


from dataclasses import dataclass

__lowerCamelCase   	=  '''0.1'''
__lowerCamelCase   	=  '''Lucia Harcekova'''


@dataclass
class 	a__						:
       lowerCamelCase__:  int
       lowerCamelCase__:  int
       lowerCamelCase__:  str

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


class 	a__						:

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  int = 1_3   ,				lowerCamelCase_   :  int = 6     ):
            a_     :  List[Any]				      =     window_size
            a_     :  Optional[Any]				      =     lookahead_buffer_size
            a_     :  Union[str, Any]				      =     self.window_size - self.lookahead_buffer_size

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  str     ):
            a_     :  Tuple				      =     []
            a_     :  Dict				      =     """"""

            # 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)
                 a_     :  Optional[Any]				      =     self._find_encoding_token(lowerCamelCase_   ,				lowerCamelCase_     )

                 # 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(lowerCamelCase_     ) > self.search_buffer_size:
                      a_     :  Dict				      =     search_buffer[-self.search_buffer_size :]

                 # update the text
                 a_     :  Union[str, Any]				      =     text[token.length + 1 :]

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

            return output

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  list[Token]     ):
            a_     :  str				      =     """"""

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

            return output

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  str     ):

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

            # Initialise result parameters to default values
            a_      ,      a_     :  Optional[Any]				      =     0, 0

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

            for i, character in enumerate(lowerCamelCase_     ):
                 a_     :  Dict				      =     len(lowerCamelCase_     ) - i
                 if character == text[0]:
                      a_     :  Union[str, Any]				      =     self._match_length_from_index(lowerCamelCase_   ,				lowerCamelCase_   ,				0   ,				lowerCamelCase_     )
                      # 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:
                           a_      ,      a_     :  Optional[Any]				      =     found_offset, found_length

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

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  int     ):
            if not text or text[text_index] != window[window_index]:
                 return 0
            return 1 + self._match_length_from_index(
                lowerCamelCase_   ,				window + text[text_index]   ,				text_index + 1   ,				window_index + 1     )


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

       testmod()
       # Initialize compressor class
       __lowerCamelCase   	=  LZaaCompressor(window_size=13, lookahead_buffer_size=6)

       # Example
       __lowerCamelCase   	=  '''cabracadabrarrarrad'''
       __lowerCamelCase   	=  lzaa_compressor.compress(TEXT)
       print(lzaa_compressor.compress('''ababcbababaa'''))
       __lowerCamelCase   	=  lzaa_compressor.decompress(compressed_text)
       assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."









"""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 							lowercase   :







						@staticmethod
						def        a_	(				*_lowerCamelCase :       Tuple   ,  **_lowerCamelCase :       str							):



							"""simple docstring"""

							pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 							lowercase   (     unittest.TestCase):
				__lowerCAmelCase     :     str								= MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
				__lowerCAmelCase     :     Union[str, Any]								= TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					List[str]						 =		ImageClassificationPipeline(model=_lowerCamelCase   ,  image_processor=_lowerCamelCase   ,  top_k=2							)
					A_	:					Tuple						 =		[
					    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							),
					    '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					]
					return image_classifier, examples







				def        a_	(				self :       Optional[Any]   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					int						 =		image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)

					self.assertEqual(
					    _lowerCamelCase   ,  [
					        {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					    ]   ,  )

					import datasets

					A_	:					Optional[Any]						 =		datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,  '''image'''   ,  split='''test'''							)

					# Accepts URL + PIL.Image + lists
					A_	:					Union[str, Any]						 =		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(
					    _lowerCamelCase   ,  [
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					        [
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					            {'''score''': ANY(_lowerCamelCase							), '''label''': ANY(_lowerCamelCase							)},
					        ],
					    ]   ,  )







				@require_torch
				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		'''hf-internal-testing/tiny-random-vit'''
					A_	:					Tuple						 =		pipeline('''image-classification'''   ,  model=_lowerCamelCase							)

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

					A_	:					str						 =		image_classifier(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]   ,  top_k=2   ,  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  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        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					List[str]						 =		'''hf-internal-testing/tiny-random-vit'''
					A_	:					Optional[int]						 =		pipeline('''image-classification'''   ,  model=_lowerCamelCase   ,  framework='''tf'''							)

					A_	:					Tuple						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]   ,  )

					A_	:					Optional[Any]						 =		image_classifier(
					    [
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
					    ]   ,  top_k=2   ,  )
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  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        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Any						 =		PreTrainedTokenizer()

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

					self.assertIs(image_classifier.tokenizer   ,  _lowerCamelCase							)







				@slow
				@require_torch
				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					List[Any]						 =		pipeline('''image-classification'''   ,  model='''deepmind/vision-perceiver-conv'''							)
					A_	:					Optional[Any]						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        {'''score''': 0.4385, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.321, '''label''': '''tiger cat'''},
					        {'''score''': 0.0502, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.0137, '''label''': '''crib, cot'''},
					        {'''score''': 0.007, '''label''': '''radiator'''},
					    ]   ,  )

					A_	:					Dict						 =		pipeline('''image-classification'''   ,  model='''deepmind/vision-perceiver-fourier'''							)
					A_	:					str						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        {'''score''': 0.5658, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.1309, '''label''': '''tiger cat'''},
					        {'''score''': 0.0722, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.0707, '''label''': '''remote control, remote'''},
					        {'''score''': 0.0082, '''label''': '''computer keyboard, keypad'''},
					    ]   ,  )

					A_	:					Optional[int]						 =		pipeline('''image-classification'''   ,  model='''deepmind/vision-perceiver-learned'''							)
					A_	:					int						 =		image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
					self.assertEqual(
					    nested_simplify(_lowerCamelCase   ,  decimals=4							)   ,  [
					        {'''score''': 0.3022, '''label''': '''tabby, tabby cat'''},
					        {'''score''': 0.2362, '''label''': '''Egyptian cat'''},
					        {'''score''': 0.1856, '''label''': '''tiger cat'''},
					        {'''score''': 0.0324, '''label''': '''remote control, remote'''},
					        {'''score''': 0.0096, '''label''': '''quilt, comforter, comfort, puff'''},
					    ]   ,  )





