




'''simple docstring'''







import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:







      class 				_a   :





           '''simple docstring'''



           @staticmethod
           def  snake_case_			(							*__a       ,**__a       )			->							Tuple:
                pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class 				_a   (unittest.TestCase  ):





     '''simple docstring'''



     lowerCAmelCase_     :    Any									=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     lowerCAmelCase_     :    Optional[Any]									=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







     def  snake_case_			(							self       ,__a       ,__a       ,__a       )			->							Tuple:
          snake_case :			List[str]					   = ImageClassificationPipeline(model=__a       ,image_processor=__a       ,top_k=2       )
          snake_case :			List[Any]					   = [
              Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       ),
              """http://images.cocodataset.org/val2017/000000039769.jpg""",
          ]
          return image_classifier, examples







     def  snake_case_			(							self       ,__a       ,__a       )			->							str:
          snake_case :			Union[str, Any]					   = image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )

          self.assertEqual(
              __a       ,[
                  {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                  {"""score""": ANY(__a       ), """label""": ANY(__a       )},
              ]       ,)

          import datasets

          snake_case :			List[str]					   = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,"""image"""       ,split="""test"""       )

          # Accepts URL + PIL.Image + lists
          snake_case :			Dict					   = image_classifier(
              [
                  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       ),
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  # RGBA
                  dataset[0]["""file"""],
                  # LA
                  dataset[1]["""file"""],
                  # L
                  dataset[2]["""file"""],
              ]       )
          self.assertEqual(
              __a       ,[
                  [
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                  ],
                  [
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                  ],
                  [
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                  ],
                  [
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                  ],
                  [
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                      {"""score""": ANY(__a       ), """label""": ANY(__a       )},
                  ],
              ]       ,)







     @require_torch
     def  snake_case_			(							self       )			->							List[Any]:
          snake_case :			Optional[int]					   = """hf-internal-testing/tiny-random-vit"""
          snake_case :			Any					   = pipeline("""image-classification"""       ,model=__a       )

          snake_case :			Optional[int]					   = image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]       ,)

          snake_case :			Any					   = image_classifier(
              [
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
              ]       ,top_k=2       ,)
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
              ]       ,)







     @require_tf
     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			Tuple					   = """hf-internal-testing/tiny-random-vit"""
          snake_case :			Dict					   = pipeline("""image-classification"""       ,model=__a       ,framework="""tf"""       )

          snake_case :			Optional[int]					   = image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]       ,)

          snake_case :			Dict					   = image_classifier(
              [
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
                  """http://images.cocodataset.org/val2017/000000039769.jpg""",
              ]       ,top_k=2       ,)
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
                  [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}],
              ]       ,)







     def  snake_case_			(							self       )			->							Optional[Any]:
          snake_case :			Any					   = PreTrainedTokenizer()

          # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
          snake_case :			Optional[Any]					   = pipeline(
              """image-classification"""       ,model="""hf-internal-testing/tiny-random-vit"""       ,tokenizer=__a       )

          self.assertIs(image_classifier.tokenizer       ,__a       )







     @slow
     @require_torch
     def  snake_case_			(							self       )			->							Optional[int]:
          # Perceiver is not tested by `run_pipeline_test` properly.
          # That is because the type of feature_extractor and model preprocessor need to be kept
          # in sync, which is not the case in the current design
          snake_case :			Tuple					   = pipeline("""image-classification"""       ,model="""deepmind/vision-perceiver-conv"""       )
          snake_case :			Any					   = image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[
                  {"""score""": 0.4_385, """label""": """tabby, tabby cat"""},
                  {"""score""": 0.321, """label""": """tiger cat"""},
                  {"""score""": 0.0_502, """label""": """Egyptian cat"""},
                  {"""score""": 0.0_137, """label""": """crib, cot"""},
                  {"""score""": 0.007, """label""": """radiator"""},
              ]       ,)

          snake_case :			Optional[Any]					   = pipeline("""image-classification"""       ,model="""deepmind/vision-perceiver-fourier"""       )
          snake_case :			Optional[Any]					   = image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[
                  {"""score""": 0.5_658, """label""": """tabby, tabby cat"""},
                  {"""score""": 0.1_309, """label""": """tiger cat"""},
                  {"""score""": 0.0_722, """label""": """Egyptian cat"""},
                  {"""score""": 0.0_707, """label""": """remote control, remote"""},
                  {"""score""": 0.0_082, """label""": """computer keyboard, keypad"""},
              ]       ,)

