




'''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:
 lowerCAmelCase_			:     str							    =     None







class     SCREAMING_SNAKE_CASE						(	unittest.TestCase							):






      def __init__(   self      :     Dict	,	lowercase__      :     str	,	lowercase__      :     Any=7	,	lowercase__      :     Tuple=3	,	lowercase__      :     List[str]=30	,	lowercase__      :     Union[str, Any]=400	,	lowercase__      :     Optional[int]=True	,	lowercase__      :     Union[str, Any]=None	,	lowercase__      :     Any=True	,	lowercase__      :     List[str]=None	,	lowercase__      :     Any=None	,	lowercase__      :     Optional[int]=True	,	lowercase__      :     List[Any]=1 / 255	,	lowercase__      :     Optional[Any]=True	,	lowercase__      :     Union[str, Any]=FLAVA_IMAGE_MEAN	,	lowercase__      :     Optional[int]=FLAVA_IMAGE_STD	,	lowercase__      :     List[str]=14	,	lowercase__      :     str=75	,	lowercase__      :     Dict=None	,	lowercase__      :     Optional[Any]=16	,	lowercase__      :     Union[str, Any]=0.3	,	lowercase__      :     Union[str, Any]=None	,	lowercase__      :     Optional[int]=True	,	lowercase__      :     Tuple=None	,	lowercase__      :     Optional[Any]=None	,	lowercase__      :     List[Any]=True	,	lowercase__      :     Any=None	,	lowercase__      :     str=True	,	lowercase__      :     int=True	,	lowercase__      :     str=FLAVA_CODEBOOK_MEAN	,	lowercase__      :     List[Any]=FLAVA_CODEBOOK_STD	,	):


           '''simple docstring'''

           a_    :       Dict    	=     size if size is not None else {"""height""": 224, """width""": 224}
           a_    :       str    	=     crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           a_    :       Tuple    	=     codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
           a_    :       List[Any]    	=     codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

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

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

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






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           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]					):


           '''simple docstring'''

           return (self.size["height"], self.size["width"])






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           return (
               (self.input_size_patches, self.input_size_patches)
               if not isinstance(self.input_size_patches	,	lowercase__					)
               else self.input_size_patches
           )






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           return (self.codebook_size["height"], self.codebook_size["width"])







@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE						(	snake_case_       ,			unittest.TestCase							):
      __magic_name__     :					Any             =				FlavaImageProcessor if is_vision_available() else None
      __magic_name__     :					str             =				None






      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     FlavaImageProcessingTester(self					)






      @property
      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()






      def        lowercase_	(   self      :     Any					):


           '''simple docstring'''

           a_    :       List[Any]    	=     self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(lowercase__	,	"""image_mean"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""image_std"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_normalize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_resize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""resample"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""crop_size"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_center_crop"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""do_rescale"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""rescale_factor"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""masking_generator"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_do_resize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_size"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_resample"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_do_center_crop"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_crop_size"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_do_map_pixels"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_do_normalize"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_image_mean"""					)					)
           self.assertTrue(hasattr(lowercase__	,	"""codebook_image_std"""					)					)






      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           a_    :       Any    	=     self.image_processing_class.from_dict(self.image_processor_dict					)
           self.assertEqual(image_processor.size	,	{"""height""": 224, """width""": 224}					)
           self.assertEqual(image_processor.crop_size	,	{"""height""": 224, """width""": 224}					)
           self.assertEqual(image_processor.codebook_size	,	{"""height""": 112, """width""": 112}					)
           self.assertEqual(image_processor.codebook_crop_size	,	{"""height""": 112, """width""": 112}					)

           a_    :       List[str]    	=     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      :     Union[str, Any]					):


           '''simple docstring'''

           pass






      def        lowercase_	(   self      :     int					):


           '''simple docstring'''

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

           # Test not batched input
           a_    :       List[Any]    	=     image_processing(image_inputs[0]	,	return_tensors="""pt"""					)

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

           a_	,    a_    :       Dict    	=     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
           a_    :       str    	=     image_processing(lowercase__	,	return_tensors="""pt"""					)
           a_	,    a_    :       Union[str, Any]    	=     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      :     str	,	lowercase__      :     List[str]	,	lowercase__      :     Optional[int]					):


           '''simple docstring'''

           a_    :       Tuple    	=     self.image_processing_class(**self.image_processor_dict					)
           # create random tensors
           a_    :       List[str]    	=     prepare_image_inputs(self.image_processor_tester	,	equal_resolution=lowercase__	,	**lowercase__					)
           for image in image_inputs:
                self.assertIsInstance(lowercase__	,	lowercase__					)

           # Test not batched input
           a_    :       Any    	=     image_processing(image_inputs[0]	,	return_tensors="""pt"""					)

           a_	,    a_    :       int    	=     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)	,	)

           a_    :       List[str]    	=     image_processing(lowercase__	,	return_image_mask=lowercase__	,	return_tensors="""pt"""					)

           a_	,    a_    :       Optional[int]    	=     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,
               )	,	)

           a_	,    a_    :       Any    	=     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
           a_    :       Dict    	=     image_processing(lowercase__	,	return_tensors="""pt"""					).pixel_values

           a_	,    a_    :       Union[str, Any]    	=     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
           a_    :       List[Any]    	=     image_processing(lowercase__	,	return_image_mask=lowercase__	,	return_tensors="""pt"""					)

           a_	,    a_    :       Optional[int]    	=     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,
               )	,	)

           a_	,    a_    :       Tuple    	=     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      :     List[Any]					):


           '''simple docstring'''

           self._test_call_framework(np.ndarray	,	prepare_kwargs={"""numpify""": True}					)






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           self._test_call_framework(torch.Tensor	,	prepare_kwargs={"""torchify""": True}					)






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           random.seed(1234					)
           a_    :       Any    	=     self.image_processing_class(**self.image_processor_dict					)
           a_    :       Optional[int]    	=     prepare_image_inputs(self.image_processor_tester	,	equal_resolution=lowercase__	,	torchify=lowercase__					)

           # Test not batched input
           a_    :       Optional[Any]    	=     image_processing(image_inputs[0]	,	return_image_mask=lowercase__	,	return_tensors="""pt"""					)
           self.assertEqual(encoded_images.bool_masked_pos.sum().item()	,	75					)






      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

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

           # Test not batched input
           a_    :       Union[str, Any]    	=     image_processing(image_inputs[0]	,	return_codebook_pixels=lowercase__	,	return_tensors="""pt"""					)
           a_	,    a_    :       List[str]    	=     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
           a_    :       List[Any]    	=     image_processing(lowercase__	,	return_codebook_pixels=lowercase__	,	return_tensors="""pt"""					)
           a_	,    a_    :       List[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'''


from .stack import Stack



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       Stack[str]    	=     Stack()
     a_    :       Tuple    	=     {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(UpperCamelCase__							)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


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

 testmod()

 lowerCAmelCase_			:     Any							    =     ['((()))', '((())', '(()))']
 print('Balanced parentheses demonstration:\n')
 for example in examples:
  lowerCAmelCase_			:     Optional[Any]							    =     '' if balanced_parentheses(example) else 'not '
  print(f"{example} is {not_str}balanced")
