


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:
			_snake_case					:		List[Any]      =    False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class     a		(unittest.TestCase				):




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  int=7   ,     lowerCamelCase	:  int=3   ,     lowerCamelCase	:  List[str]=18   ,     lowerCamelCase	:  str=30   ,     lowerCamelCase	:  Tuple=400   ,     lowerCamelCase	:  List[Any]=None   ,     lowerCamelCase	:  List[Any]=True   ,     lowerCamelCase	:  Any=True   ,     lowerCamelCase	:  Optional[Any]=None   ,     )     ->   List[str]:
														__snake_case		:      int										=       size if size is not None else {'height': 20, 'width': 20}
														__snake_case		:      List[Any]										=       parent
														__snake_case		:      List[Any]										=       batch_size
														__snake_case		:      List[Any]										=       num_channels
														__snake_case		:      str										=       image_size
														__snake_case		:      Optional[Any]										=       min_resolution
														__snake_case		:      str										=       max_resolution
														__snake_case		:      List[Any]										=       size
														__snake_case		:      int										=       do_normalize
														__snake_case		:      Any										=       do_convert_rgb
														__snake_case		:      Tuple										=       [512, 1024, 2048, 4096]
														__snake_case		:      Tuple										=       patch_size if patch_size is not None else {'height': 16, 'width': 16}

							def    __snake_case (							self	:  List[Any]    )     ->   List[str]:
														return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






							def    __snake_case (							self	:  List[Any]    )     ->   int:
														__snake_case		:      List[str]										=       'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
														__snake_case		:      Any										=       Image.open(requests.get(_UpperCAmelCase   ,     stream=_UpperCAmelCase    ).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     a		(_UpperCamelCase					,    unittest.TestCase				):




							"""simple docstring"""


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

							def    __snake_case (							self	:  List[Any]    )     ->   str:
														__snake_case		:      Dict										=       PixaStructImageProcessingTester(self    )

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

							def    __snake_case (							self	:  str    )     ->   int:
														__snake_case		:      int										=       self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(_UpperCAmelCase   ,     "do_normalize"    )    )
														self.assertTrue(hasattr(_UpperCAmelCase   ,     "do_convert_rgb"    )    )

							def    __snake_case (							self	:  Dict    )     ->   List[Any]:
														__snake_case		:      List[Any]										=       self.image_processor_tester.prepare_dummy_image()

														__snake_case		:      Optional[int]										=       self.image_processing_class(**self.image_processor_dict    )
														__snake_case		:      List[str]										=       2048

														__snake_case		:      Dict										=       image_processor(_UpperCAmelCase   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    )
														self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,     torch.tensor(0.06_06    )   ,     atol=1E-3   ,     rtol=1E-3    )    )

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

														# Test not batched input
														__snake_case		:      Dict										=       (
														    (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
																					__snake_case		:      Union[str, Any]										=       image_processor(
																					    image_inputs[0]   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (1, max_patch, expected_hidden_dim)   ,     )

																					# Test batched
																					__snake_case		:      Union[str, Any]										=       image_processor(
																					    _UpperCAmelCase   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,     )

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

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

														__snake_case		:      List[str]										=       True

														for max_patch in self.image_processor_tester.max_patches:
																					# Test not batched input
																					with self.assertRaises(_UpperCAmelCase    ):
																												__snake_case		:      str										=       image_processor(
																												    image_inputs[0]   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches

																					__snake_case		:      Union[str, Any]										=       'Hello'

																					__snake_case		:      str										=       image_processor(
																					    image_inputs[0]   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase   ,     header_text=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (1, max_patch, expected_hidden_dim)   ,     )

																					# Test batched
																					__snake_case		:      Union[str, Any]										=       image_processor(
																					    _UpperCAmelCase   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase   ,     header_text=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,     )

							def    __snake_case (							self	:  int    )     ->   Dict:
														# Initialize image_processor
														__snake_case		:      Dict										=       self.image_processing_class(**self.image_processor_dict    )
														# create random numpy tensors
														__snake_case		:      Dict										=       prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=_UpperCAmelCase   ,     numpify=_UpperCAmelCase    )
														for image in image_inputs:
																					self.assertIsInstance(_UpperCAmelCase   ,     np.ndarray    )

														__snake_case		:      Any										=       (
														    (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
																					__snake_case		:      Dict										=       image_processor(
																					    image_inputs[0]   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (1, max_patch, expected_hidden_dim)   ,     )

																					# Test batched
																					__snake_case		:      str										=       image_processor(
																					    _UpperCAmelCase   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,     )






							def    __snake_case (							self	:  Union[str, Any]    )     ->   int:
														# Initialize image_processor
														__snake_case		:      Optional[int]										=       self.image_processing_class(**self.image_processor_dict    )
														# create random PyTorch tensors
														__snake_case		:      List[Any]										=       prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=_UpperCAmelCase   ,     torchify=_UpperCAmelCase    )
														for image in image_inputs:
																					self.assertIsInstance(_UpperCAmelCase   ,     torch.Tensor    )

														# Test not batched input
														__snake_case		:      Union[str, Any]										=       (
														    (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
																					__snake_case		:      Optional[int]										=       image_processor(
																					    image_inputs[0]   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (1, max_patch, expected_hidden_dim)   ,     )

																					# Test batched
																					__snake_case		:      Union[str, Any]										=       image_processor(
																					    _UpperCAmelCase   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).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     a		(_UpperCamelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  PixaStructImageProcessor if is_vision_available() else None

							def    __snake_case (							self	:  Tuple    )     ->   Any:
														__snake_case		:      Tuple										=       PixaStructImageProcessingTester(self   ,     num_channels=4    )
														__snake_case		:      Tuple										=       3

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

							def    __snake_case (							self	:  Union[str, Any]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(_UpperCAmelCase   ,     "do_normalize"    )    )
														self.assertTrue(hasattr(_UpperCAmelCase   ,     "do_convert_rgb"    )    )






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

														# Test not batched input
														__snake_case		:      int										=       (
														    (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
																					__snake_case		:      Optional[int]										=       image_processor(
																					    image_inputs[0]   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (1, max_patch, expected_hidden_dim)   ,     )

																					# Test batched
																					__snake_case		:      Optional[Any]										=       image_processor(
																					    _UpperCAmelCase   ,     return_tensors="pt"   ,     max_patches=_UpperCAmelCase    ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape   ,     (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,     )







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							if index == r:
														for j in range(__lowerCamelCase     ):
																					print(data[j]							,     end=" "     )
														print(" "     )
														return
							#  When no more elements are there to put in data[]
							if i >= n:
														return
							# current is included, put next at next location
							__snake_case		:      Union[str, Any]										=       arr[i]
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     index + 1							,     __lowerCamelCase							,     i + 1     )
							# current is excluded, replace it with
							# next (Note that i+1 is passed, but
							# index is not changed)
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     i + 1     )
							# The main function that prints all combinations
							# of size r in arr[] of size n. This function
							# mainly uses combinationUtil()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							# A temporary array to store all combination one by one
							__snake_case		:      Union[str, Any]										=       [0] * r
							# Print all combination using temporary array 'data[]'
							combination_util(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     0							,     __lowerCamelCase							,     0     )


if __name__ == "__main__":
			# Driver code to check the function above
			_snake_case					:		List[str]      =    [10, 20, 30, 40, 50]
			print_combination(arr, len(arr), 3)
			# This code is contributed by Ambuj sahu

