


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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import ChineseCLIPImageProcessor

class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def __init__(				self			:    List[Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    int=7       ,      lowerCamelCase			:    Dict=3       ,      lowerCamelCase			:    Optional[Any]=18       ,      lowerCamelCase			:    Optional[Any]=30       ,      lowerCamelCase			:    Any=400       ,      lowerCamelCase			:    int=True       ,      lowerCamelCase			:    Tuple=None       ,      lowerCamelCase			:    List[str]=True       ,      lowerCamelCase			:    Optional[int]=None       ,      lowerCamelCase			:    Union[str, Any]=True       ,      lowerCamelCase			:    str=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,      lowerCamelCase			:    Optional[int]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,      lowerCamelCase			:    Optional[int]=True       ,      )      ->       List[Any]:
														__snake_case						:     List[str]									=  size if size is not None else {"height": 224, "width": 224}
														__snake_case						:     List[str]									=  crop_size if crop_size is not None else {"height": 18, "width": 18}
														__snake_case						:     Tuple									=  parent
														__snake_case						:     List[str]									=  batch_size
														__snake_case						:     Union[str, Any]									=  num_channels
														__snake_case						:     Union[str, Any]									=  image_size
														__snake_case						:     Union[str, Any]									=  min_resolution
														__snake_case						:     str									=  max_resolution
														__snake_case						:     List[Any]									=  do_resize
														__snake_case						:     Optional[int]									=  size
														__snake_case						:     int									=  do_center_crop
														__snake_case						:     Dict									=  crop_size
														__snake_case						:     List[Any]									=  do_normalize
														__snake_case						:     str									=  image_mean
														__snake_case						:     Optional[int]									=  image_std
														__snake_case						:     Union[str, Any]									=  do_convert_rgb

							def        __snake_case   (				self			:    str							)      ->       Optional[int]:
														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,
														    "do_convert_rgb": self.do_convert_rgb,
														}






							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    List[Any]=False       ,      lowerCamelCase			:    Optional[Any]=False       ,      lowerCamelCase			:    List[str]=False							)      ->       Optional[int]:
														assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

														if equal_resolution:
																					__snake_case						:     List[str]									=  []
																					for i in range(self.batch_size							):
																												image_inputs.append(
																												    np.random.randint(
																												        255       ,      size=(self.num_channels, self.max_resolution, self.max_resolution)       ,      dtype=np.uinta							)							)
														else:
																					__snake_case						:     List[str]									=  []
																					for i in range(self.batch_size							):
																												__snake_case					,     __snake_case						:     Tuple									=  np.random.choice(np.arange(self.min_resolution       ,      self.max_resolution							)       ,      2							)
																												image_inputs.append(np.random.randint(255       ,      size=(self.num_channels, width, height)       ,      dtype=np.uinta							)							)

														if not numpify and not torchify:
																					# PIL expects the channel dimension as last dimension
																					__snake_case						:     str									=  [Image.fromarray(np.moveaxis(lowerCamelCase       ,      0       ,      -1							)							) for x in image_inputs]

														if torchify:
																					__snake_case						:     Dict									=  [torch.from_numpy(lowerCamelCase							) for x in image_inputs]

														return image_inputs

@require_torch
@require_vision
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Tuple	    =    ChineseCLIPImageProcessor if is_vision_available() else None

							def        __snake_case   (				self			:    int							)      ->       str:
														__snake_case						:     Optional[Any]									=  ChineseCLIPImageProcessingTester(self       ,      do_center_crop=lowerCamelCase							)

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

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Optional[Any]:
														__snake_case						:     Union[str, Any]									=  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"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_convert_rgb"							)							)

							def        __snake_case   (				self			:    List[Any]							)      ->       Dict:
														__snake_case						:     Optional[Any]									=  self.image_processing_class.from_dict(self.image_processor_dict							)
														self.assertEqual(image_processor.size       ,      {"height": 224, "width": 224}							)
														self.assertEqual(image_processor.crop_size       ,      {"height": 18, "width": 18}							)

														__snake_case						:     Optional[Any]									=  self.image_processing_class.from_dict(self.image_processor_dict       ,      size=42       ,      crop_size=84							)
														self.assertEqual(image_processor.size       ,      {"shortest_edge": 42}							)
														self.assertEqual(image_processor.crop_size       ,      {"height": 84, "width": 84}							)

							def        __snake_case   (				self			:    str							)      ->       int:
														pass

							def        __snake_case   (				self			:    Optional[int]							)      ->       Any:
														# Initialize image_processing
														__snake_case						:     Union[str, Any]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PIL images
														__snake_case						:     List[Any]									=  self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      Image.Image							)

