



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:
	_lowercase				:					Tuple     					=None
class lowerCAmelCase_       (   unittest.TestCase			):







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase   ,       lowerCamelCase=7   ,       lowerCamelCase=3   ,       lowerCamelCase=30   ,       lowerCamelCase=400   ,       lowerCamelCase=True   ,       lowerCamelCase=None   ,       lowerCamelCase=True   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase=True   ,       lowerCamelCase=1 / 255   ,       lowerCamelCase=True   ,       lowerCamelCase=FLAVA_IMAGE_MEAN   ,       lowerCamelCase=FLAVA_IMAGE_STD   ,       lowerCamelCase=14   ,       lowerCamelCase=75   ,       lowerCamelCase=None   ,       lowerCamelCase=16   ,       lowerCamelCase=0.3   ,       lowerCamelCase=None   ,       lowerCamelCase=True   ,       lowerCamelCase=None   ,       lowerCamelCase=None   ,       lowerCamelCase=True   ,       lowerCamelCase=None   ,       lowerCamelCase=True   ,       lowerCamelCase=True   ,       lowerCamelCase=FLAVA_CODEBOOK_MEAN   ,       lowerCamelCase=FLAVA_CODEBOOK_STD   ,       ):



				'''simple docstring'''


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

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

				a__					=  input_size_patches
				a__					=  total_mask_patches
				a__					=  mask_group_max_patches
				a__					=  mask_group_min_patches
				a__					=  mask_group_min_aspect_ratio
				a__					=  mask_group_max_aspect_ratio

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

	def 				_A			(	self       ):



				'''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 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self       ):



				'''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 				_A			(	self       ):



				'''simple docstring'''


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


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







	'''simple docstring'''




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

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  FlavaImageProcessingTester(self       )

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return self.image_processor_tester.prepare_image_processor_dict()

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  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 				_A			(	self       ):



				'''simple docstring'''


				a__					=  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__					=  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 				_A			(	self       ):



				'''simple docstring'''


				pass

	def 				_A			(	self       ):



				'''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=lowerCamelCase__       )
				for image in image_inputs:
							self.assertIsInstance(lowerCamelCase__   ,       PIL.Image.Image       )

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

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

				a__				,       a__					=  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__					=  image_processing(lowerCamelCase__   ,       return_tensors="""pt"""       )
				a__				,       a__					=  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 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase       ):



				'''simple docstring'''


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

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

				a__				,       a__					=  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__					=  image_processing(lowerCamelCase__   ,       return_image_mask=lowerCamelCase__   ,       return_tensors="""pt"""       )

				a__				,       a__					=  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__					=  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__					=  image_processing(lowerCamelCase__   ,       return_tensors="""pt"""       ).pixel_values

				a__				,       a__					=  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__					=  image_processing(lowerCamelCase__   ,       return_image_mask=lowerCamelCase__   ,       return_tensors="""pt"""       )

				a__				,       a__					=  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__					=  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 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self       ):



				'''simple docstring'''


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

	def 				_A			(	self       ):



				'''simple docstring'''


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

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







	def 				_A			(	self       ):



				'''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=lowerCamelCase__       )
				for image in image_inputs:
							self.assertIsInstance(lowerCamelCase__   ,       PIL.Image.Image       )

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


