




'''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:
   A     =False

if is_vision_available():
   from PIL import Image

   from transformers import PixaStructImageProcessor


class 			_a  (	unittest.TestCase ):
       def __init__(	self						:	Optional[int]   ,				lowercase						:	Any   ,				lowercase						:	List[Any]=7   ,				lowercase						:	Dict=3   ,				lowercase						:	Optional[Any]=18   ,				lowercase						:	Dict=30   ,				lowercase						:	Optional[int]=400   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Optional[int]=True   ,				lowercase						:	List[Any]=None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		size if size is not None else {'''height''': 20, '''width''': 20}
           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		image_size
           UpperCAmelCase			       =		min_resolution
           UpperCAmelCase			       =		max_resolution
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		do_convert_rgb
           UpperCAmelCase			       =		[512, 1_024, 2_048, 4_096]
           UpperCAmelCase			       =		patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







       def     A      (	self						:	List[str]				):



           '''simple docstring'''

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



           '''simple docstring'''

           UpperCAmelCase			       =		PixaStructImageProcessingTester(self				)
       @property
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(lowercase   ,				'''do_normalize'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''do_convert_rgb'''				)				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processor_tester.prepare_dummy_image()

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           UpperCAmelCase			       =		2_048

           UpperCAmelCase			       =		image_processor(lowercase   ,				return_tensors='''pt'''   ,				max_patches=lowercase				)
           self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,				torch.tensor(0.0606				)   ,				atol=1E-3   ,				rtol=1E-3				)				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				Image.Image				)

           # Test not batched input
           UpperCAmelCase			       =		(
               (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
               UpperCAmelCase			       =		image_processor(
                   image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

               # Test batched
               UpperCAmelCase			       =		image_processor(
                   lowercase   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				Image.Image				)

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

           UpperCAmelCase			       =		True

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

               UpperCAmelCase			       =		'''Hello'''

               UpperCAmelCase			       =		image_processor(
                   image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=lowercase   ,				header_text=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

               # Test batched
               UpperCAmelCase			       =		image_processor(
                   lowercase   ,				return_tensors='''pt'''   ,				max_patches=lowercase   ,				header_text=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

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

           UpperCAmelCase			       =		(
               (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
               UpperCAmelCase			       =		image_processor(
                   image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

               # Test batched
               UpperCAmelCase			       =		image_processor(
                   lowercase   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)







       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

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

           # Test not batched input
           UpperCAmelCase			       =		(
               (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
               UpperCAmelCase			       =		image_processor(
                   image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

               # Test batched
               UpperCAmelCase			       =		image_processor(
                   lowercase   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).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  (	__a							,							unittest.TestCase ):
       __a				:     Tuple   				=					PixaStructImageProcessor if is_vision_available() else None
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		PixaStructImageProcessingTester(self   ,				num_channels=4				)
           UpperCAmelCase			       =		3
       @property
       def     A      (	self						:	int				):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(lowercase   ,				'''do_normalize'''				)				)
           self.assertTrue(hasattr(lowercase   ,				'''do_convert_rgb'''				)				)







       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.image_processing_class(**self.image_processor_dict				)
           # create random PIL images
           UpperCAmelCase			       =		prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowercase				)
           for image in image_inputs:
               self.assertIsInstance(lowercase   ,				Image.Image				)

           # Test not batched input
           UpperCAmelCase			       =		(
               (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
               UpperCAmelCase			       =		image_processor(
                   image_inputs[0]   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(1, max_patch, expected_hidden_dim)   ,				)

               # Test batched
               UpperCAmelCase			       =		image_processor(
                   lowercase   ,				return_tensors='''pt'''   ,				max_patches=lowercase				).flattened_patches
               self.assertEqual(
                   encoded_images.shape   ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,				)










'''simple docstring'''







import os
from distutils.util import strtobool





def 						snake_case_							(_a	:							Union[str, Any]    ,   _a	:							List[Any]     ):
    for e in env_keys:
        UpperCAmelCase			       =		int(os.environ.get(_a    ,   -1     )     )
        if val >= 0:
            return val
    return default





def 						snake_case_							(_a	:							Dict    ,   _a	:							Any=False     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return strtobool(_a     ) == 1  # As its name indicates `strtobool` actually returns an int...







def 						snake_case_							(_a	:							str    ,   _a	:							Optional[Any]="no"     ):
    UpperCAmelCase			       =		os.environ.get(_a    ,   str(_a     )     )
    return value




