


'''simple docstring'''



import random
import unittest

import numpy as np

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

if is_vision_available():
      import PIL

      from transformers import FlavaImageProcessor
      from transformers.image_utils import PILImageResampling
      from transformers.models.flava.image_processing_flava import (
          FLAVA_CODEBOOK_MEAN,
          FLAVA_CODEBOOK_STD,
          FLAVA_IMAGE_MEAN,
          FLAVA_IMAGE_STD,
      )
else:
      A__		: Any												=  None





class        UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=7      ,					SCREAMING_SNAKE_CASE_=3      ,					SCREAMING_SNAKE_CASE_=30      ,					SCREAMING_SNAKE_CASE_=4_00      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=1 / 2_55      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=FLAVA_IMAGE_MEAN      ,					SCREAMING_SNAKE_CASE_=FLAVA_IMAGE_STD      ,					SCREAMING_SNAKE_CASE_=14      ,					SCREAMING_SNAKE_CASE_=75      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=16      ,					SCREAMING_SNAKE_CASE_=0.3      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=FLAVA_CODEBOOK_MEAN      ,					SCREAMING_SNAKE_CASE_=FLAVA_CODEBOOK_STD      ,					)    ->					List[Any]:
         __lowerCamelCase       :     Union[str, Any]        =		size if size is not None else {'height': 2_24, 'width': 2_24}
         __lowerCamelCase       :     int        =		crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
         __lowerCamelCase       :     str        =		codebook_size if codebook_size is not None else {'height': 1_12, 'width': 1_12}
         __lowerCamelCase       :     Any        =		codebook_crop_size if codebook_crop_size is not None else {'height': 1_12, 'width': 1_12}

         __lowerCamelCase       :     Any        =		parent
         __lowerCamelCase       :     Optional[Any]        =		batch_size
         __lowerCamelCase       :     Any        =		num_channels
         __lowerCamelCase       :     Union[str, Any]        =		do_resize
         __lowerCamelCase       :     List[str]        =		do_rescale
         __lowerCamelCase       :     List[Any]        =		rescale_factor
         __lowerCamelCase       :     str        =		min_resolution
         __lowerCamelCase       :     List[Any]        =		max_resolution
         __lowerCamelCase       :     Dict        =		size
         __lowerCamelCase       :     Optional[Any]        =		resample if resample is not None else PILImageResampling.BICUBIC
         __lowerCamelCase       :     Union[str, Any]        =		do_normalize
         __lowerCamelCase       :     Union[str, Any]        =		image_mean
         __lowerCamelCase       :     Dict        =		image_std
         __lowerCamelCase       :     List[str]        =		do_center_crop
         __lowerCamelCase       :     Tuple        =		crop_size

         __lowerCamelCase       :     Optional[Any]        =		input_size_patches
         __lowerCamelCase       :     Any        =		total_mask_patches
         __lowerCamelCase       :     List[str]        =		mask_group_max_patches
         __lowerCamelCase       :     Tuple        =		mask_group_min_patches
         __lowerCamelCase       :     Optional[Any]        =		mask_group_min_aspect_ratio
         __lowerCamelCase       :     int        =		mask_group_max_aspect_ratio

         __lowerCamelCase       :     Tuple        =		codebook_do_resize
         __lowerCamelCase       :     Any        =		codebook_size
         __lowerCamelCase       :     Any        =		codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
         __lowerCamelCase       :     Union[str, Any]        =		codebook_do_center_crop
         __lowerCamelCase       :     Tuple        =		codebook_crop_size
         __lowerCamelCase       :     int        =		codebook_do_map_pixels
         __lowerCamelCase       :     Tuple        =		codebook_do_normalize
         __lowerCamelCase       :     Tuple        =		codebook_image_mean
         __lowerCamelCase       :     str        =		codebook_image_std







    def 				lowercase_    (							self     )    ->					int:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "resample": self.resample,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "input_size_patches": self.input_size_patches,
             "total_mask_patches": self.total_mask_patches,
             "mask_group_max_patches": self.mask_group_max_patches,
             "mask_group_min_patches": self.mask_group_min_patches,
             "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
             "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
             "codebook_do_resize": self.codebook_do_resize,
             "codebook_size": self.codebook_size,
             "codebook_resample": self.codebook_resample,
             "codebook_do_center_crop": self.codebook_do_center_crop,
             "codebook_crop_size": self.codebook_crop_size,
             "codebook_do_map_pixels": self.codebook_do_map_pixels,
             "codebook_do_normalize": self.codebook_do_normalize,
             "codebook_image_mean": self.codebook_image_mean,
             "codebook_image_std": self.codebook_image_std,
         }







