




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch


@require_torch
@require_tf
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__							=   SMALL_MODEL_IDENTIFIER
           A__							=   '''pt'''
           A__							=   '''tf'''


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Tuple)						->List[str]:





           '''simple docstring'''

           A__							=   AutoModel.from_pretrained(self.test_model)
           model_pt.save_pretrained(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Dict)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   TFAutoModel.from_pretrained(self.test_model ,			from_pt=UpperCAmelCase__)
           model_tf.save_pretrained(UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Dict)						->Dict:





           '''simple docstring'''

           A__							=   '''mock_framework'''

           # Framework provided - return whatever the user provides
           A__							=   FeaturesManager.determine_framework(self.test_model ,			UpperCAmelCase__)
           self.assertEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           # Local checkpoint and framework provided - return provided framework
           # PyTorch checkpoint
           with TemporaryDirectory() as local_pt_ckpt:
                  self._setup_pt_ckpt(UpperCAmelCase__)
                  A__							=   FeaturesManager.determine_framework(UpperCAmelCase__ ,			UpperCAmelCase__)
                  self.assertEqual(UpperCAmelCase__ ,			UpperCAmelCase__)

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                  self._setup_tf_ckpt(UpperCAmelCase__)
                  A__							=   FeaturesManager.determine_framework(UpperCAmelCase__ ,			UpperCAmelCase__)
                  self.assertEqual(UpperCAmelCase__ ,			UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->int:





           '''simple docstring'''

           with TemporaryDirectory() as local_pt_ckpt:
                  self._setup_pt_ckpt(UpperCAmelCase__)
                  A__							=   FeaturesManager.determine_framework(UpperCAmelCase__)
                  self.assertEqual(UpperCAmelCase__ ,			self.framework_pt)

           # TensorFlow checkpoint
           with TemporaryDirectory() as local_tf_ckpt:
                  self._setup_tf_ckpt(UpperCAmelCase__)
                  A__							=   FeaturesManager.determine_framework(UpperCAmelCase__)
                  self.assertEqual(UpperCAmelCase__ ,			self.framework_tf)

           # Invalid local checkpoint
           with TemporaryDirectory() as local_invalid_ckpt:
                  with self.assertRaises(UpperCAmelCase__):
                         A__							=   FeaturesManager.determine_framework(UpperCAmelCase__)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   MagicMock(return_value=UpperCAmelCase__)
           with patch('''transformers.onnx.features.is_tf_available''' ,			UpperCAmelCase__):
                  A__							=   FeaturesManager.determine_framework(self.test_model)
                  self.assertEqual(UpperCAmelCase__ ,			self.framework_pt)

           # PyTorch not in environment -> use TensorFlow
           A__							=   MagicMock(return_value=UpperCAmelCase__)
           with patch('''transformers.onnx.features.is_torch_available''' ,			UpperCAmelCase__):
                  A__							=   FeaturesManager.determine_framework(self.test_model)
                  self.assertEqual(UpperCAmelCase__ ,			self.framework_tf)

           # Both in environment -> use PyTorch
           A__							=   MagicMock(return_value=UpperCAmelCase__)
           A__							=   MagicMock(return_value=UpperCAmelCase__)
           with patch('''transformers.onnx.features.is_tf_available''' ,			UpperCAmelCase__), patch(
               '''transformers.onnx.features.is_torch_available''' ,			UpperCAmelCase__):
                  A__							=   FeaturesManager.determine_framework(self.test_model)
                  self.assertEqual(UpperCAmelCase__ ,			self.framework_pt)

           # Both not in environment -> raise error
           A__							=   MagicMock(return_value=UpperCAmelCase__)
           A__							=   MagicMock(return_value=UpperCAmelCase__)
           with patch('''transformers.onnx.features.is_tf_available''' ,			UpperCAmelCase__), patch(
               '''transformers.onnx.features.is_torch_available''' ,			UpperCAmelCase__):
                  with self.assertRaises(UpperCAmelCase__):
                         A__							=   FeaturesManager.determine_framework(self.test_model)








from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE    (	lowercase_ = ""						)       ->	dict[str, float]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_ = "IMDb_Top_250_Movies.csv"						)       ->	None:



       """simple docstring"""

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


if __name__ == "__main__":
  write_movies()


