






"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


A_			: Any				=			logging.getLogger(__name__)



@dataclass
class 		a_      :







      '''simple docstring'''

      lowerCamelCase__       :				str =	field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
      lowerCamelCase__       :				Optional[str] =	field(
          default=snake_case_  ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
      lowerCamelCase__       :				Optional[str] =	field(
          default='NER'  ,						metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'} )
      lowerCamelCase__       :				Optional[str] =	field(
          default=snake_case_  ,						metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
      lowerCamelCase__       :				bool =	field(default=snake_case_  ,						metadata={'help': 'Set this flag to use fast tokenization.'} )
      # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
      # or just modify its tokenizer_config.json.
      lowerCamelCase__       :				Optional[str] =	field(
          default=snake_case_  ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,						)



@dataclass
class 		a_      :







      '''simple docstring'''

      lowerCamelCase__       :				str =	field(
          metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'} )
      lowerCamelCase__       :				Optional[str] =	field(
          metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'} )
      lowerCamelCase__       :				int =	field(
          default=128  ,						metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }  ,						)
      lowerCamelCase__       :				bool =	field(
          default=snake_case_  ,						metadata={'help': 'Overwrite the cached training and evaluation sets'} )






def        lowerCamelCase_  (						):
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.
 lowerCamelCase__     :			int     				=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
 lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__     :			str     				=						parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir				)
     and os.listdir(training_args.output_dir				)
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
      ' --overwrite_output_dir to overcome.'				)

 lowerCamelCase__     :			Union[str, Any]     				=						import_module('tasks'				)

 try:
  lowerCamelCase__     :			Tuple     				=						getattr(_lowerCamelCase			,   model_args.task_type				)
  lowerCamelCase__     :			TokenClassificationTask     				=						token_classification_task_clazz()
 except AttributeError:
  raise ValueError(
      f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
      f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''				)

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'			,   datefmt='%m/%d/%Y %H:%M:%S'			,   level=logging.INFO			,   )
 logger.info(
     'n_replicas: %s, distributed training: %s, 16-bits training: %s'			,   training_args.n_replicas			,   bool(training_args.n_replicas > 1				)			,   training_args.fpaa			,   )
 logger.info('Training/evaluation parameters %s'			,   _lowerCamelCase				)

 # Prepare Token Classification task
 lowerCamelCase__     :			Optional[int]     				=						token_classification_task.get_labels(data_args.labels				)
 lowerCamelCase__     :			Dict[int, str]     				=						dict(enumerate(_lowerCamelCase				)				)
 lowerCamelCase__     :			Optional[int]     				=						len(_lowerCamelCase				)

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 lowerCamelCase__     :			Any     				=						AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path			,   num_labels=_lowerCamelCase			,   idalabel=_lowerCamelCase			,   labelaid={label: i for i, label in enumerate(_lowerCamelCase				)}			,   cache_dir=model_args.cache_dir			,   )
 lowerCamelCase__     :			Optional[Any]     				=						AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,   cache_dir=model_args.cache_dir			,   use_fast=model_args.use_fast			,   )

 with training_args.strategy.scope():
  lowerCamelCase__     :			str     				=						TFAutoModelForTokenClassification.from_pretrained(
      model_args.model_name_or_path			,   from_pt=bool('.bin' in model_args.model_name_or_path				)			,   config=_lowerCamelCase			,   cache_dir=model_args.cache_dir			,   )

 # Get datasets
 lowerCamelCase__     :			Optional[Any]     				=						(
     TFTokenClassificationDataset(
         token_classification_task=_lowerCamelCase			,   data_dir=data_args.data_dir			,   tokenizer=_lowerCamelCase			,   labels=_lowerCamelCase			,   model_type=config.model_type			,   max_seq_length=data_args.max_seq_length			,   overwrite_cache=data_args.overwrite_cache			,   mode=Split.train			,   )
     if training_args.do_train
     else None
 )
 lowerCamelCase__     :			Dict     				=						(
     TFTokenClassificationDataset(
         token_classification_task=_lowerCamelCase			,   data_dir=data_args.data_dir			,   tokenizer=_lowerCamelCase			,   labels=_lowerCamelCase			,   model_type=config.model_type			,   max_seq_length=data_args.max_seq_length			,   overwrite_cache=data_args.overwrite_cache			,   mode=Split.dev			,   )
     if training_args.do_eval
     else None
 )

 def align_predictions(_lowerCamelCase			,   _lowerCamelCase				) -> Tuple[List[int], List[int]]:
  lowerCamelCase__     :			List[str]     				=						np.argmax(_lowerCamelCase			,   axis=2				)
  lowerCamelCase__				,     lowerCamelCase__     :			str     				=						preds.shape
  lowerCamelCase__     :			Tuple     				=						[[] for _ in range(_lowerCamelCase				)]
  lowerCamelCase__     :			List[str]     				=						[[] for _ in range(_lowerCamelCase				)]

