



'''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 DeiTImageProcessor






class 							lowercase__			(							unittest.TestCase     ):





	def __init__(				self      :       List[str]				,lowerCamelCase__      :       Optional[Any]				,lowerCamelCase__      :       List[str]=7				,lowerCamelCase__      :       Dict=3				,lowerCamelCase__      :       Any=18				,lowerCamelCase__      :       int=30				,lowerCamelCase__      :       Any=400				,lowerCamelCase__      :       Optional[int]=True				,lowerCamelCase__      :       Dict=None				,lowerCamelCase__      :       Optional[int]=True				,lowerCamelCase__      :       str=None				,lowerCamelCase__      :       int=True				,lowerCamelCase__      :       Dict=[0.5, 0.5, 0.5]				,lowerCamelCase__      :       int=[0.5, 0.5, 0.5]				,):







							'''simple docstring'''
							_UpperCamelCase      :	Union[str, Any]				       =     size if size is not None else {'height': 20, 'width': 20}
							_UpperCamelCase      :	Dict				       =     crop_size if crop_size is not None else {'height': 18, 'width': 18}

							_UpperCamelCase      :	str				       =     parent
							_UpperCamelCase      :	str				       =     batch_size
							_UpperCamelCase      :	Union[str, Any]				       =     num_channels
							_UpperCamelCase      :	List[Any]				       =     image_size
							_UpperCamelCase      :	Union[str, Any]				       =     min_resolution
							_UpperCamelCase      :	Optional[Any]				       =     max_resolution
							_UpperCamelCase      :	Optional[Any]				       =     do_resize
							_UpperCamelCase      :	str				       =     size
							_UpperCamelCase      :	List[Any]				       =     do_center_crop
							_UpperCamelCase      :	str				       =     crop_size
							_UpperCamelCase      :	Optional[Any]				       =     do_normalize
							_UpperCamelCase      :	Optional[Any]				       =     image_mean
							_UpperCamelCase      :	Dict				       =     image_std





	def 			UpperCamelCase_						(				self      :       List[Any]				):







							'''simple docstring'''
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_center_crop": self.do_center_crop,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}






@require_torch
@require_vision
class 							lowercase__			(							lowercase		,      unittest.TestCase     ):
	lowercase__						       =    DeiTImageProcessor if is_vision_available() else None
	lowercase__						       =    True





	def 			UpperCamelCase_						(				self      :       Optional[Any]				):







							'''simple docstring'''
							_UpperCamelCase      :	List[Any]				       =     DeiTImageProcessingTester(self				)





	@property
	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def 			UpperCamelCase_						(				self      :       Optional[int]				):







							'''simple docstring'''
							_UpperCamelCase      :	str				       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(lowerCamelCase__				,'do_resize'				)				)
							self.assertTrue(hasattr(lowerCamelCase__				,'size'				)				)
							self.assertTrue(hasattr(lowerCamelCase__				,'do_center_crop'				)				)
							self.assertTrue(hasattr(lowerCamelCase__				,'center_crop'				)				)
							self.assertTrue(hasattr(lowerCamelCase__				,'do_normalize'				)				)
							self.assertTrue(hasattr(lowerCamelCase__				,'image_mean'				)				)
							self.assertTrue(hasattr(lowerCamelCase__				,'image_std'				)				)





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







							'''simple docstring'''
							_UpperCamelCase      :	Any				       =     self.image_processing_class.from_dict(self.image_processor_dict				)
							self.assertEqual(image_processor.size				,{'height': 20, 'width': 20}				)
							self.assertEqual(image_processor.crop_size				,{'height': 18, 'width': 18}				)

							_UpperCamelCase      :	Optional[int]				       =     self.image_processing_class.from_dict(self.image_processor_dict				,size=42				,crop_size=84				)
							self.assertEqual(image_processor.size				,{'height': 42, 'width': 42}				)
							self.assertEqual(image_processor.crop_size				,{'height': 84, 'width': 84}				)





	def 			UpperCamelCase_						(				self      :       Tuple				):







							'''simple docstring'''
							pass





	def 			UpperCamelCase_						(				self      :       List[Any]				):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase      :	Union[str, Any]				       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							_UpperCamelCase      :	Tuple				       =     prepare_image_inputs(self.image_processor_tester				,equal_resolution=lowerCamelCase__				)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,Image.Image				)

							# Test not batched input
							_UpperCamelCase      :	List[str]				       =     image_processing(image_inputs[0]				,return_tensors='pt'				).pixel_values
							self.assertEqual(
							    encoded_images.shape				,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,)

							# Test batched
							_UpperCamelCase      :	Optional[Any]				       =     image_processing(lowerCamelCase__				,return_tensors='pt'				).pixel_values
							self.assertEqual(
							    encoded_images.shape				,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,)





	def 			UpperCamelCase_						(				self      :       Dict				):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase      :	Any				       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							_UpperCamelCase      :	List[Any]				       =     prepare_image_inputs(self.image_processor_tester				,equal_resolution=lowerCamelCase__				,numpify=lowerCamelCase__				)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,np.ndarray				)

							# Test not batched input
							_UpperCamelCase      :	Optional[int]				       =     image_processing(image_inputs[0]				,return_tensors='pt'				).pixel_values
							self.assertEqual(
							    encoded_images.shape				,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,)

							# Test batched
							_UpperCamelCase      :	int				       =     image_processing(lowerCamelCase__				,return_tensors='pt'				).pixel_values
							self.assertEqual(
							    encoded_images.shape				,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,)





	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							# Initialize image_processing
							_UpperCamelCase      :	str				       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							_UpperCamelCase      :	Dict				       =     prepare_image_inputs(self.image_processor_tester				,equal_resolution=lowerCamelCase__				,torchify=lowerCamelCase__				)
							for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,torch.Tensor				)

							# Test not batched input
							_UpperCamelCase      :	Tuple				       =     image_processing(image_inputs[0]				,return_tensors='pt'				).pixel_values
							self.assertEqual(
							    encoded_images.shape				,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,)

							# Test batched
							_UpperCamelCase      :	int				       =     image_processing(lowerCamelCase__				,return_tensors='pt'				).pixel_values
							self.assertEqual(
							    encoded_images.shape				,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['height'],
							        self.image_processor_tester.crop_size['width'],
							    )				,)







'''simple docstring'''
def A__					(  UpperCAmelCase_							):
						_UpperCamelCase      :	Optional[Any]				       =     word.upper()
						_UpperCamelCase      :	Optional[Any]				       =     0
						_UpperCamelCase      :	Dict				       =     {}
						_UpperCamelCase      :	List[str]				       =     []

						for letter in word:
												if letter not in letter_nums:
																		_UpperCamelCase      :	int				       =     str(UpperCAmelCase_							)
																		next_num += 1
												word_pattern.append(letter_nums[letter]							)
						return ".".join(UpperCAmelCase_							)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_				:			List[Any]						 =						time.time()
					with open('dictionary.txt') as in_file:
										snake_case_				:			Any						 =						in_file.read().splitlines()

					snake_case_				:			dict						 =						{}
					for word in word_list:
										snake_case_				:			Optional[int]						 =						get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_				:			Any						 =						[word]

					with open('word_patterns.txt', 'w') as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_				:			List[str]						 =						round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.


