




'''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


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ChineseCLIPImageProcessor




class  _snake_case		(	unittest.TestCase							):




			def __init__(					self				,							a__				,							a__=7				,							a__=3				,							a__=18				,							a__=30				,							a__=400				,							a__=True				,							a__=None				,							a__=True				,							a__=None				,							a__=True				,							a__=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				,							a__=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				,							a__=True				,							)				->				int:

										'''simple docstring'''




										snake_case_						=      size if size is not None else {"height": 224, "width": 224}
										snake_case_						=      crop_size if crop_size is not None else {"height": 18, "width": 18}
										snake_case_						=      parent
										snake_case_						=      batch_size
										snake_case_						=      num_channels
										snake_case_						=      image_size
										snake_case_						=      min_resolution
										snake_case_						=      max_resolution
										snake_case_						=      do_resize
										snake_case_						=      size
										snake_case_						=      do_center_crop
										snake_case_						=      crop_size
										snake_case_						=      do_normalize
										snake_case_						=      image_mean
										snake_case_						=      image_std
										snake_case_						=      do_convert_rgb




			def    lowerCAmelCase__				(					self							)				->				Tuple:

										'''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,
										    "do_convert_rgb": self.do_convert_rgb,
										}




			def    lowerCAmelCase__				(					self				,							a__=False				,							a__=False				,							a__=False							)				->				List[str]:

										'''simple docstring'''




										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																	snake_case_						=      []
																	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_						=      []
																	for i in range(self.batch_size							):
																								snake_case_					,      snake_case_						=      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_						=      [Image.fromarray(np.moveaxis(a__				,							0				,							-1							)							) for x in image_inputs]

										if torchify:
																	snake_case_						=      [torch.from_numpy(a__							) for x in image_inputs]

										return image_inputs




@require_torch
@require_vision
class  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      str						       =    ChineseCLIPImageProcessor if is_vision_available() else None




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      ChineseCLIPImageProcessingTester(self				,							do_center_crop=a__							)




			@property
			def    lowerCAmelCase__				(					self							)				->				Any:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(a__				,							"do_resize"							)							)
										self.assertTrue(hasattr(a__				,							"size"							)							)
										self.assertTrue(hasattr(a__				,							"do_center_crop"							)							)
										self.assertTrue(hasattr(a__				,							"center_crop"							)							)
										self.assertTrue(hasattr(a__				,							"do_normalize"							)							)
										self.assertTrue(hasattr(a__				,							"image_mean"							)							)
										self.assertTrue(hasattr(a__				,							"image_std"							)							)
										self.assertTrue(hasattr(a__				,							"do_convert_rgb"							)							)




			def    lowerCAmelCase__				(					self							)				->				List[str]:

										'''simple docstring'''




										snake_case_						=      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_						=      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    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_						=      self.image_processor_tester.prepare_inputs(equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__				,							Image.Image							)

										# Test not batched input
										snake_case_						=      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_						=      image_processing(a__				,							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    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										snake_case_						=      self.image_processor_tester.prepare_inputs(equal_resolution=a__				,							numpify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__				,							np.ndarray							)

										# Test not batched input
										snake_case_						=      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_						=      image_processing(a__				,							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    lowerCAmelCase__				(					self							)				->				str:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										snake_case_						=      self.image_processor_tester.prepare_inputs(equal_resolution=a__				,							torchify=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__				,							torch.Tensor							)

										# Test not batched input
										snake_case_						=      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_						=      image_processing(a__				,							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  _snake_case		(	lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_     :      List[Any]						       =    ChineseCLIPImageProcessor if is_vision_available() else None




			def    lowerCAmelCase__				(					self							)				->				List[Any]:

										'''simple docstring'''




										snake_case_						=      ChineseCLIPImageProcessingTester(self				,							num_channels=4				,							do_center_crop=a__							)
										snake_case_						=      3




			@property
			def    lowerCAmelCase__				(					self							)				->				Union[str, Any]:

										'''simple docstring'''




										return self.image_processor_tester.prepare_image_processor_dict()




			def    lowerCAmelCase__				(					self							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(a__				,							"do_resize"							)							)
										self.assertTrue(hasattr(a__				,							"size"							)							)
										self.assertTrue(hasattr(a__				,							"do_center_crop"							)							)
										self.assertTrue(hasattr(a__				,							"center_crop"							)							)
										self.assertTrue(hasattr(a__				,							"do_normalize"							)							)
										self.assertTrue(hasattr(a__				,							"image_mean"							)							)
										self.assertTrue(hasattr(a__				,							"image_std"							)							)
										self.assertTrue(hasattr(a__				,							"do_convert_rgb"							)							)




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										pass




			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




										snake_case_						=      self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										snake_case_						=      self.image_processor_tester.prepare_inputs(equal_resolution=a__							)
										for image in image_inputs:
																	self.assertIsInstance(a__				,							Image.Image							)

										# Test not batched input
										snake_case_						=      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_						=      image_processing(a__				,							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

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       list[list[int]]:






       """simple docstring"""







       lowercase__      			= []
       create_all_state(1  , A  , A  , []  , A       )
       return result

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A  , A  , )       ->       None:






       """simple docstring"""







       if level == 0:
              total_list.append(current_list[:]       )
              return

       for i in range(A  , total_number - level + 2       ):
              current_list.append(A       )
              create_all_state(i + 1  , A  , level - 1  , A  , A       )
              current_list.pop()

def _SCREAMING_SNAKE_CASE      (A       )       ->       None:






       """simple docstring"""







       for i in total_list:
              print(*A       )


if __name__ == "__main__":
 lowerCamelCase							:   Tuple  						=	4
 lowerCamelCase							:   Union[str, Any]  						=	2
 lowerCamelCase							:   Dict  						=	generate_all_combinations(n, k)
 print_all_state(total_list)