  for i in range(_lowerCamelCase				):
   for j in range(_lowerCamelCase				):
    if label_ids[i, j] != -100:
     out_label_list[i].append(label_map[label_ids[i][j]]				)
     preds_list[i].append(label_map[preds[i][j]]				)

  return preds_list, out_label_list

 def compute_metrics(_lowerCamelCase				) -> Dict:
  lowerCamelCase__				,     lowerCamelCase__     :			int     				=						align_predictions(p.predictions			,   p.label_ids				)

  return {
      "precision": precision_score(_lowerCamelCase			,   _lowerCamelCase				),
      "recall": recall_score(_lowerCamelCase			,   _lowerCamelCase				),
      "f1": fa_score(_lowerCamelCase			,   _lowerCamelCase				),
  }

 # Initialize our Trainer
 lowerCamelCase__     :			Union[str, Any]     				=						TFTrainer(
     model=_lowerCamelCase			,   args=_lowerCamelCase			,   train_dataset=train_dataset.get_dataset() if train_dataset else None			,   eval_dataset=eval_dataset.get_dataset() if eval_dataset else None			,   compute_metrics=_lowerCamelCase			,   )

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir				)

 # Evaluation
 lowerCamelCase__     :			Any     				=						{}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'				)

  lowerCamelCase__     :			List[str]     				=						trainer.evaluate()
  lowerCamelCase__     :			Dict     				=						os.path.join(training_args.output_dir			,   'eval_results.txt'				)

  with open(_lowerCamelCase			,   'w'				) as writer:
   logger.info('***** Eval results *****'				)

   for key, value in result.items():
    logger.info('  %s = %s'			,   _lowerCamelCase			,   _lowerCamelCase				)
    writer.write('%s = %s\n' % (key, value)				)

   results.update(_lowerCamelCase				)

    # Predict
 if training_args.do_predict:
  lowerCamelCase__     :			Union[str, Any]     				=						TFTokenClassificationDataset(
      token_classification_task=_lowerCamelCase			,   data_dir=data_args.data_dir			,   tokenizer=_lowerCamelCase			,   labels=_lowerCamelCase			,   model_type=config.model_type			,   max_seq_length=data_args.max_seq_length			,   overwrite_cache=data_args.overwrite_cache			,   mode=Split.test			,   )

  lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__     :			Optional[int]     				=						trainer.predict(test_dataset.get_dataset()				)
  lowerCamelCase__				,     lowerCamelCase__     :			Tuple     				=						align_predictions(_lowerCamelCase			,   _lowerCamelCase				)
  lowerCamelCase__     :			Dict     				=						classification_report(_lowerCamelCase			,   _lowerCamelCase				)

  logger.info('\n%s'			,   _lowerCamelCase				)

  lowerCamelCase__     :			List[Any]     				=						os.path.join(training_args.output_dir			,   'test_results.txt'				)

  with open(_lowerCamelCase			,   'w'				) as writer:
   writer.write('%s\n' % report				)

  # Save predictions
  lowerCamelCase__     :			Tuple     				=						os.path.join(training_args.output_dir			,   'test_predictions.txt'				)

  with open(_lowerCamelCase			,   'w'				) as writer:
   with open(os.path.join(data_args.data_dir			,   'test.txt'				)			,   'r'				) as f:
    lowerCamelCase__     :			Dict     				=						0

    for line in f:
     if line.startswith('-DOCSTART-'				) or line == "" or line == "\n":
      writer.write(_lowerCamelCase				)

      if not preds_list[example_id]:
       example_id += 1
     elif preds_list[example_id]:
      lowerCamelCase__     :			Union[str, Any]     				=						line.split()[0] + ' ' + preds_list[example_id].pop(0				) + '\n'

      writer.write(_lowerCamelCase				)
     else:
      logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'			,   line.split()[0]				)

 return results


if __name__ == "__main__":
      main()









"""simple docstring"""
import bsa
import requests






def        lowerCamelCase_  (						_lowerCamelCase				):
 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        lowerCamelCase_  (						_lowerCamelCase = 5				):
 lowerCamelCase__     :			Optional[int]     				=						int(float(_lowerCamelCase				)				)
 if num_movies < 1:
  return ()
 lowerCamelCase__     :			Any     				=						(
     'https://www.imdb.com/search/title?title_type='
     f'''feature&sort=num_votes,desc&count={num_movies}'''
 )
 lowerCamelCase__     :			Union[str, Any]     				=						bsa.BeautifulSoup(requests.get(_lowerCamelCase				).content			,   'html.parser'				)
 return tuple(
     get_movie_data_from_soup(_lowerCamelCase				)
     for movie in source.find_all('div'			,   class_='lister-item mode-advanced'				)				)


if __name__ == "__main__":
      import json

      A_			: 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)
          )
      )

