





from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup






def lowerCamelCase__    (							snake_case_	:  str = ""       )      ->				dict[str, float]:
   __snake_case							       =   url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
   __snake_case							       =   BeautifulSoup(requests.get(snake_case_       ).text       ,      '''html.parser'''       )
   __snake_case							       =   soup.find_all('''td'''       ,      attrs='''titleColumn'''       )
   __snake_case							       =   soup.find_all('''td'''       ,      class_='''ratingColumn imdbRating'''       )
   return {
       title.a.text: float(rating.strong.text       )
       for title, rating in zip(snake_case_       ,      snake_case_       )
   }






def lowerCamelCase__    (							snake_case_	:  str = "IMDb_Top_250_Movies.csv"       )      ->				None:
   __snake_case							       =   get_imdb_top_aaa_movies()
   with open(snake_case_       ,      '''w'''       ,      newline=''''''       ) as out_file:
      __snake_case							       =   csv.writer(snake_case_       )
      writer.writerow(['''Movie title''', '''IMDb rating''']       )
      for title, rating in movies.items():
         writer.writerow([title, rating]       )


if __name__ == "__main__":
    write_movies()








import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


snake_case_				=			logging.getLogger(__name__)

@dataclass(frozen=_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				str
      A_						:				str
      A_						:				Optional[str]							=    None
      A_						:				Optional[str]							=    None
      A_						:				Optional[str]							=    None






@dataclass(frozen=_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				:
      A_						:				List[int]
      A_						:				Optional[List[int]]							=    None
      A_						:				Optional[List[int]]							=    None
      A_						:				Optional[Union[int, float]]							=    None
      A_						:				Optional[int]							=    None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset

    class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
          A_						:				List[InputFeatures]


          def __init__(self       :      int				,			a__       :      str				,			a__       :      PreTrainedTokenizer				,			a__       :      str				,			a__       :      Optional[int] = None				,			a__       :      List[Any]=False				,			a__       :      bool = False				,			):





             """simple docstring"""



             __snake_case							       =   hans_processors[task]()

             __snake_case							       =   os.path.join(
                 a__				,			'''cached_{}_{}_{}_{}'''.format(
                     '''dev''' if evaluate else '''train'''				,			tokenizer.__class__.__name__				,			str(a__      )				,			a__				,			)				,			)
             __snake_case							       =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                __snake_case       ,	__snake_case							       =   label_list[2], label_list[1]
             __snake_case							       =   label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             __snake_case							       =   cached_features_file + '''.lock'''
             with FileLock(a__      ):
                if os.path.exists(a__      ) and not overwrite_cache:
                   logger.info(f"""Loading features from cached file {cached_features_file}"""      )
                   __snake_case							       =   torch.load(a__      )
                else:
                   logger.info(f"""Creating features from dataset file at {data_dir}"""      )

                   __snake_case							       =   (
                       processor.get_dev_examples(a__      ) if evaluate else processor.get_train_examples(a__      )
                   )

                   logger.info('''Training examples: %s'''				,			len(a__      )      )
                   __snake_case							       =   hans_convert_examples_to_features(a__				,			a__				,			a__				,			a__      )
                   logger.info('''Saving features into cached file %s'''				,			a__      )
                   torch.save(self.features				,			a__      )


          def __len__(self       :      int      ):





             """simple docstring"""



             return len(self.features      )


          def __getitem__(self       :      Dict				,			a__       :      List[Any]      ):





             """simple docstring"""



             return self.features[i]


          def    a (self       :      List[Any]      ):





             """simple docstring"""



             return self.label_list


if is_tf_available():
    import tensorflow as tf

    class 		SCREAMING_SNAKE_CASE__				:
          A_						:				List[InputFeatures]


          def __init__(self       :      Tuple				,			a__       :      str				,			a__       :      PreTrainedTokenizer				,			a__       :      str				,			a__       :      Optional[int] = 128				,			a__       :      Any=False				,			a__       :      bool = False				,			):





             """simple docstring"""



             __snake_case							       =   hans_processors[task]()
             __snake_case							       =   processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                # HACK(label indices are swapped in RoBERTa pretrained model)
                __snake_case       ,	__snake_case							       =   label_list[2], label_list[1]
             __snake_case							       =   label_list

