



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 	a						(				_UpperCAmelCase							: Union[str, Any]			,       _UpperCAmelCase							: Optional[Any]=False			,       _UpperCAmelCase							: Union[str, Any]=False			,       _UpperCAmelCase							: Union[str, Any]=False						):







		'''simple docstring'''





		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

		__UpperCAmelCase   :					str													=     []
		for i in range(image_processor_tester.batch_size						):
				if equal_resolution:
						__UpperCAmelCase   :					List[Any]													=     image_processor_tester.max_resolution
				else:
						# To avoid getting image width/height 0
						__UpperCAmelCase   :					List[Any]													=     image_processor_tester.min_resolution
						if getattr(_UpperCAmelCase			,       '''size_divisor'''			,       _UpperCAmelCase						):
								# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
								__UpperCAmelCase   :					List[Any]													=     max(image_processor_tester.size_divisor			,       _UpperCAmelCase						)
						__UpperCAmelCase							, __UpperCAmelCase   :					List[Any]													=     np.random.choice(np.arange(_UpperCAmelCase			,       image_processor_tester.max_resolution						)			,       2						)
				image_inputs.append(
				    np.random.randint(2_55			,       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(_UpperCAmelCase			,       0			,       -1						)						) for image in image_inputs]

		if torchify:
				__UpperCAmelCase   :					Dict													=     [torch.from_numpy(_UpperCAmelCase						) for image in image_inputs]

		return image_inputs

def 	a						(				_UpperCAmelCase							: Any			,       _UpperCAmelCase							: Tuple=10			,       _UpperCAmelCase							: Union[str, Any]=10			,       _UpperCAmelCase							: Tuple=False			,       _UpperCAmelCase							: int=False						):







		'''simple docstring'''





		__UpperCAmelCase   :					Union[str, Any]													=     []
		for i in range(image_processor_tester.num_frames						):
				video.append(np.random.randint(2_55			,       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(_UpperCAmelCase			,       0			,       -1						)						) for frame in video]

		if torchify:
				__UpperCAmelCase   :					Optional[int]													=     [torch.from_numpy(_UpperCAmelCase						) for frame in video]

		return video

def 	a						(				_UpperCAmelCase							: List[str]			,       _UpperCAmelCase							: int=False			,       _UpperCAmelCase							: Optional[Any]=False			,       _UpperCAmelCase							: str=False						):







		'''simple docstring'''





		assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

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

		return video_inputs

class 		UpperCAmelCase__      :




			'''simple docstring'''






			UpperCamelCase      						=	None




			def 			snake_case__					(       self							:			Union[str, Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class(**self.image_processor_dict			)
					__UpperCAmelCase   :					Union[str, Any]													=     json.loads(image_processor.to_json_string()			)
					for key, value in self.image_processor_dict.items():
							self.assertEqual(obj[key]	,	a_			)




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					int													=     self.image_processing_class(**self.image_processor_dict			)

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase   :					str													=     os.path.join(a_	,	'''image_processor.json'''			)
							image_processor_first.to_json_file(a_			)
							__UpperCAmelCase   :					Dict													=     self.image_processing_class.from_json_file(a_			)

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




			def 			snake_case__					(       self							:			str			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     self.image_processing_class(**self.image_processor_dict			)

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase   :					Dict													=     image_processor_first.save_pretrained(a_			)[0]
							check_json_file_has_correct_format(a_			)
							__UpperCAmelCase   :					Union[str, Any]													=     self.image_processing_class.from_pretrained(a_			)

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




			def 			snake_case__					(       self							:			List[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[int]													=     self.image_processing_class()
					self.assertIsNotNone(a_			)




			@require_torch
			@require_vision
			def 			snake_case__					(       self							:			Tuple			):
					'''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=a_	,	torchify=a_			)

							__UpperCAmelCase   :					Dict													=     image_processor(a_	,	return_tensors='''pt'''			)
							# for layoutLM compatiblity
							self.assertEqual(encoding.pixel_values.device	,	torch.device('''cpu'''			)			)
							self.assertEqual(encoding.pixel_values.dtype	,	torch.floataa			)

							__UpperCAmelCase   :					Any													=     image_processor(a_	,	return_tensors='''pt'''			).to(torch.floataa			)
							self.assertEqual(encoding.pixel_values.device	,	torch.device('''cpu'''			)			)
							self.assertEqual(encoding.pixel_values.dtype	,	torch.floataa			)

							__UpperCAmelCase   :					Any													=     image_processor(a_	,	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(a_			):
									__UpperCAmelCase   :					Any													=     image_processor(a_	,	return_tensors='''pt'''			).to(torch.bfloataa	,	'''cpu'''			)

							# Try with text + image feature
							__UpperCAmelCase   :					Tuple													=     image_processor(a_	,	return_tensors='''pt'''			)
							encoding.update({'''input_ids''': torch.LongTensor([[1, 2, 3], [4, 5, 6]]			)}			)
							__UpperCAmelCase   :					List[Any]													=     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			)










def 	a						(				_UpperCAmelCase							: str						):







		'''simple docstring'''





		__UpperCAmelCase   :					Optional[Any]													=     53_81
		for x in s:
				__UpperCAmelCase   :					str													=     ((hash_value << 5) + hash_value) + ord(_UpperCAmelCase						)
		return hash_value & 0xFFFF_FFFF





