






"""simple docstring"""







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 GLPNImageProcessor


class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__=7	,  snake_case__=3	,  snake_case__=18	,  snake_case__=30	,  snake_case__=400	,  snake_case__=True	,  snake_case__=32	,  snake_case__=True	,  ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	parent
												lowercase__    :				Dict=	batch_size
												lowercase__    :				Dict=	num_channels
												lowercase__    :				Union[str, Any]=	image_size
												lowercase__    :				List[str]=	min_resolution
												lowercase__    :				str=	max_resolution
												lowercase__    :				str=	do_resize
												lowercase__    :				Tuple=	size_divisor
												lowercase__    :				Optional[Any]=	do_rescale




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return {
												    "do_resize": self.do_resize,
												    "size_divisor": self.size_divisor,
												    "do_rescale": self.do_rescale,
												}


@require_torch
@require_vision
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= GLPNImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	GLPNImageProcessingTester(self      )




						@property
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[Any]=	self.image_processing_class(**self.image_processor_dict      )
												self.assertTrue(hasattr(snake_case__	,  "do_resize"      )      )
												self.assertTrue(hasattr(snake_case__	,  "size_divisor"      )      )
												self.assertTrue(hasattr(snake_case__	,  "resample"      )      )
												self.assertTrue(hasattr(snake_case__	,  "do_rescale"      )      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												pass




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

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												lowercase__    :				int=	image_processing(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				Any=	self.image_processing_class(**self.image_processor_dict      )
												# create random numpy tensors
												lowercase__    :				str=	prepare_image_inputs(self.image_processor_tester	,  equal_resolution=snake_case__	,  numpify=snake_case__      )
												for image in image_inputs:
																		self.assertIsInstance(snake_case__	,  np.ndarray      )

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												lowercase__    :				Dict=	image_processing(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__    :				Tuple=	self.image_processing_class(**self.image_processor_dict      )
												# create random PyTorch tensors
												lowercase__    :				Any=	prepare_image_inputs(self.image_processor_tester	,  equal_resolution=snake_case__	,  torchify=snake_case__      )
												for image in image_inputs:
																		self.assertIsInstance(snake_case__	,  torch.Tensor      )

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												lowercase__    :				Dict=	image_processing(image_inputs[0]	,  return_tensors="pt"      ).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0      )
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0      )














"""simple docstring"""







from __future__ import annotations





def 						lowercase__(A ,		A						)    ->list[str]:




						"""simple docstring"""



						if partitions <= 0:
												raise ValueError("partitions must be a positive number!"						)
						if partitions > number_of_bytes:
												raise ValueError("partitions can not > number_of_bytes!"						)
						lowercase__    :				List[str]=	number_of_bytes // partitions
						lowercase__    :				Dict=	[]
						for i in range(A						):
												lowercase__    :				Union[str, Any]=	i * bytes_per_partition + 1
												lowercase__    :				Any=	(
												    number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
												)
												allocation_list.append(f'''{start_bytes}-{end_bytes}'''						)
						return allocation_list


if __name__ == "__main__":
			import doctest

			doctest.testmod()






