

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 					_a		(unittest.TestCase       ):


					'''simple docstring'''






					def __init__(				self					, A__					, A__=7					, A__=3					, A__=18					, A__=30					, A__=400					, A__=True					, A__=32					, A__=True					, ):
						A__							:					List[Any]       =	parent
						A__							:					List[str]       =	batch_size
						A__							:					Tuple       =	num_channels
						A__							:					int       =	image_size
						A__							:					Any       =	min_resolution
						A__							:					str       =	max_resolution
						A__							:					List[Any]       =	do_resize
						A__							:					str       =	size_divisor
						A__							:					Optional[int]       =	do_rescale






					def 						__A    (				self    ):
						return {
						    "do_resize": self.do_resize,
						    "size_divisor": self.size_divisor,
						    "do_rescale": self.do_rescale,
						}



@require_torch
@require_vision
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     List[str]													= GLPNImageProcessor if is_vision_available() else None






					def 						__A    (				self    ):
						A__							:					str       =	GLPNImageProcessingTester(self    )






					@property
					def 						__A    (				self    ):
						return self.image_processor_tester.prepare_image_processor_dict()






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	self.image_processing_class(**self.image_processor_dict    )
						self.assertTrue(hasattr(A__					, """do_resize"""    )    )
						self.assertTrue(hasattr(A__					, """size_divisor"""    )    )
						self.assertTrue(hasattr(A__					, """resample"""    )    )
						self.assertTrue(hasattr(A__					, """do_rescale"""    )    )






					def 						__A    (				self    ):
						pass






					def 						__A    (				self    ):
						# Initialize image_processing
						A__							:					Dict       =	self.image_processing_class(**self.image_processor_dict    )
						# create random PIL images
						A__							:					int       =	prepare_image_inputs(self.image_processor_tester					, equal_resolution=A__    )
						for image in image_inputs:
							self.assertIsInstance(A__					, Image.Image    )

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A__							:					Optional[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 						__A    (				self    ):
						# Initialize image_processing
						A__							:					List[str]       =	self.image_processing_class(**self.image_processor_dict    )
						# create random numpy tensors
						A__							:					Tuple       =	prepare_image_inputs(self.image_processor_tester					, equal_resolution=A__					, numpify=A__    )
						for image in image_inputs:
							self.assertIsInstance(A__					, np.ndarray    )

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A__							:					Any       =	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 						__A    (				self    ):
						# Initialize image_processing
						A__							:					int       =	self.image_processing_class(**self.image_processor_dict    )
						# create random PyTorch tensors
						A__							:					List[Any]       =	prepare_image_inputs(self.image_processor_tester					, equal_resolution=A__					, torchify=A__    )
						for image in image_inputs:
							self.assertIsInstance(A__					, torch.Tensor    )

						# Test not batched input (GLPNImageProcessor doesn't support batching)
						A__							:					Optional[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    )






import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

A_ :				Dict						 =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_ :				Optional[Any]						 =    [file for file in filepaths if file != file.lower()]
if upper_files:
	print(f'''{len(upper_files)} files contain uppercase characters:''')
	print('\n'.join(upper_files) + '\n')

A_ :				Tuple						 =    [file for file in filepaths if ' ' in file]
if space_files:
	print(f'''{len(space_files)} files contain space characters:''')
	print('\n'.join(space_files) + '\n')

A_ :				Any						 =    [file for file in filepaths if '-' in file]
if hyphen_files:
	print(f'''{len(hyphen_files)} files contain hyphen characters:''')
	print('\n'.join(hyphen_files) + '\n')

A_ :				List[str]						 =    [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(f'''{len(nodir_files)} files are not in a directory:''')
	print('\n'.join(nodir_files) + '\n')

A_ :				Any						 =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)



