





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:
							UpperCAmelCase__						:			Any									=False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor

class       __A     (						unittest.TestCase    ):


				def __init__(		self   , UpperCAmelCase_   , UpperCAmelCase_=7   , UpperCAmelCase_=3   , UpperCAmelCase_=18   , UpperCAmelCase_=30   , UpperCAmelCase_=400   , UpperCAmelCase_=None   , UpperCAmelCase_=True   , UpperCAmelCase_=True   , UpperCAmelCase_=None   , ):
								lowerCamelCase	 =size if size is not None else {"""height""": 20, """width""": 20}
								lowerCamelCase	 =parent
								lowerCamelCase	 =batch_size
								lowerCamelCase	 =num_channels
								lowerCamelCase	 =image_size
								lowerCamelCase	 =min_resolution
								lowerCamelCase	 =max_resolution
								lowerCamelCase	 =size
								lowerCamelCase	 =do_normalize
								lowerCamelCase	 =do_convert_rgb
								lowerCamelCase	 =[512, 1024, 2048, 4096]
								lowerCamelCase	 =patch_size if patch_size is not None else {"""height""": 16, """width""": 16}


				def _snake_case       (		self							):
								return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}


				def _snake_case       (		self							):
								lowerCamelCase	 ="""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
								lowerCamelCase	 =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       __A     (						__SCREAMING_SNAKE_CASE    ,					unittest.TestCase    ):
				__A      =				PixaStructImageProcessor if is_vision_available() else None


				def _snake_case       (		self							):
								lowerCamelCase	 =PixaStructImageProcessingTester(self							)


				@property
				def _snake_case       (		self							):
								return self.image_processor_tester.prepare_image_processor_dict()


				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								self.assertTrue(hasattr(_a   , """do_normalize"""							)							)
								self.assertTrue(hasattr(_a   , """do_convert_rgb"""							)							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processor_tester.prepare_dummy_image()

								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								lowerCamelCase	 =2048

								lowerCamelCase	 =image_processor(_a   , return_tensors="""pt"""   , max_patches=_a							)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   , torch.tensor(0.0_6_0_6							)   , atol=1E-3   , rtol=1E-3							)							)


				def _snake_case       (		self							):
								# Initialize image_processor
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								lowerCamelCase	 =prepare_image_inputs(self.image_processor_tester   , equal_resolution=_a							)
								for image in image_inputs:
												self.assertIsInstance(_a   , Image.Image							)

								# Test not batched input
								lowerCamelCase	 =(
								    (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
												lowerCamelCase	 =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
												lowerCamelCase	 =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 _snake_case       (		self							):
								# Initialize image_processor
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								lowerCamelCase	 =prepare_image_inputs(self.image_processor_tester   , equal_resolution=_a							)
								for image in image_inputs:
												self.assertIsInstance(_a   , Image.Image							)

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

								lowerCamelCase	 =True

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

												lowerCamelCase	 ="""Hello"""

												lowerCamelCase	 =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
												lowerCamelCase	 =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 _snake_case       (		self							):
								# Initialize image_processor
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random numpy tensors
								lowerCamelCase	 =prepare_image_inputs(self.image_processor_tester   , equal_resolution=_a   , numpify=_a							)
								for image in image_inputs:
												self.assertIsInstance(_a   , np.ndarray							)

								lowerCamelCase	 =(
								    (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
												lowerCamelCase	 =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
												lowerCamelCase	 =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 _snake_case       (		self							):
								# Initialize image_processor
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random PyTorch tensors
								lowerCamelCase	 =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
								lowerCamelCase	 =(
								    (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
												lowerCamelCase	 =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
												lowerCamelCase	 =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       __A     (						__SCREAMING_SNAKE_CASE    ,					unittest.TestCase    ):
				__A      =				PixaStructImageProcessor if is_vision_available() else None


				def _snake_case       (		self							):
								lowerCamelCase	 =PixaStructImageProcessingTester(self   , num_channels=4							)
								lowerCamelCase	 =3


				@property
				def _snake_case       (		self							):
								return self.image_processor_tester.prepare_image_processor_dict()


				def _snake_case       (		self							):
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								self.assertTrue(hasattr(_a   , """do_normalize"""							)							)
								self.assertTrue(hasattr(_a   , """do_convert_rgb"""							)							)


				def _snake_case       (		self							):
								# Initialize image_processor
								lowerCamelCase	 =self.image_processing_class(**self.image_processor_dict							)
								# create random PIL images
								lowerCamelCase	 =prepare_image_inputs(self.image_processor_tester   , equal_resolution=_a							)
								for image in image_inputs:
												self.assertIsInstance(_a   , Image.Image							)

								# Test not batched input
								lowerCamelCase	 =(
								    (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
												lowerCamelCase	 =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
												lowerCamelCase	 =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      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =[0 for i in range(r + 1				)]
				# nc0 = 1
				lowerCamelCase	 =1
				for i in range(1			,      n + 1				):
								# to compute current row from previous row.
								lowerCamelCase	 =min(_UpperCAmelCase			,      _UpperCAmelCase				)
								while j > 0:
												c[j] += c[j - 1]
												j -= 1
				return c[r]




print(binomial_coefficient(n=10, r=5))