          snake_case :			Optional[Any]					   = pipeline("""image-classification"""       ,model="""deepmind/vision-perceiver-learned"""       )
          snake_case :			List[Any]					   = image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
          self.assertEqual(
              nested_simplify(__a       ,decimals=4       )       ,[
                  {"""score""": 0.3_022, """label""": """tabby, tabby cat"""},
                  {"""score""": 0.2_362, """label""": """Egyptian cat"""},
                  {"""score""": 0.1_856, """label""": """tiger cat"""},
                  {"""score""": 0.0_324, """label""": """remote control, remote"""},
                  {"""score""": 0.0_096, """label""": """quilt, comforter, comfort, puff"""},
              ]       ,)









'''simple docstring'''







from dataclasses import dataclass

lowercase			:      Tuple			=					"""0.1"""
lowercase			:      Optional[Any]			=					"""Lucia Harcekova"""







@dataclass
class 				_a   :





     '''simple docstring'''



     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    int
     lowerCAmelCase_     :    str







     def __repr__(							self       )			->							str:
          return F'''({self.offset}, {self.length}, {self.indicator})'''







class 				_a   :





     '''simple docstring'''



     def __init__(							self       ,__a = 13       ,__a = 6       )			->							None:
          snake_case :			Dict					   = window_size
          snake_case :			Any					   = lookahead_buffer_size
          snake_case :			str					   = self.window_size - self.lookahead_buffer_size







     def  snake_case_			(							self       ,__a       )			->							list[Token]:
          snake_case :			Tuple					   = []
          snake_case :			Optional[int]					   = """"""

          # while there are still characters in text to compress
          while text:
               # find the next encoding phrase
               # - triplet with offset, length, indicator (the next encoding character)
               snake_case :			str					   = self._find_encoding_token(__a       ,__a       )

               # update the search buffer:
               # - add new characters from text into it
               # - check if size exceed the max search buffer size, if so, drop the
               #   oldest elements
               search_buffer += text[: token.length + 1]
               if len(__a       ) > self.search_buffer_size:
                    snake_case :			List[Any]					   = search_buffer[-self.search_buffer_size :]

               # update the text
               snake_case :			str					   = text[token.length + 1 :]

               # append the token to output
               output.append(__a       )

          return output







     def  snake_case_			(							self       ,__a       )			->							str:
          snake_case :			Union[str, Any]					   = """"""

          for token in tokens:
               for _ in range(token.length       ):
                    output += output[-token.offset]
               output += token.indicator

          return output







     def  snake_case_			(							self       ,__a       ,__a       )			->							Token:

          if not text:
               raise ValueError("""We need some text to work with."""       )

          # Initialise result parameters to default values
          snake_case   ,		snake_case :			int					   = 0, 0

          if not search_buffer:
               return Token(__a       ,__a       ,text[length]       )

          for i, character in enumerate(__a       ):
               snake_case :			Tuple					   = len(__a       ) - i
               if character == text[0]:
                    snake_case :			List[Any]					   = self._match_length_from_index(__a       ,__a       ,0       ,__a       )
                    # if the found length is bigger than the current or if it's equal,
                    # which means it's offset is smaller: update offset and length
                    if found_length >= length:
                         snake_case   ,		snake_case :			Optional[Any]					   = found_offset, found_length

          return Token(__a       ,__a       ,text[length]       )







     def  snake_case_			(							self       ,__a       ,__a       ,__a       ,__a       )			->							int:
          if not text or text[text_index] != window[window_index]:
               return 0
          return 1 + self._match_length_from_index(
              __a       ,window + text[text_index]       ,text_index + 1       ,window_index + 1       )


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

      testmod()
      # Initialize compressor class
      lowercase			:      int			=					LZaaCompressor(window_size=13, lookahead_buffer_size=6)

      # Example
      lowercase			:      int			=					"""cabracadabrarrarrad"""
      lowercase			:      str			=					lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("""ababcbababaa"""))
      lowercase			:      List[Any]			=					lzaa_compressor.decompress(compressed_text)
      assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."



