
"""simple docstring"""
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():
    from PIL import Image

    from transformers import OwlViTImageProcessor






class   _UpperCAmelCase					(       unittest.TestCase):
    def __init__(     self    ,     snake_case_    ,     snake_case_=7    ,     snake_case_=3    ,     snake_case_=18    ,     snake_case_=30    ,     snake_case_=4_00    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=None    ,     snake_case_=True    ,     snake_case_=[0.48145466, 0.4578275, 0.40821073]    ,     snake_case_=[0.26862954, 0.26130258, 0.27577711]    ,     snake_case_=True    ,     ):
      _snake_case  :    str            = parent
      _snake_case  :    Dict            = batch_size
      _snake_case  :    List[Any]            = num_channels
      _snake_case  :    int            = image_size
      _snake_case  :    Optional[int]            = min_resolution
      _snake_case  :    Optional[Any]            = max_resolution
      _snake_case  :    Tuple            = do_resize
      _snake_case  :    int            = size if size is not None else {"height": 18, "width": 18}
      _snake_case  :    Dict            = do_center_crop
      _snake_case  :    Optional[Any]            = crop_size if crop_size is not None else {"height": 18, "width": 18}
      _snake_case  :    Union[str, Any]            = do_normalize
      _snake_case  :    Dict            = image_mean
      _snake_case  :    Tuple            = image_std
      _snake_case  :    Any            = do_convert_rgb


    def        lowerCamelCase__						(     self  ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }






@require_torch
@require_vision
class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       OwlViTImageProcessor if is_vision_available() else None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = OwlViTImageProcessingTester(self  )
    @property
    def        lowerCamelCase__						(     self  ):
      return self.image_processor_tester.prepare_image_processor_dict()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(snake_case_    ,     "do_resize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "size"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_center_crop"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "center_crop"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_normalize"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_mean"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "image_std"  )  )
      self.assertTrue(hasattr(snake_case_    ,     "do_convert_rgb"  )  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size    ,     {"height": 18, "width": 18}  )
      self.assertEqual(image_processor.crop_size    ,     {"height": 18, "width": 18}  )

      _snake_case  :    str            = self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42    ,     crop_size=84  )
      self.assertEqual(image_processor.size    ,     {"height": 42, "width": 42}  )
      self.assertEqual(image_processor.crop_size    ,     {"height": 84, "width": 84}  )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    str            = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _snake_case  :    Tuple            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_  )

      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     Image.Image  )

      # Test not batched input
      _snake_case  :    Dict            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Dict            = image_processing(snake_case_    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )
    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    Union[str, Any]            = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _snake_case  :    Union[str, Any]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     numpify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     np.ndarray  )

      # Test not batched input
      _snake_case  :    Tuple            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Optional[int]            = image_processing(snake_case_    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )


    def        lowerCamelCase__						(     self  ):
      # Initialize image_processing
      _snake_case  :    str            = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _snake_case  :    Optional[int]            = prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=snake_case_    ,     torchify=snake_case_  )
      for image in image_inputs:
        self.assertIsInstance(snake_case_    ,     torch.Tensor  )

      # Test not batched input
      _snake_case  :    Tuple            = image_processing(image_inputs[0]    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )

      # Test batched
      _snake_case  :    Optional[Any]            = image_processing(snake_case_    ,     return_tensors="pt"  ).pixel_values
      self.assertEqual(
          encoded_images.shape    ,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )    ,     )



"""simple docstring"""
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

