



"""simple docstring"""

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:
					lowercase__					:						Optional[Any]												=			False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor




class  _UpperCAmelCase (    unittest.TestCase):

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

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






	def 		_snake_case      (  self	: List[Any]       ):
					snake_case_				:  List[str]       =			'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
					snake_case_				:  Dict       =			Image.open(requests.get(_lowerCAmelCase						,  stream=_lowerCAmelCase       ).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  _UpperCAmelCase (    _lowerCAmelCase					,  unittest.TestCase):
	_lowerCAmelCase     :    List[str]	      =	PixaStructImageProcessor if is_vision_available() else None

	def 		_snake_case      (  self	: List[str]       ):
					snake_case_				:  int       =			PixaStructImageProcessingTester(self       )

	@property
	def 		_snake_case      (  self	: Union[str, Any]       ):
					return self.image_processor_tester.prepare_image_processor_dict()

	def 		_snake_case      (  self	: Any       ):
					snake_case_				:  Dict       =			self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(_lowerCAmelCase						,  '''do_normalize'''       )       )
					self.assertTrue(hasattr(_lowerCAmelCase						,  '''do_convert_rgb'''       )       )

	def 		_snake_case      (  self	: Tuple       ):
					snake_case_				:  Optional[Any]       =			self.image_processor_tester.prepare_dummy_image()

					snake_case_				:  Optional[Any]       =			self.image_processing_class(**self.image_processor_dict       )
					snake_case_				:  Optional[Any]       =			2048

					snake_case_				:  Optional[Any]       =			image_processor(_lowerCAmelCase						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       )
					self.assertTrue(torch.allclose(inputs.flattened_patches.mean()						,  torch.tensor(0.06_06       )						,  atol=1E-3						,  rtol=1E-3       )       )

	def 		_snake_case      (  self	: Any       ):
					snake_case_				:  Optional[int]       =			self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case_				:  Optional[int]       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_lowerCAmelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,  Image.Image       )

					# 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_				:  Union[str, Any]       =			image_processor(
									    image_inputs[0]						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

									# Test batched
									snake_case_				:  str       =			image_processor(
									    _lowerCAmelCase						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,  )

	def 		_snake_case      (  self	: List[Any]       ):
					snake_case_				:  str       =			self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case_				:  Tuple       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_lowerCAmelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,  Image.Image       )

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

					snake_case_				:  str       =			True

					for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(_lowerCAmelCase       ):
													snake_case_				:  Tuple       =			image_processor(
													    image_inputs[0]						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches

									snake_case_				:  List[Any]       =			'''Hello'''

									snake_case_				:  Union[str, Any]       =			image_processor(
									    image_inputs[0]						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase						,  header_text=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

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

	def 		_snake_case      (  self	: List[str]       ):
					snake_case_				:  Dict       =			self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					snake_case_				:  List[Any]       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_lowerCAmelCase						,  numpify=_lowerCAmelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,  np.ndarray       )

					snake_case_				:  Optional[int]       =			(
					    (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_				:  int       =			image_processor(
									    image_inputs[0]						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

									# Test batched
									snake_case_				:  Any       =			image_processor(
									    _lowerCAmelCase						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,  )






	def 		_snake_case      (  self	: List[str]       ):
					snake_case_				:  List[str]       =			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=_lowerCAmelCase						,  torchify=_lowerCAmelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,  torch.Tensor       )

					# 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
					) + 2

					for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									snake_case_				:  List[Any]       =			image_processor(
									    image_inputs[0]						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

									# Test batched
									snake_case_				:  int       =			image_processor(
									    _lowerCAmelCase						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).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  _UpperCAmelCase (    _lowerCAmelCase					,  unittest.TestCase):
	_lowerCAmelCase     :    int	      =	PixaStructImageProcessor if is_vision_available() else None

	def 		_snake_case      (  self	: Dict       ):
					snake_case_				:  List[str]       =			PixaStructImageProcessingTester(self						,  num_channels=4       )
					snake_case_				:  Any       =			3

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

	def 		_snake_case      (  self	: Any       ):
					snake_case_				:  str       =			self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(_lowerCAmelCase						,  '''do_normalize'''       )       )
					self.assertTrue(hasattr(_lowerCAmelCase						,  '''do_convert_rgb'''       )       )






	def 		_snake_case      (  self	: Dict       ):
					snake_case_				:  str       =			self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					snake_case_				:  Dict       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=_lowerCAmelCase       )
					for image in image_inputs:
									self.assertIsInstance(_lowerCAmelCase						,  Image.Image       )

					# 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 - 1)
					) + 2

					for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									snake_case_				:  List[Any]       =			image_processor(
									    image_inputs[0]						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (1, max_patch, expected_hidden_dim)						,  )

									# Test batched
									snake_case_				:  Tuple       =			image_processor(
									    _lowerCAmelCase						,  return_tensors='''pt'''						,  max_patches=_lowerCAmelCase       ).flattened_patches
									self.assertEqual(
									    encoded_images.shape						,  (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)						,  )










"""simple docstring"""

def __lowercase    (							_a					):
    snake_case_				:  Optional[Any]       =			int(_a					)
    if decimal in (0, 1):  # Exit cases for the recursion
        return str(_a					)
    snake_case_,			snake_case_				:  Optional[int]       =			divmod(_a	,    2					)
    return binary_recursive(_a					) + str(_a					)




def __lowercase    (							_a					):
    snake_case_				:  Dict       =			str(_a					).strip()
    if not number:
        raise ValueError('''No input value was provided'''					)
    snake_case_				:  List[str]       =			'''-''' if number.startswith('''-'''					) else ''''''
    snake_case_				:  Optional[Any]       =			number.lstrip('''-'''					)
    if not number.isnumeric():
        raise ValueError('''Input value is not an integer'''					)
    return f"{negative}0b{binary_recursive(int(_a					)					)}"


if __name__ == "__main__":
     from doctest import testmod

     testmod()





