




'''simple docstring'''




import requests
from bsa import BeautifulSoup





def      a_							(      __snake_case							:			str = "AAPL"   )							->						str:



       """simple docstring"""
       lowerCamelCase_							=F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
       lowerCamelCase_							=BeautifulSoup(requests.get(__snake_case   ).text   ,       '''html.parser'''   )
       lowerCamelCase_							='''My(6px) Pos(r) smartphone_Mt(6px)'''
       return soup.find('''div'''   ,       class_=class_   ).find('''span'''   ).text


if __name__ == "__main__":
     for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
          print(F"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")








'''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,
)


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





@dataclass
class      snake_case    :





  """simple docstring"""




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





@dataclass
class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  field(
      metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}			)
  _lowerCamelCase		    =  field(
      default="summarization"			,						metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}			,						)
  _lowerCamelCase		    =  field(
      default=10_24			,						metadata={
          "help": (
              "The maximum total input sequence length after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_28			,						metadata={
          "help": (
              "The maximum total sequence length for target text after tokenization. Sequences longer "
              "than this will be truncated, sequences shorter will be padded."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_42			,						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``."
          )
      }			,						)
  _lowerCamelCase		    =  field(
      default=1_42			,						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."
          )
      }			,						)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# training examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# validation examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=-1			,						metadata={"help": "# test examples. -1 means use all."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Source language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "Target language id for translation."}			)
  _lowerCamelCase		    =  field(default=lowercase			,						metadata={"help": "# num_beams to use for evaluation."}			)
  _lowerCamelCase		    =  field(
      default=lowercase			,						metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}			,						)





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						List[Any]		):
 logger.info(F'''***** {split} metrics *****'''		)
 for key in sorted(metrics.keys()		):
  logger.info(F'''  {key} = {metrics[key]}'''		)
 save_json(UpperCAmelCase_					,				os.path.join(UpperCAmelCase_					,				F'''{split}_results.json'''		)		)





def __snake_case					(      ):
 # 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(UpperCAmelCase_		)

 # 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"					,				UpperCAmelCase_		)

 # 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(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_		):
   assert hasattr(UpperCAmelCase_					,				UpperCAmelCase_		), F'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
   setattr(UpperCAmelCase_					,				UpperCAmelCase_					,				getattr(UpperCAmelCase_					,				UpperCAmelCase_		)		)

 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=UpperCAmelCase_					,				cache_dir=model_args.cache_dir					,				)

 # use task specific params
 use_task_specific_params(UpperCAmelCase_					,				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(UpperCAmelCase_					,				(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(UpperCAmelCase_					,				UpperCAmelCase_		):
   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(UpperCAmelCase_		)
 if model_args.freeze_encoder:
  freeze_params(model.get_encoder()		)
  assert_all_frozen(model.get_encoder()		)

 lowerCamelCase_      =  SeqaSeqDataset

 # Get datasets
 lowerCamelCase_      =  (
     dataset_class(
         UpperCAmelCase_					,				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(
         UpperCAmelCase_					,				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(
         UpperCAmelCase_					,				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					,				UpperCAmelCase_		) if training_args.predict_with_generate else None
 )
 lowerCamelCase_      =  SeqaSeqTrainer(
     model=UpperCAmelCase_					,				args=UpperCAmelCase_					,				data_args=UpperCAmelCase_					,				train_dataset=UpperCAmelCase_					,				eval_dataset=UpperCAmelCase_					,				data_collator=SeqaSeqDataCollator(
         UpperCAmelCase_					,				UpperCAmelCase_					,				model.config.decoder_start_token_id					,				training_args.tpu_num_cores		)					,				compute_metrics=UpperCAmelCase_					,				tokenizer=UpperCAmelCase_					,				)

 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"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

   # 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"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

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

  lowerCamelCase_      =  trainer.predict(test_dataset=UpperCAmelCase_					,				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"					,				UpperCAmelCase_					,				training_args.output_dir		)
   all_metrics.update(UpperCAmelCase_		)

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

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

 return all_metrics





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
     main()