             __snake_case							       =   processor.get_dev_examples(a__      ) if evaluate else processor.get_train_examples(a__      )
             __snake_case							       =   hans_convert_examples_to_features(a__				,			a__				,			a__				,			a__      )

             def gen():
                for ex_index, ex in tqdm.tqdm(enumerate(self.features      )				,			desc='''convert examples to features'''      ):
                   if ex_index % 1_0000 == 0:
                      logger.info('''Writing example %d of %d''' % (ex_index, len(a__      ))      )

                   yield (
                       {
                           "example_id": 0,
                           "input_ids": ex.input_ids,
                           "attention_mask": ex.attention_mask,
                           "token_type_ids": ex.token_type_ids,
                       },
                       ex.label,
                   )

             __snake_case							       =   tf.data.Dataset.from_generator(
                 a__				,			(
                     {
                         '''example_id''': tf.intaa,
                         '''input_ids''': tf.intaa,
                         '''attention_mask''': tf.intaa,
                         '''token_type_ids''': tf.intaa,
                     },
                     tf.intaa,
                 )				,			(
                     {
                         '''example_id''': tf.TensorShape([]      ),
                         '''input_ids''': tf.TensorShape([None, None]      ),
                         '''attention_mask''': tf.TensorShape([None, None]      ),
                         '''token_type_ids''': tf.TensorShape([None, None]      ),
                     },
                     tf.TensorShape([]      ),
                 )				,			)


          def    a (self       :      Union[str, Any]      ):





             """simple docstring"""



             return self.dataset


          def __len__(self       :      Dict      ):





             """simple docstring"""



             return len(self.features      )


          def __getitem__(self       :      Any				,			a__       :      Dict      ):





             """simple docstring"""



             return self.features[i]


          def    a (self       :      str      ):





             """simple docstring"""



             return self.label_list

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def    a (self       :      Dict				,			a__       :      Dict      ):





         """simple docstring"""



         return self._create_examples(self._read_tsv(os.path.join(a__				,			'''heuristics_train_set.txt'''      )      )				,			'''train'''      )


      def    a (self       :      Optional[int]				,			a__       :      Tuple      ):





         """simple docstring"""



         return self._create_examples(self._read_tsv(os.path.join(a__				,			'''heuristics_evaluation_set.txt'''      )      )				,			'''dev'''      )


      def    a (self       :      int      ):





         """simple docstring"""



         return ["contradiction", "entailment", "neutral"]


      def    a (self       :      Any				,			a__       :      Optional[int]				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   []
         for i, line in enumerate(a__      ):
            if i == 0:
               continue
            __snake_case							       =   '''%s-%s''' % (set_type, line[0])
            __snake_case							       =   line[5]
            __snake_case							       =   line[6]
            __snake_case							       =   line[7][2:] if line[7].startswith('''ex'''      ) else line[7]
            __snake_case							       =   line[0]
            examples.append(InputExample(guid=a__				,			text_a=a__				,			text_b=a__				,			label=a__				,			pairID=a__      )      )
         return examples






def lowerCamelCase__    (							snake_case_	:  List[InputExample]       ,      snake_case_	:  List[str]       ,      snake_case_	:  int       ,      snake_case_	:  PreTrainedTokenizer       ,      )      ->				List[str]:
   __snake_case							       =   {label: i for i, label in enumerate(snake_case_       )}

   __snake_case							       =   []
   for ex_index, example in tqdm.tqdm(enumerate(snake_case_       )       ,      desc='''convert examples to features'''       ):
      if ex_index % 1_0000 == 0:
         logger.info('''Writing example %d''' % (ex_index)       )

      __snake_case							       =   tokenizer(
          example.text_a       ,      example.text_b       ,      add_special_tokens=snake_case_       ,      max_length=snake_case_       ,      padding='''max_length'''       ,      truncation=snake_case_       ,      return_overflowing_tokens=snake_case_       ,      )

      __snake_case							       =   label_map[example.label] if example.label in label_map else 0

      __snake_case							       =   int(example.pairID       )

      features.append(InputFeatures(**snake_case_       ,      label=snake_case_       ,      pairID=snake_case_       )       )

   for i, example in enumerate(examples[:5]       ):
      logger.info('''*** Example ***'''       )
      logger.info(f"""guid: {example}"""       )
      logger.info(f"""features: {features[i]}"""       )

   return features


snake_case_				=			{
    'hans': 3,
}







snake_case_				=			{
    'hans': HansProcessor,
}

