

"""simple docstring"""




import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('''Googling.....''')
     SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			'''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			requests.get(url, headers={'''UserAgent''': UserAgent().random})
     # res.raise_for_status()
     with open('''project1a.html''', '''wb''') as out_file:  # only for knowing the class
          for data in res.iter_content(1_0000):
               out_file.write(data)
     SCREAMING_SNAKE_CASE_     :			Dict	  =			BeautifulSoup(res.text, '''html.parser''')
     SCREAMING_SNAKE_CASE_     :			List[Any]	  =			list(soup.select('''.eZt8xd'''))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('''href'''))
          else:
               webbrowser.open(F'''https://google.com{link.get('href')}''')







"""simple docstring"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.getLogger(__name__)


@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,       )
      __a								=	field(default=__a				,       metadata={'help': 'Whether tp freeze the encoder.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Whether  to freeze the embeddings.'}					)






@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
      __a								=	field(
          default='summarization'				,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,       )
      __a								=	field(
          default=1024				,       metadata={
              'help': (
                  'The maximum total input sequence length after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=128				,       metadata={
              'help': (
                  'The maximum total sequence length for target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded. '
                  'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                  'during ``evaluate`` and ``predict``.'
              )
          }				,       )
      __a								=	field(
          default=142				,       metadata={
              'help': (
                  'The maximum total sequence length for test target text after tokenization. Sequences longer '
                  'than this will be truncated, sequences shorter will be padded.'
              )
          }				,       )
      __a								=	field(default=-1				,       metadata={'help': '# training examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# validation examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# test examples. -1 means use all.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Source language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Target language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': '# num_beams to use for evaluation.'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}				,       )






def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Union[str, Any]:




   """simple docstring"""
   logger.info(f'***** {split} metrics *****'   )
   for key in sorted(metrics.keys()   ):
      logger.info(f'  {key} = {metrics[key]}'   )
   save_json(A__	,    os.path.join(A__	,    f'{split}_results.json'   )   )



def 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   # 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__						=			HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)   )

   if len(sys.argv   ) == 2 and sys.argv[1].endswith(".json"   ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_args_into_dataclasses()

   check_output_dir(A__   )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,    datefmt="%m/%d/%Y %H:%M:%S"	,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN	,    )
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"	,    training_args.local_rank	,    training_args.device	,    training_args.n_gpu	,    bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED   )	,    training_args.fpaa	,    )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank   ):
      transformers.utils.logging.set_verbosity_info()
   logger.info("Training/evaluation parameters %s"	,    A__   )

   # Set seed
   set_seed(training_args.seed   )

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

   lowerCamelCase__						=			AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path	,    cache_dir=model_args.cache_dir	,    )

   lowerCamelCase__						=			("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(A__	,    A__	,    A__   ):
         assert hasattr(A__	,    A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
         setattr(A__	,    A__	,    getattr(A__	,    A__   )   )

   lowerCamelCase__						=			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	,    )
   lowerCamelCase__						=			AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path	,    from_tf=".ckpt" in model_args.model_name_or_path	,    config=A__	,    cache_dir=model_args.cache_dir	,    )

   # use task specific params
   use_task_specific_params(A__	,    data_args.task   )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      lowerCamelCase__						=			model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(A__	,    (MBartTokenizer, MBartTokenizerFast)   ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(A__	,    A__   ):
         lowerCamelCase__						=			tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

   if model_args.freeze_embeds:
      freeze_embeds(A__   )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()   )
      assert_all_frozen(model.get_encoder()   )

   lowerCamelCase__						=			SeqaSeqDataset

   # Get datasets
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="train"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_train	,    max_target_length=data_args.max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_train
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="val"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_val	,    max_target_length=data_args.val_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   lowerCamelCase__						=			(
       dataset_class(
           A__	,    type_path="test"	,    data_dir=data_args.data_dir	,    n_obs=data_args.n_test	,    max_target_length=data_args.test_max_target_length	,    max_source_length=data_args.max_source_length	,    prefix=model.config.prefix or ""	,    )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   lowerCamelCase__						=			(
       build_compute_metrics_fn(data_args.task	,    A__   ) if training_args.predict_with_generate else None
   )
   lowerCamelCase__						=			SeqaSeqTrainer(
       model=A__	,    args=A__	,    data_args=A__	,    train_dataset=A__	,    eval_dataset=A__	,    data_collator=SeqaSeqDataCollator(
           A__	,    A__	,    model.config.decoder_start_token_id	,    training_args.tpu_num_cores   )	,    compute_metrics=A__	,    tokenizer=A__	,    )

   lowerCamelCase__						=			{}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"   )

      lowerCamelCase__						=			trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
      lowerCamelCase__						=			train_result.metrics
      lowerCamelCase__						=			data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir	,    "trainer_state.json"   )   )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir   )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"   )

      lowerCamelCase__						=			trainer.evaluate(metric_key_prefix="val"   )
      lowerCamelCase__						=			data_args.n_val
      lowerCamelCase__						=			round(metrics["val_loss"]	,    4   )

      if trainer.is_world_process_zero():
         handle_metrics("val"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

   if training_args.do_predict:
      logger.info("*** Predict ***"   )

      lowerCamelCase__						=			trainer.predict(test_dataset=A__	,    metric_key_prefix="test"   )
      lowerCamelCase__						=			test_output.metrics
      lowerCamelCase__						=			data_args.n_test

      if trainer.is_world_process_zero():
         lowerCamelCase__						=			round(metrics["test_loss"]	,    4   )
         handle_metrics("test"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         if training_args.predict_with_generate:
            lowerCamelCase__						=			tokenizer.batch_decode(
                test_output.predictions	,    skip_special_tokens=A__	,    clean_up_tokenization_spaces=A__   )
            lowerCamelCase__						=			lmap(str.strip	,    A__   )
            write_txt_file(A__	,    os.path.join(training_args.output_dir	,    "test_generations.txt"   )   )

   if trainer.is_world_process_zero():
      save_json(A__	,    os.path.join(training_args.output_dir	,    "all_results.json"   )   )

   return all_metrics



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()