    def 				lowercase_    (							self     )    ->					Optional[int]:
         return (self.size["height"], self.size["width"])







    def 				lowercase_    (							self     )    ->					Optional[int]:
         return (
             (self.input_size_patches, self.input_size_patches)
             if not isinstance(self.input_size_patches      ,					SCREAMING_SNAKE_CASE_     )
             else self.input_size_patches
         )







    def 				lowercase_    (							self     )    ->					List[str]:
         return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class        UpperCAmelCase_  (_UpperCAmelCase ,		unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  : Optional[Any]												=      FlavaImageProcessor if is_vision_available() else None
    lowerCamelCase  : Optional[Any]												=      None







    def 				lowercase_    (							self     )    ->					Union[str, Any]:
         __lowerCamelCase       :     str        =		FlavaImageProcessingTester(self     )







    @property
    def 				lowercase_    (							self     )    ->					int:
         return self.image_processor_tester.prepare_image_processor_dict()







    def 				lowercase_    (							self     )    ->					Dict:
         __lowerCamelCase       :     Any        =		self.image_processing_class(**self.image_processor_dict     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'image_mean'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'image_std'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'do_normalize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'do_resize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'resample'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'crop_size'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'do_center_crop'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'do_rescale'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'rescale_factor'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'masking_generator'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_do_resize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_size'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_resample'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_do_center_crop'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_crop_size'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_do_map_pixels'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_do_normalize'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_image_mean'     )     )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_      ,					'codebook_image_std'     )     )







    def 				lowercase_    (							self     )    ->					List[Any]:
         __lowerCamelCase       :     str        =		self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size      ,					{'height': 2_24, 'width': 2_24}     )
         self.assertEqual(image_processor.crop_size      ,					{'height': 2_24, 'width': 2_24}     )
         self.assertEqual(image_processor.codebook_size      ,					{'height': 1_12, 'width': 1_12}     )
         self.assertEqual(image_processor.codebook_crop_size      ,					{'height': 1_12, 'width': 1_12}     )

         __lowerCamelCase       :     Dict        =		self.image_processing_class.from_dict(
             self.image_processor_dict      ,					size=42      ,					crop_size=84      ,					codebook_size=33      ,					codebook_crop_size=66     )
         self.assertEqual(image_processor.size      ,					{'height': 42, 'width': 42}     )
         self.assertEqual(image_processor.crop_size      ,					{'height': 84, 'width': 84}     )
         self.assertEqual(image_processor.codebook_size      ,					{'height': 33, 'width': 33}     )
         self.assertEqual(image_processor.codebook_crop_size      ,					{'height': 66, 'width': 66}     )







    def 				lowercase_    (							self     )    ->					Any:
         pass







    def 				lowercase_    (							self     )    ->					Dict:
         # Initialize image_processing
         __lowerCamelCase       :     Tuple        =		self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         __lowerCamelCase       :     List[Any]        =		prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=SCREAMING_SNAKE_CASE_     )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,					PIL.Image.Image     )

         # Test not batched input
         __lowerCamelCase       :     Tuple        =		image_processing(image_inputs[0]      ,					return_tensors='pt'     )

         # Test no bool masked pos
         self.assertFalse('bool_masked_pos' in encoded_images     )

         __lowerCamelCase    ,	__lowerCamelCase       :     str        =		self.image_processor_tester.get_expected_image_size()

         self.assertEqual(
             encoded_images.pixel_values.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

         # Test batched
         __lowerCamelCase       :     Any        =		image_processing(SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )
         __lowerCamelCase    ,	__lowerCamelCase       :     List[str]        =		self.image_processor_tester.get_expected_image_size()

         # Test no bool masked pos
         self.assertFalse('bool_masked_pos' in encoded_images     )

         self.assertEqual(
             encoded_images.pixel_values.shape      ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,					)







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					str:
         # Initialize image_processing
         __lowerCamelCase       :     Dict        =		self.image_processing_class(**self.image_processor_dict     )
         # create random tensors
         __lowerCamelCase       :     Optional[Any]        =		prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )

         # Test not batched input
         __lowerCamelCase       :     Tuple        =		image_processing(image_inputs[0]      ,					return_tensors='pt'     )

