
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     :	Optional[Any]    = False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor







class 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case=7		,     _snake_case=3		,     _snake_case=18		,     _snake_case=30		,     _snake_case=400		,     _snake_case=None		,     _snake_case=True		,     _snake_case=True		,     _snake_case=None		,     )    ->      Tuple:


								'''simple docstring'''




								__a										=    size if size is not None else {'''height''': 20, '''width''': 20}
								__a										=    parent
								__a										=    batch_size
								__a										=    num_channels
								__a										=    image_size
								__a										=    min_resolution
								__a										=    max_resolution
								__a										=    size
								__a										=    do_normalize
								__a										=    do_convert_rgb
								__a										=    [512, 1_024, 2_048, 4_096]
								__a										=    patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    '''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
								__a										=    Image.open(requests.get(_snake_case		,     stream=_snake_case				).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			):
				snake_case_						   =    PixaStructImageProcessor if is_vision_available() else None




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


								'''simple docstring'''




								__a										=    PixaStructImageProcessingTester(self				)




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.image_processor_tester.prepare_dummy_image()

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

								__a										=    image_processor(_snake_case		,     return_tensors='''pt'''		,     max_patches=_snake_case				)
								self.assertTrue(torch.allclose(inputs.flattened_patches.mean()		,     torch.tensor(0.0606				)		,     atol=1E-3		,     rtol=1E-3				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




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

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




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

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

								__a										=    True

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

												__a										=    '''Hello'''

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




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

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

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




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

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

												# Test batched
												__a										=    image_processor(
												    _snake_case		,     return_tensors='''pt'''		,     max_patches=_snake_case				).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			):
				snake_case_						   =    PixaStructImageProcessor if is_vision_available() else None




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    PixaStructImageProcessingTester(self		,     num_channels=4				)
								__a										=    3




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


								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(_snake_case		,     '''do_normalize'''				)				)
								self.assertTrue(hasattr(_snake_case		,     '''do_convert_rgb'''				)				)




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


								'''simple docstring'''




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

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

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



"""simple docstring"""







import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
		from scipy.stats import pearsonr, spearmanr
		from sklearn.metrics import fa_score, matthews_corrcoef


a__				:							Any						=     (
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					return (preds == labels).mean()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
					__SCREAMING_SNAKE_CASE						   =				fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_   )
					return {
					    "acc": acc,
					    "f1": fa,
					    "acc_and_f1": (acc + fa) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					__SCREAMING_SNAKE_CASE						   =				pearsonr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					__SCREAMING_SNAKE_CASE						   =				spearmanr(lowerCAmelCase_  ,						lowerCAmelCase_   )[0]
					return {
					    "pearson": pearson_corr,
					    "spearmanr": spearman_corr,
					    "corr": (pearson_corr + spearman_corr) / 2,
					}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					assert len(lowerCAmelCase_   ) == len(lowerCAmelCase_   ), f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""
					if task_name == "cola":
										return {"mcc": matthews_corrcoef(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "sst-2":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mrpc":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "sts-b":
										return pearson_and_spearman(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "qqp":
										return acc_and_fa(lowerCAmelCase_  ,						lowerCAmelCase_   )
					elif task_name == "mnli":
										return {"mnli/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "mnli-mm":
										return {"mnli-mm/acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "qnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "rte":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "wnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					elif task_name == "hans":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
										raise ValueError(f"""Predictions and labels have mismatched lengths {len(lowerCAmelCase_   )} and {len(lowerCAmelCase_   )}"""   )
					if task_name == "xnli":
										return {"acc": simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )}
					else:
										raise KeyError(lowerCAmelCase_   )
