


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'''






from __future__ import annotations

from typing import Any




class  _A :

		def __init__(   self					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   float = 0			)					->    None:


					"""simple docstring"""

					__snake_case      ,     __snake_case					:      Optional[Any]							      =     row, column
					__snake_case					:      Dict							      =     [[default_value for c in range(__magic_name__			)] for r in range(__magic_name__			)]

		def __str__(   self					:   List[Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     f'''Matrix consist of {self.row} rows and {self.column} columns\n'''

					# Make string identifier
					__snake_case					:      Optional[int]							      =     0
					for row_vector in self.array:
								for obj in row_vector:
											__snake_case					:      Optional[int]							      =     max(__magic_name__  ,      len(str(__magic_name__			)			)			)
					__snake_case					:      str							      =     f'''%{max_element_length}s'''

					# Make string and return
					def single_line(__magic_name__					:   list[float]			) -> str:
								nonlocal string_format_identifier
								__snake_case					:      Union[str, Any]							      =     """["""
								line += ", ".join(string_format_identifier % (obj,) for obj in row_vector			)
								line += "]"
								return line

					s += "\n".join(single_line(__magic_name__			) for row_vector in self.array			)
					return s

		def __repr__(   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					return str(self			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   tuple[int, int]			)					->    bool:


					"""simple docstring"""

					if not (isinstance(__magic_name__  ,      (list, tuple)			) and len(__magic_name__			) == 2):
								return False
					elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
								return False
					else:
								return True

		def __getitem__(   self					:   int  ,      __magic_name__					:   tuple[int, int]			)					->    Any:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					return self.array[loc[0]][loc[1]]

		def __setitem__(   self					:   List[str]  ,      __magic_name__					:   tuple[int, int]  ,      __magic_name__					:   float			)					->    None:


					"""simple docstring"""

					assert self.validate_indicies(__magic_name__			)
					__snake_case					:      Optional[int]							      =     value

		def __add__(   self					:   Any  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == another.row and self.column == another.column

					# Add
					__snake_case					:      Union[str, Any]							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     self[r, c] + another[r, c]
					return result

		def __neg__(   self					:   Tuple			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Matrix(self.row  ,      self.column			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      List[Any]							      =     -self[r, c]
					return result

		def __sub__(   self					:   Optional[int]  ,      __magic_name__					:   Matrix			)					->    Matrix:


					"""simple docstring"""

					return self + (-another)

		def __mul__(   self					:   List[Any]  ,      __magic_name__					:   int | float | Matrix			)					->    Matrix:


					"""simple docstring"""

					if isinstance(__magic_name__  ,      (int, float)			):  # Scalar multiplication
								__snake_case					:      Optional[int]							      =     Matrix(self.row  ,      self.column			)
								for r in range(self.row			):
											for c in range(self.column			):
														__snake_case					:      Tuple							      =     self[r, c] * another
								return result
					elif isinstance(__magic_name__  ,      __magic_name__			):  # Matrix multiplication
								assert self.column == another.row
								__snake_case					:      Dict							      =     Matrix(self.row  ,      another.column			)
								for r in range(self.row			):
											for c in range(another.column			):
														for i in range(self.column			):
																	result[r, c] += self[r, i] * another[i, c]
								return result
					else:
								__snake_case					:      Optional[int]							      =     f'''Unsupported type given for another ({type(__magic_name__			)})'''
								raise TypeError(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    Matrix:


					"""simple docstring"""

					__snake_case					:      Any							      =     Matrix(self.column  ,      self.row			)
					for r in range(self.row			):
								for c in range(self.column			):
											__snake_case					:      str							      =     self[r, c]
					return result

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Matrix  ,      __magic_name__					:   Matrix			)					->    Any:


					"""simple docstring"""

					assert isinstance(__magic_name__  ,      __magic_name__			) and isinstance(__magic_name__  ,      __magic_name__			)
					assert self.row == self.column == u.row == v.row  # u, v should be column vector
					assert u.column == v.column == 1  # u, v should be column vector

					# Calculate
					__snake_case					:      List[str]							      =     v.transpose()
					__snake_case					:      Tuple							      =     (v_t * self * u)[0, 0] + 1
					if numerator_factor == 0:
								return None  # It's not invertable
					return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				__snake_case					:      Tuple							      =     Matrix(3    ,	3    ,	0 )
				for i in range(3 ):
							__snake_case					:      Any							      =     1
				print(F'''a^(-1) is {ainv}''' )
				# u, v
				__snake_case					:      Dict							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Union[str, Any]							      =     1, 2, -3
				__snake_case					:      str							      =     Matrix(3    ,	1    ,	0 )
				__snake_case      ,     __snake_case      ,     __snake_case					:      Tuple							      =     4, -2, 5
				print(F'''u is {u}''' )
				print(F'''v is {v}''' )
				print(F'''uv^T is {u * v.transpose()}''' )
				# Sherman Morrison
				print(F'''(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowerCamelCase    ,	_lowerCamelCase )}''' )







	def 					_a						(       ) ->				None:



				"""simple docstring"""


				import doctest

				doctest.testmod()

	testa()


