






import unittest

import numpy as np
import requests

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

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				__A      =				False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor





class 						SCREAMING_SNAKE_CASE				( unittest.TestCase					):





							"""simple docstring"""

							def __init__(  self:   Tuple		,     __A:   str		,     __A:   Dict=7		,     __A:   Tuple=3		,     __A:   Dict=18		,     __A:   Dict=30		,     __A:   Tuple=4_00		,     __A:   Optional[int]=None		,     __A:   List[Any]=True		,     __A:   int=True		,     __A:   Optional[int]=None		,     )    ->				str:
												_A								= size if size is not None else {'''height''': 20, '''width''': 20}
												_A								= parent
												_A								= batch_size
												_A								= num_channels
												_A								= image_size
												_A								= min_resolution
												_A								= max_resolution
												_A								= size
												_A								= do_normalize
												_A								= do_convert_rgb
												_A								= [5_12, 10_24, 20_48, 40_96]
												_A								= patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}

							def       __A (  self:   Tuple   )    ->				Optional[Any]:
												return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}



							def       __A (  self:   str   )    ->				List[str]:
												_A								= '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
												_A								= Image.open(requests.get(__A		,     stream=__A   ).raw   ).convert('''RGB'''   )
												return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."  ,	)
@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					PixaStructImageProcessor if is_vision_available() else None

							def       __A (  self:   Optional[int]   )    ->				Any:
												_A								= PixaStructImageProcessingTester(self   )

							@property
							def       __A (  self:   List[Any]   )    ->				str:
												return self.image_processor_tester.prepare_image_processor_dict()

							def       __A (  self:   Optional[int]   )    ->				Union[str, Any]:
												_A								= self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(__A		,     '''do_normalize'''   )   )
												self.assertTrue(hasattr(__A		,     '''do_convert_rgb'''   )   )

							def       __A (  self:   Any   )    ->				Union[str, Any]:
												_A								= self.image_processor_tester.prepare_dummy_image()

												_A								= self.image_processing_class(**self.image_processor_dict   )
												_A								= 20_48

												_A								= image_processor(__A		,     return_tensors='''pt'''		,     max_patches=__A   )
												self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,     torch.tensor(0.0_606   )		,     atol=1e-3		,     rtol=1e-3   )   )

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     Image.Image   )

												# Test not batched input
												_A								= (
												    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
												    * self.image_processor_tester.num_channels
												) + 2

												for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	_A								= image_processor(
																	    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

																	# Test batched
																	_A								= image_processor(
																	    __A		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )

							def       __A (  self:   Any   )    ->				Tuple:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     Image.Image   )

												# Test not batched input
												_A								= (
												    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
												    * self.image_processor_tester.num_channels
												) + 2

												_A								= True

												for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	with self.assertRaises(__A   ):
																						_A								= image_processor(
																						    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches

																	_A								= '''Hello'''

																	_A								= image_processor(
																	    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A		,     header_text=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

																	# Test batched
																	_A								= image_processor(
																	    __A		,     return_tensors='''pt'''		,     max_patches=__A		,     header_text=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )

							def       __A (  self:   Optional[int]   )    ->				Any:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random numpy tensors
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A		,     numpify=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     np.ndarray   )

												_A								= (
												    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
												    * self.image_processor_tester.num_channels
												) + 2

												for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	_A								= image_processor(
																	    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

																	# Test batched
																	_A								= image_processor(
																	    __A		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )



							def       __A (  self:   Tuple   )    ->				str:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PyTorch tensors
												_A								= 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
												_A								= (
												    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
												    * self.image_processor_tester.num_channels
												) + 2

												for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	_A								= image_processor(
																	    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

																	# Test batched
																	_A								= image_processor(
																	    __A		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."  ,	)
@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					PixaStructImageProcessor if is_vision_available() else None

							def       __A (  self:   Optional[int]   )    ->				List[str]:
												_A								= PixaStructImageProcessingTester(self		,     num_channels=4   )
												_A								= 3

							@property
							def       __A (  self:   List[Any]   )    ->				str:
												return self.image_processor_tester.prepare_image_processor_dict()

							def       __A (  self:   str   )    ->				Dict:
												_A								= self.image_processing_class(**self.image_processor_dict   )
												self.assertTrue(hasattr(__A		,     '''do_normalize'''   )   )
												self.assertTrue(hasattr(__A		,     '''do_convert_rgb'''   )   )



							def       __A (  self:   List[Any]   )    ->				str:
												# Initialize image_processor
												_A								= self.image_processing_class(**self.image_processor_dict   )
												# create random PIL images
												_A								= prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__A   )
												for image in image_inputs:
																	self.assertIsInstance(__A		,     Image.Image   )

												# Test not batched input
												_A								= (
												    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
												    * (self.image_processor_tester.num_channels - 1)
												) + 2

												for max_patch in self.image_processor_tester.max_patches:
																	# Test not batched input
																	_A								= image_processor(
																	    image_inputs[0]		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (1, max_patch, expected_hidden_dim)		,     )

																	# Test batched
																	_A								= image_processor(
																	    __A		,     return_tensors='''pt'''		,     max_patches=__A   ).flattened_patches
																	self.assertEqual(
																	    encoded_images.shape		,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)		,     )











import itertools
import string
from collections.abc import Generator, Iterable




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= iter(_lowercase )
					while True:
										_A								= tuple(itertools.islice(_lowercase					,			_lowercase ) )
										if not chunk:
															return
										yield chunk




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= ''''''.join([c.upper() for c in dirty if c in string.ascii_letters] )
					_A								= ''''''

					if len(_lowercase ) < 2:
										return dirty

					for i in range(len(_lowercase ) - 1 ):
										clean += dirty[i]

										if dirty[i] == dirty[i + 1]:
															clean += "X"

					clean += dirty[-1]

					if len(_lowercase ) & 1:
										clean += "X"

					return clean




def      __A					(     _lowercase ):

					'''simple docstring'''







					_A								= '''ABCDEFGHIKLMNOPQRSTUVWXYZ'''
					# we're using a list instead of a '2d' array because it makes the math
					# for setting up the table and doing the actual encoding/decoding simpler
					_A								= []

					# copy key chars into the table if they are in `alphabet` ignoring duplicates
					for char in key.upper():
										if char not in table and char in alphabet:
															table.append(_lowercase )

    # fill the rest of the table in with the remaining alphabet chars
					for char in alphabet:
										if char not in table:
															table.append(_lowercase )

					return table




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= prepare_input(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
															ciphertext += table[rowa * 5 + (cola + 1) % 5]
										elif cola == cola:
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
															ciphertext += table[((rowa + 1) % 5) * 5 + cola]
										else:  # rectangle
															ciphertext += table[rowa * 5 + cola]
															ciphertext += table[rowa * 5 + cola]

					return ciphertext




def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= generate_table(_lowercase )
					_A								= ''''''

					# https://en.wikipedia.org/wiki/Playfair_cipher#Description
					for chara, chara in chunker(_lowercase					,			2 ):
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )
										_A							,_A								= divmod(table.index(_lowercase )					,			5 )

										if rowa == rowa:
															plaintext += table[rowa * 5 + (cola - 1) % 5]
															plaintext += table[rowa * 5 + (cola - 1) % 5]
										elif cola == cola:
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
															plaintext += table[((rowa - 1) % 5) * 5 + cola]
										else:  # rectangle
															plaintext += table[rowa * 5 + cola]
															plaintext += table[rowa * 5 + cola]

					return plaintext



