






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 					_snake_case						(			unittest.TestCase ):






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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return {
					    "do_resize": self.do_resize,
					    "size_divisor": self.size_divisor,
					    "do_rescale": self.do_rescale,
					}




@require_torch
@require_vision
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   GLPNImageProcessor if is_vision_available() else None






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[Any]         =   GLPNImageProcessingTester(self       )






	@property
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					return self.image_processor_tester.prepare_image_processor_dict()






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Any         =   self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(UpperCamelCase					,"do_resize"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"size_divisor"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"resample"       )       )
					self.assertTrue(hasattr(UpperCamelCase					,"do_rescale"       )       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					pass






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					# Initialize image_processing
					snake_case__      :int         =   self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case__      :Tuple         =   prepare_image_inputs(self.image_processor_tester					,equal_resolution=UpperCamelCase       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase					,Image.Image       )

					# Test not batched input (GLPNImageProcessor doesn't support batching)
					snake_case__      :Optional[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    lowerCAmelCase_							(		self       )						->    int:
					# Initialize image_processing
					snake_case__      :Any         =   self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					snake_case__      :Union[str, Any]         =   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 (GLPNImageProcessor doesn't support batching)
					snake_case__      :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    lowerCAmelCase_							(		self       )						->    str:
					# Initialize image_processing
					snake_case__      :List[str]         =   self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					snake_case__      :Tuple         =   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 (GLPNImageProcessor doesn't support batching)
					snake_case__      :List[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       )







import os

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

__UpperCAmelCase							:   List[Any]							=  list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

__UpperCAmelCase							:   int							=  [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")

__UpperCAmelCase							:   Any							=  [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")

__UpperCAmelCase							:   str							=  [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")

__UpperCAmelCase							:   Dict							=  [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")

__UpperCAmelCase							:   int							=  len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)