         __lowerCamelCase    ,	__lowerCamelCase       :     List[str]        =		self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

         __lowerCamelCase       :     Tuple        =		image_processing(SCREAMING_SNAKE_CASE_      ,					return_image_mask=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )

         __lowerCamelCase    ,	__lowerCamelCase       :     Tuple        =		self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape      ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,					)

         __lowerCamelCase    ,	__lowerCamelCase       :     int        =		self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape      ,					(
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )      ,					)

         # Test batched
         __lowerCamelCase       :     Any        =		image_processing(SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     ).pixel_values

         __lowerCamelCase    ,	__lowerCamelCase       :     List[str]        =		self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.shape      ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,					)

         # Test masking
         __lowerCamelCase       :     Optional[int]        =		image_processing(SCREAMING_SNAKE_CASE_      ,					return_image_mask=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )

         __lowerCamelCase    ,	__lowerCamelCase       :     Dict        =		self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape      ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,					)

         __lowerCamelCase    ,	__lowerCamelCase       :     int        =		self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape      ,					(
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )      ,					)







    def 				lowercase_    (							self     )    ->					int:
         self._test_call_framework(np.ndarray      ,					prepare_kwargs={'numpify': True}     )







    def 				lowercase_    (							self     )    ->					List[str]:
         self._test_call_framework(torch.Tensor      ,					prepare_kwargs={'torchify': True}     )







    def 				lowercase_    (							self     )    ->					Optional[int]:
         # Initialize image_processing
         random.seed(12_34     )
         __lowerCamelCase       :     int        =		self.image_processing_class(**self.image_processor_dict     )
         __lowerCamelCase       :     Dict        =		prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=SCREAMING_SNAKE_CASE_      ,					torchify=SCREAMING_SNAKE_CASE_     )

         # Test not batched input
         __lowerCamelCase       :     List[str]        =		image_processing(image_inputs[0]      ,					return_image_mask=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )
         self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,					75     )







    def 				lowercase_    (							self     )    ->					List[str]:
         # Initialize image_processing
         __lowerCamelCase       :     Optional[int]        =		self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         __lowerCamelCase       :     List[str]        =		prepare_image_inputs(self.image_processor_tester      ,					equal_resolution=SCREAMING_SNAKE_CASE_     )
         for image in image_inputs:
              self.assertIsInstance(SCREAMING_SNAKE_CASE_      ,					PIL.Image.Image     )

         # Test not batched input
         __lowerCamelCase       :     int        =		image_processing(image_inputs[0]      ,					return_codebook_pixels=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )
         __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape      ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,					)

         # Test batched
         __lowerCamelCase       :     List[Any]        =		image_processing(SCREAMING_SNAKE_CASE_      ,					return_codebook_pixels=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'     )
         __lowerCamelCase    ,	__lowerCamelCase       :     Any        =		self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape      ,					(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,					)










'''simple docstring'''



import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
      A__		: Optional[int]												=  []

      A__		: List[Any]												=  os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
      A__		: Optional[int]												=  glob.glob(pattern)
      # TODO: deal with TF/Flax too
      A__		: List[str]												=  [
          x for x in test_files if not (x.startswith("""test_modeling_tf_""") or x.startswith("""test_modeling_flax_"""))
      ]

      for test_file in test_files:
            A__		: Tuple												=  get_tester_classes(test_file)
            for tester_class in tester_classes:
                  # A few tester classes don't have `parent` parameter in `__init__`.
                  # TODO: deal this better
                  try:
                        A__		: Tuple												=  tester_class(parent=None)
                  except Exception:
                        continue
                  if hasattr(tester, """get_config"""):
                        A__		: Any												=  tester.get_config()
                        for k, v in config.to_dict().items():
                              if isinstance(v, int):
                                    A__		: List[Any]												=  None
                                    if k in ["vocab_size"]:
                                          A__		: Any												=  100
                                    elif k in ["max_position_embeddings"]:
                                          A__		: List[Any]												=  128
                                    elif k in ["hidden_size", "d_model"]:
                                          A__		: List[str]												=  40
                                    elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                                          A__		: str												=  5
                                    if target is not None and v > target:
                                          failures.append(
                                              f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                              f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                              f''' than {target}.'''
                                          )

      if len(failures) > 0:
            raise Exception(f'''There were {len(failures)} failures:\n''' + """\n""".join(failures))






