





'''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 			lowerCamelCase					(						unittest.TestCase  ):


					def __init__(					self	:  Dict       ,     __snake_case	:  Optional[Any]       ,     __snake_case	:  Optional[Any]=7       ,     __snake_case	:  Any=3       ,     __snake_case	:  Dict=18       ,     __snake_case	:  str=30       ,     __snake_case	:  Optional[int]=4_00       ,     __snake_case	:  str=True       ,     __snake_case	:  Union[str, Any]=32       ,     __snake_case	:  Any=True       ,     ):





											'''simple docstring'''





											_snake_case:   str  					=		parent
											_snake_case:   List[str]  					=		batch_size
											_snake_case:   Dict  					=		num_channels
											_snake_case:   List[Any]  					=		image_size
											_snake_case:   Union[str, Any]  					=		min_resolution
											_snake_case:   str  					=		max_resolution
											_snake_case:   Tuple  					=		do_resize
											_snake_case:   int  					=		size_divisor
											_snake_case:   Optional[int]  					=		do_rescale




					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											return {
											    "do_resize": self.do_resize,
											    "size_divisor": self.size_divisor,
											    "do_rescale": self.do_rescale,
											}







@require_torch
@require_vision
class 			lowerCamelCase					(						__UpperCAmelCase     ,							unittest.TestCase  ):
					_SCREAMING_SNAKE_CASE							   =    GLPNImageProcessor if is_vision_available() else None


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		GLPNImageProcessingTester(self       )


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											return self.image_processor_tester.prepare_image_processor_dict()


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											_snake_case:   Optional[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    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											pass


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random PIL images
											_snake_case:   List[str]  					=		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:   Tuple  					=		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    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random numpy tensors
											_snake_case:   Tuple  					=		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:   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       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		self.image_processing_class(**self.image_processor_dict       )
											# create random PyTorch tensors
											_snake_case:   List[str]  					=		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:   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       )












'''simple docstring'''






A   : str             =			{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


A   : Union[str, Any]             =			{value: key for key, value in encode_dict.items()}





def       lowercase_      (		lowercase__						)   ->str:
						_snake_case:   Union[str, Any]  					=		''
						for letter in word.lower():
												if letter.isalpha() or letter == " ":
																		encoded += encode_dict[letter]
												else:
																		raise Exception('encode() accepts only letters of the alphabet and spaces'						)
						return encoded





def       lowercase_      (		lowercase__						)   ->str:
						if set(lowercase__						) - {"A", "B", " "} != set():
												raise Exception('decode() accepts only \'A\', \'B\' and spaces'						)
						_snake_case:   List[Any]  					=		''
						for word in coded.split():
												while len(lowercase__						) != 0:
																		decoded += decode_dict[word[:5]]
																		_snake_case:   Optional[int]  					=		word[5:]
												decoded += " "
						return decoded.strip()


if __name__ == "__main__":
		from doctest import testmod

		testmod()





