAmelCase__   )

  self.assertEqual(image_processor_second.to_dict()					,     image_processor_first.to_dict()   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Dict					      =  self.image_processing_class(**self.image_processor_dict   )

  with tempfile.TemporaryDirectory() as tmpdirname:
   _UpperCamelCase      :	Optional[int]					      =  image_processor_first.save_pretrained(lowerCAmelCase__   )[0]
   check_json_file_has_correct_format(lowerCAmelCase__   )
   _UpperCamelCase      :	int					      =  self.image_processing_class.from_pretrained(lowerCAmelCase__   )

  self.assertEqual(image_processor_second.to_dict()					,     image_processor_first.to_dict()   )






 def lowercase_      (self   ):

  '''simple docstring'''







  _UpperCamelCase      :	Tuple					      =  self.image_processing_class()
  self.assertIsNotNone(lowerCAmelCase__   )






 @require_torch
 @require_vision
 def lowercase_      (self   ):

  '''simple docstring'''







  if self.test_cast_dtype is not None:
   # Initialize image_processor
   _UpperCamelCase      :	Optional[int]					      =  self.image_processing_class(**self.image_processor_dict   )

   # create random PyTorch tensors
   _UpperCamelCase      :	str					      =  prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase__					,     torchify=lowerCAmelCase__   )

   _UpperCamelCase      :	List[Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   )
   # for layoutLM compatiblity
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )

   _UpperCamelCase      :	List[Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to(torch.floataa   )
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )

   _UpperCamelCase      :	str					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to("cpu"					,     torch.bfloataa   )
   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.bfloataa   )

   with self.assertRaises(lowerCAmelCase__   ):
    _UpperCamelCase      :	Optional[int]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   ).to(torch.bfloataa					,     "cpu"   )

   # Try with text + image feature
   _UpperCamelCase      :	Union[str, Any]					      =  image_processor(lowerCAmelCase__					,     return_tensors="pt"   )
   encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]]   )}   )
   _UpperCamelCase      :	Optional[int]					      =  encoding.to(torch.floataa   )

   self.assertEqual(encoding.pixel_values.device					,     torch.device("cpu"   )   )
   self.assertEqual(encoding.pixel_values.dtype					,     torch.floataa   )
   self.assertEqual(encoding.input_ids.dtype					,     torch.long   )



