





'''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:
 __magic_name__   :						Optional[Any]    =			False

if is_vision_available():
 from PIL import Image

 from transformers import PixaStructImageProcessor


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=18						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase=None						,   ):
       _snake_case     		=				size if size is not None else {"height": 20, "width": 20}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				do_convert_rgb
       _snake_case     		=				[512, 1_024, 2_048, 4_096]
       _snake_case     		=				patch_size if patch_size is not None else {"height": 16, "width": 16}

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
       _snake_case     		=				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       __SCREAMING_SNAKE_CASE	(						_lowerCAmelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[str]      =   PixaStructImageProcessor if is_vision_available() else None

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

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

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

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

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

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				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     		=				(
           (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     		=				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     		=				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 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				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     		=				(
           (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
           * self.image_processor_tester.num_channels
       ) + 2

       _snake_case     		=				True

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

         _snake_case     		=				"Hello"

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

       _snake_case     		=				(
           (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     		=				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     		=				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 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PyTorch tensors
       _snake_case     		=				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     		=				(
           (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     		=				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     		=				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       __SCREAMING_SNAKE_CASE	(						_lowerCAmelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   PixaStructImageProcessor if is_vision_available() else None

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

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

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






     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       # create random PIL images
       _snake_case     		=				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     		=				(
           (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     		=				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     		=				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'''



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


__magic_name__   :						List[str]    =			(
    """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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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


