





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:
      SCREAMING_SNAKE_CASE_		  =			SCREAMING_SNAKE_CASE_		  =			SCREAMING_SNAKE_CASE_		  =			SCREAMING_SNAKE_CASE_		  =			None

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		size if size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE						=		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
           SCREAMING_SNAKE_CASE						=		codebook_size if codebook_size is not None else {"""height""": 112, """width""": 112}
           SCREAMING_SNAKE_CASE						=		codebook_crop_size if codebook_crop_size is not None else {"""height""": 112, """width""": 112}

           SCREAMING_SNAKE_CASE						=		parent
           SCREAMING_SNAKE_CASE						=		batch_size
           SCREAMING_SNAKE_CASE						=		num_channels
           SCREAMING_SNAKE_CASE						=		do_resize
           SCREAMING_SNAKE_CASE						=		do_rescale
           SCREAMING_SNAKE_CASE						=		rescale_factor
           SCREAMING_SNAKE_CASE						=		min_resolution
           SCREAMING_SNAKE_CASE						=		max_resolution
           SCREAMING_SNAKE_CASE						=		size
           SCREAMING_SNAKE_CASE						=		resample if resample is not None else PILImageResampling.BICUBIC
           SCREAMING_SNAKE_CASE						=		do_normalize
           SCREAMING_SNAKE_CASE						=		image_mean
           SCREAMING_SNAKE_CASE						=		image_std
           SCREAMING_SNAKE_CASE						=		do_center_crop
           SCREAMING_SNAKE_CASE						=		crop_size

           SCREAMING_SNAKE_CASE						=		input_size_patches
           SCREAMING_SNAKE_CASE						=		total_mask_patches
           SCREAMING_SNAKE_CASE						=		mask_group_max_patches
           SCREAMING_SNAKE_CASE						=		mask_group_min_patches
           SCREAMING_SNAKE_CASE						=		mask_group_min_aspect_ratio
           SCREAMING_SNAKE_CASE						=		mask_group_max_aspect_ratio

           SCREAMING_SNAKE_CASE						=		codebook_do_resize
           SCREAMING_SNAKE_CASE						=		codebook_size
           SCREAMING_SNAKE_CASE						=		codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
           SCREAMING_SNAKE_CASE						=		codebook_do_center_crop
           SCREAMING_SNAKE_CASE						=		codebook_crop_size
           SCREAMING_SNAKE_CASE						=		codebook_do_map_pixels
           SCREAMING_SNAKE_CASE						=		codebook_do_normalize
           SCREAMING_SNAKE_CASE						=		codebook_image_mean
           SCREAMING_SNAKE_CASE						=		codebook_image_std






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						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 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						Optional[int]:




           '''simple docstring'''


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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[int]:




           '''simple docstring'''


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

@require_torch
@require_vision
class 						UpperCamelCase__					( lowerCAmelCase_				, unittest.TestCase  ):



     '''simple docstring'''

     __snake_case       :      Tuple						      =     FlavaImageProcessor if is_vision_available() else None
     __snake_case       :      str						      =     None






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		FlavaImageProcessingTester(self			)






     @property
     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Dict:




           '''simple docstring'''


           return self.image_processor_tester.prepare_image_processor_dict()






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Dict			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""image_mean"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""image_std"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_normalize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_resize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""resample"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""crop_size"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_center_crop"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""do_rescale"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""rescale_factor"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""masking_generator"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_do_resize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_size"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_resample"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_do_center_crop"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_crop_size"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_do_map_pixels"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_do_normalize"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_image_mean"""			)			)
           self.assertTrue(hasattr(lowerCamelCase__		,"""codebook_image_std"""			)			)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		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}			)

           SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						List[str]:




           '''simple docstring'''


           pass






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						int:




           '''simple docstring'''


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

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

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

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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
           SCREAMING_SNAKE_CASE						=		image_processing(lowerCamelCase__		,return_tensors="""pt"""			)
           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       Any		,lowerCamelCase__		:       str		,lowerCamelCase__		:       List[Any]			)	->						Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           # create random tensors
           SCREAMING_SNAKE_CASE						=		prepare_image_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__		,**lowerCamelCase__			)
           for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase__		,lowerCamelCase__			)

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

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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)		,)

           SCREAMING_SNAKE_CASE						=		image_processing(lowerCamelCase__		,return_image_mask=lowerCamelCase__		,return_tensors="""pt"""			)

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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,
               )		,)

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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
           SCREAMING_SNAKE_CASE						=		image_processing(lowerCamelCase__		,return_tensors="""pt"""			).pixel_values

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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
           SCREAMING_SNAKE_CASE						=		image_processing(lowerCamelCase__		,return_image_mask=lowerCamelCase__		,return_tensors="""pt"""			)

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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,
               )		,)

           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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 							SCREAMING_SNAKE_CASE__    (				self		:       Union[str, Any]			)	->						str:




           '''simple docstring'''


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






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




           '''simple docstring'''


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






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Optional[Any]:




           '''simple docstring'''


           random.seed(1234			)
           SCREAMING_SNAKE_CASE						=		self.image_processing_class(**self.image_processor_dict			)
           SCREAMING_SNAKE_CASE						=		prepare_image_inputs(self.image_processor_tester		,equal_resolution=lowerCamelCase__		,torchify=lowerCamelCase__			)

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processing(image_inputs[0]		,return_image_mask=lowerCamelCase__		,return_tensors="""pt"""			)
           self.assertEqual(encoded_images.bool_masked_pos.sum().item()		,75			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						List[str]:




           '''simple docstring'''


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

           # Test not batched input
           SCREAMING_SNAKE_CASE						=		image_processing(image_inputs[0]		,return_codebook_pixels=lowerCamelCase__		,return_tensors="""pt"""			)
           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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
           SCREAMING_SNAKE_CASE						=		image_processing(lowerCamelCase__		,return_codebook_pixels=lowerCamelCase__		,return_tensors="""pt"""			)
           SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		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,
               )		,)









import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_		  =			ascii_letters + """ \t\n"""



def   __lowercase  ( )					->  dict[str, None]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		os.path.split(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		{}
      with open(path[0] + """/dictionary.txt"""      ) as dictionary_file:
            for word in dictionary_file.read().split("""\n"""      ):
                  SCREAMING_SNAKE_CASE						=		None
      return english_words


SCREAMING_SNAKE_CASE_		  =			load_dictionary()



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  float:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		message.upper()
      SCREAMING_SNAKE_CASE						=		remove_non_letters(_SCREAMING_SNAKE_CASE      )
      SCREAMING_SNAKE_CASE						=		message.split()
      SCREAMING_SNAKE_CASE						=		len([word for word in possible_words if word in ENGLISH_WORDS]      )
      return float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  str:


      '''simple docstring'''


      return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE      )



def   __lowercase  ( _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE = 20					,		_SCREAMING_SNAKE_CASE = 85      )					->  bool:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		get_english_count(_SCREAMING_SNAKE_CASE      ) * 1_00 >= word_percentage
      SCREAMING_SNAKE_CASE						=		len(remove_non_letters(_SCREAMING_SNAKE_CASE      )      )
      SCREAMING_SNAKE_CASE						=		(float(_SCREAMING_SNAKE_CASE      ) / len(_SCREAMING_SNAKE_CASE      )) * 1_00
      SCREAMING_SNAKE_CASE						=		message_letters_percentage >= letter_percentage
      return words_match and letters_match


if __name__ == "__main__":
      import doctest

      doctest.testmod()