														# Test not batched input
														__snake_case						:     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
														__snake_case						:     Dict									=  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        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[int]:
														# Initialize image_processing
														__snake_case						:     Optional[int]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random numpy tensors
														__snake_case						:     Any									=  self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase       ,      numpify=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      np.ndarray							)

														# Test not batched input
														__snake_case						:     Any									=  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
														__snake_case						:     List[str]									=  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        __snake_case   (				self			:    Any							)      ->       Dict:
														# Initialize image_processing
														__snake_case						:     Optional[int]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PyTorch tensors
														__snake_case						:     str									=  self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase       ,      torchify=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      torch.Tensor							)

														# Test not batched input
														__snake_case						:     Union[str, Any]									=  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
														__snake_case						:     Dict									=  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"],
														    )       ,      )

@require_torch
@require_vision
class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    ChineseCLIPImageProcessor if is_vision_available() else None

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														__snake_case						:     Optional[int]									=  ChineseCLIPImageProcessingTester(self       ,      num_channels=4       ,      do_center_crop=lowerCamelCase							)
														__snake_case						:     Any									=  3

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

							def        __snake_case   (				self			:    List[str]							)      ->       Union[str, Any]:
														__snake_case						:     Tuple									=  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"							)							)
														self.assertTrue(hasattr(lowerCamelCase       ,      "do_convert_rgb"							)							)

							def        __snake_case   (				self			:    int							)      ->       Tuple:
														pass






							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														# Initialize image_processing
														__snake_case						:     List[Any]									=  self.image_processing_class(**self.image_processor_dict							)
														# create random PIL images
														__snake_case						:     Union[str, Any]									=  self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase							)
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase       ,      Image.Image							)

														# Test not batched input
														__snake_case						:     Union[str, Any]									=  image_processing(image_inputs[0]       ,      return_tensors="pt"							).pixel_values
														self.assertEqual(
														    encoded_images.shape       ,      (
														        1,
														        self.expected_encoded_image_num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )       ,      )

														# Test batched
														__snake_case						:     List[Any]									=  image_processing(lowerCamelCase       ,      return_tensors="pt"							).pixel_values
														self.assertEqual(
														    encoded_images.shape       ,      (
														        self.image_processor_tester.batch_size,
														        self.expected_encoded_image_num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )       ,      )







'''simple docstring'''







import numpy as np

lowercase      :   str      =       [
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					_a :





     '''simple docstring'''



     def __init__(				self							)      ->				None:
          snake_case			:      Dict        =			np.array(__a							)







     def 			snake_case_      (				self							,__a							)      ->				np.ndarray:
          snake_case ,		snake_case			:      Optional[Any]        =			np.where(letter == self.SQUARE							)
          snake_case			:      List[Any]        =			np.concatenate([indexa + 1, indexa + 1]							)
          return indexes







     def 			snake_case_      (				self							,__a							,__a							)      ->				str:
          snake_case			:      List[Any]        =			self.SQUARE[indexa - 1, indexa - 1]
          return letter







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      List[Any]        =			message.lower()
          snake_case			:      Dict        =			message.replace(""" """							,""""""							)
          snake_case			:      List[str]        =			message.replace("""j"""							,"""i"""							)

          snake_case			:      Optional[int]        =			np.empty((2, len(__a							))							)
          for letter_index in range(len(__a							)							):
               snake_case			:      Tuple        =			self.letter_to_numbers(message[letter_index]							)

               snake_case			:      Optional[Any]        =			numbers[0]
               snake_case			:      List[str]        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape(2 * len(__a							)							)
          snake_case			:      List[Any]        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      List[str]        =			int(second_step[numbers_index * 2]							)
               snake_case			:      List[Any]        =			int(second_step[(numbers_index * 2) + 1]							)
               snake_case			:      str        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      Any        =			encoded_message + letter

          return encoded_message







     def 			snake_case_      (				self							,__a							)      ->				str:
          snake_case			:      Any        =			message.lower()
          message.replace(""" """							,""""""							)
          snake_case			:      Dict        =			np.empty(2 * len(__a							)							)
          for letter_index in range(len(__a							)							):
               snake_case			:      str        =			self.letter_to_numbers(message[letter_index]							)
               snake_case			:      Any        =			numbers[0]
               snake_case			:      int        =			numbers[1]

          snake_case			:      List[str]        =			first_step.reshape((2, len(__a							))							)
          snake_case			:      Dict        =			""""""
          for numbers_index in range(len(__a							)							):
               snake_case			:      Optional[Any]        =			int(second_step[0, numbers_index]							)
               snake_case			:      Optional[int]        =			int(second_step[1, numbers_index]							)
               snake_case			:      Tuple        =			self.numbers_to_letter(__a							,__a							)
               snake_case			:      int        =			decoded_message + letter

          return decoded_message



