
from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

_A   =     logging.get_logger(__name__)





@add_end_docstrings(__a       )
class 	_lowerCAmelCase					(	__a       ):




				def __init__(					self				,				**_UpperCamelCase     )				->      str:
							super().__init__(**_UpperCamelCase     )

							requires_backends(self				,				"vision"     )
							self.check_model_type(
							    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
							    if self.framework == "tf"
							    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING     )




				def __call__(					self				,				_UpperCamelCase				,				**_UpperCamelCase     )				->      Tuple:
							return super().__call__(_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				**_UpperCamelCase     )				->      Optional[Any]:
							lowerCAmelCase_						=	{}
							if "candidate_labels" in kwargs:
										lowerCAmelCase_						=	kwargs["candidate_labels"]
							if "hypothesis_template" in kwargs:
										lowerCAmelCase_						=	kwargs["hypothesis_template"]

							return preprocess_params, {}, {}




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase=None				,				_UpperCamelCase="This is a photo of {}."     )				->      List[str]:
							lowerCAmelCase_						=	load_image(_UpperCamelCase     )
							lowerCAmelCase_						=	self.image_processor(images=[image]				,				return_tensors=self.framework     )
							lowerCAmelCase_						=	candidate_labels
							lowerCAmelCase_						=	[hypothesis_template.format(_UpperCamelCase     ) for x in candidate_labels]
							lowerCAmelCase_						=	self.tokenizer(_UpperCamelCase				,				return_tensors=self.framework				,				padding=_UpperCamelCase     )
							lowerCAmelCase_						=	[text_inputs]
							return inputs




				def      __a						(					self				,				_UpperCamelCase     )				->      Optional[int]:
							lowerCAmelCase_						=	model_inputs.pop("candidate_labels"     )
							lowerCAmelCase_						=	model_inputs.pop("text_inputs"     )
							if isinstance(text_inputs[0]				,				_UpperCamelCase     ):
										lowerCAmelCase_						=	text_inputs[0]
							else:
										# Batching case.
										lowerCAmelCase_						=	text_inputs[0][0]

							lowerCAmelCase_						=	self.model(**_UpperCamelCase				,				**_UpperCamelCase     )

							lowerCAmelCase_						=	{
							    "candidate_labels": candidate_labels,
							    "logits": outputs.logits_per_image,
							}
							return model_outputs




				def      __a						(					self				,				_UpperCamelCase     )				->      Dict:
							lowerCAmelCase_						=	model_outputs.pop("candidate_labels"     )
							lowerCAmelCase_						=	model_outputs["logits"][0]
							if self.framework == "pt":
										lowerCAmelCase_						=	logits.softmax(dim=-1     ).squeeze(-1     )
										lowerCAmelCase_						=	probs.tolist()
										if not isinstance(_UpperCamelCase				,				_UpperCamelCase     ):
													lowerCAmelCase_						=	[scores]
							elif self.framework == "tf":
										lowerCAmelCase_						=	stable_softmax(_UpperCamelCase				,				axis=-1     )
										lowerCAmelCase_						=	probs.numpy().tolist()
							else:
										raise ValueError(f"""Unsupported framework: {self.framework}"""     )

							lowerCAmelCase_						=	[
							    {"score": score, "label": candidate_label}
							    for score, candidate_label in sorted(zip(_UpperCamelCase				,				_UpperCamelCase     )				,				key=lambda _UpperCamelCase     : -x[0]     )
							]
							return result





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = ""				):




			"""simple docstring"""


			lowerCAmelCase_						=	url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
			lowerCAmelCase_						=	BeautifulSoup(requests.get(__lowerCAmelCase				).text    ,							"html.parser"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							attrs="titleColumn"				)
			lowerCAmelCase_						=	soup.find_all("td"    ,							class_="ratingColumn imdbRating"				)
			return {
			    title.a.text: float(rating.strong.text				)
			    for title, rating in zip(__lowerCAmelCase    ,							__lowerCAmelCase				)
			}






def    lowerCamelCase__				(				__lowerCAmelCase  :  str = "IMDb_Top_250_Movies.csv"				):




			"""simple docstring"""


			lowerCAmelCase_						=	get_imdb_top_aaa_movies()
			with open(__lowerCAmelCase    ,							"w"    ,							newline=""				) as out_file:
						lowerCAmelCase_						=	csv.writer(__lowerCAmelCase				)
						writer.writerow(["Movie title", "IMDb rating"]				)
						for title, rating in movies.items():
									writer.writerow([title, rating]				)


if __name__ == "__main__":
			write_movies()



