


'''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__  :





											'''simple docstring'''


											@staticmethod
											def UpperCAmelCase_     (   *__snake_case   , **__snake_case  ):
															pass





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





							'''simple docstring'''


							A_							:				Dict				     =  MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							A_							:				List[str]				     =  TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				ImageClassificationPipeline(model=__snake_case   , image_processor=__snake_case   , top_k=2  )
											_SCREAMING_SNAKE_CASE			:	str							=				[
											    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  ),
											    """http://images.cocodataset.org/val2017/000000039769.jpg""",
											]
											return image_classifier, examples






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	str							=				image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )

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

											import datasets

											_SCREAMING_SNAKE_CASE			:	str							=				datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""   , """image"""   , split="""test"""  )

											# Accepts URL + PIL.Image + lists
											_SCREAMING_SNAKE_CASE			:	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(
											    __snake_case   , [
											        [
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											        ],
											        [
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											        ],
											        [
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											        ],
											        [
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											        ],
											        [
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											            {"""score""": ANY(__snake_case  ), """label""": ANY(__snake_case  )},
											        ],
											    ]   , )






							@require_torch
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				"""hf-internal-testing/tiny-random-vit"""
											_SCREAMING_SNAKE_CASE			:	Dict							=				pipeline("""image-classification"""   , model=__snake_case  )

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

											_SCREAMING_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(__snake_case   , 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 UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				"""hf-internal-testing/tiny-random-vit"""
											_SCREAMING_SNAKE_CASE			:	str							=				pipeline("""image-classification"""   , model=__snake_case   , framework="""tf"""  )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""  )
											self.assertEqual(
											    nested_simplify(__snake_case   , decimals=4  )   , [{"""label""": """LABEL_1""", """score""": 0.574}, {"""label""": """LABEL_0""", """score""": 0.426}]   , )

											_SCREAMING_SNAKE_CASE			:	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(__snake_case   , 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 UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				PreTrainedTokenizer()

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

											self.assertIs(image_classifier.tokenizer   , __snake_case  )






							@slow
							@require_torch
							def UpperCAmelCase_     (   self  ):
											# 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
											_SCREAMING_SNAKE_CASE			:	int							=				pipeline("""image-classification"""   , model="""deepmind/vision-perceiver-conv"""  )
											_SCREAMING_SNAKE_CASE			:	Any							=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""  )
											self.assertEqual(
											    nested_simplify(__snake_case   , 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"""},
											    ]   , )

											_SCREAMING_SNAKE_CASE			:	str							=				pipeline("""image-classification"""   , model="""deepmind/vision-perceiver-fourier"""  )
											_SCREAMING_SNAKE_CASE			:	str							=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""  )
											self.assertEqual(
											    nested_simplify(__snake_case   , 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"""},
											    ]   , )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				pipeline("""image-classification"""   , model="""deepmind/vision-perceiver-learned"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""  )
											self.assertEqual(
											    nested_simplify(__snake_case   , 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"""},
											    ]   , )








'''simple docstring'''


import bsa
import requests







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				return {
				    "name": soup.ha.a.text,
				    "genre": soup.find("""span"""							,				class_="""genre"""					).text.strip(),
				    "rating": soup.strong.text,
				    "page_link": f"""https://www.imdb.com{soup.a.get("href"					)}""",
				}







def       snake_case_       (  SCREAMING_SNAKE_CASE__ = 5					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[str]							=				int(float(SCREAMING_SNAKE_CASE__					)					)
				if num_movies < 1:
								return ()
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				(
				    """https://www.imdb.com/search/title?title_type="""
				    f"""feature&sort=num_votes,desc&count={num_movies}"""
				)
				_SCREAMING_SNAKE_CASE			:	Any							=				bsa.BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE__					).content							,				"""html.parser"""					)
				return tuple(
				    get_movie_data_from_soup(SCREAMING_SNAKE_CASE__					)
				    for movie in source.find_all("""div"""							,				class_="""lister-item mode-advanced"""					)					)


if __name__ == "__main__":
				import json

				UpperCAmelCase_		:       List[str]							       =		int(input('How many movies would you like to see? '))
				print(
				    ', '.join(
				        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
				    )
				)




