





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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    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()


def     lowerCamelCase_	(      lowerCAmelCase:	str    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	PreTrainedTokenizer    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	Optional[int] = None    ,							)->		str:
  _snake_case   :				Any														=	{}

  if train_file is not None:
    _snake_case   :				Tuple														=	[train_file]
  if eval_file is not None:
    _snake_case   :				Optional[Any]														=	[eval_file]
  if test_file is not None:
    _snake_case   :				Union[str, Any]														=	[test_file]

  _snake_case   :				Any														=	datasets.load_dataset('csv'    ,							data_files=lowerCAmelCase				)
  _snake_case   :				Dict														=	list(ds[list(files.keys()				)[0]].features.keys()				)
  _snake_case   :				Dict														=	features_name.pop(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	list(set(ds[list(files.keys()				)[0]][label_name]				)				)
  _snake_case   :				Union[str, Any]														=	{label: i for i, label in enumerate(lowerCAmelCase				)}
  _snake_case   :				Tuple														=	tokenizer.model_input_names
  _snake_case   :				Dict														=	{}

  if len(lowerCAmelCase				) == 1:
    for k in files.keys():
      _snake_case   :				Tuple														=	ds[k].map(
          lambda lowerCAmelCase				: tokenizer.batch_encode_plus(
              example[features_name[0]]    ,							truncation=lowerCAmelCase    ,							max_length=lowerCAmelCase    ,							padding='max_length'				)    ,							batched=lowerCAmelCase    ,							)
  elif len(lowerCAmelCase				) == 2:
    for k in files.keys():
      _snake_case   :				Dict														=	ds[k].map(
          lambda lowerCAmelCase				: tokenizer.batch_encode_plus(
              (example[features_name[0]], example[features_name[1]])    ,							truncation=lowerCAmelCase    ,							max_length=lowerCAmelCase    ,							padding='max_length'    ,							)    ,							batched=lowerCAmelCase    ,							)

  def gen_train():
    for ex in transformed_ds[datasets.Split.TRAIN]:
      _snake_case   :				Optional[Any]														=	{k: v for k, v in ex.items() if k in input_names}
      _snake_case   :				Optional[Any]														=	labelaid[ex[label_name]]
      yield (d, label)

  def gen_val():
    for ex in transformed_ds[datasets.Split.VALIDATION]:
      _snake_case   :				Tuple														=	{k: v for k, v in ex.items() if k in input_names}
      _snake_case   :				Any														=	labelaid[ex[label_name]]
      yield (d, label)

  def gen_test():
    for ex in transformed_ds[datasets.Split.TEST]:
      _snake_case   :				Any														=	{k: v for k, v in ex.items() if k in input_names}
      _snake_case   :				Any														=	labelaid[ex[label_name]]
      yield (d, label)

  _snake_case   :				Any														=	(
      tf.data.Dataset.from_generator(
          lowerCAmelCase    ,							({k: tf.intaa for k in input_names}, tf.intaa)    ,							({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))    ,							)
      if datasets.Split.TRAIN in transformed_ds
      else None
  )

  if train_ds is not None:
    _snake_case   :				Union[str, Any]														=	train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

  _snake_case   :				Optional[int]														=	(
      tf.data.Dataset.from_generator(
          lowerCAmelCase    ,							({k: tf.intaa for k in input_names}, tf.intaa)    ,							({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))    ,							)
      if datasets.Split.VALIDATION in transformed_ds
      else None
  )

  if val_ds is not None:
    _snake_case   :				List[Any]														=	val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

  _snake_case   :				List[str]														=	(
      tf.data.Dataset.from_generator(
          lowerCAmelCase    ,							({k: tf.intaa for k in input_names}, tf.intaa)    ,							({k: tf.TensorShape([None]				) for k in input_names}, tf.TensorShape([]				))    ,							)
      if datasets.Split.TEST in transformed_ds
      else None
  )

  if test_ds is not None:
    _snake_case   :				List[Any]														=	test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

  return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase_						   =    logging.getLogger(__name__)



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       int      					=field(metadata={"""help""": """Which column contains the label"""}							)
  a_							:       str      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """The path of the training file"""}							)
  a_							:       Optional[str]      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """The path of the development file"""}							)
  a_							:       Optional[str]      					=field(default=UpperCAmelCase_							,    metadata={"""help""": """The path of the test file"""}							)
  a_							:       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."""
          )
      }							,    )
  a_							:       bool      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)




@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       str      					=field(
      metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
  a_							:       bool      					=field(default=UpperCAmelCase_							,    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.
  a_							:       Optional[str]      					=field(
      default=UpperCAmelCase_							,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,    )




def     lowerCamelCase_	(      )->		Dict:
  # 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.
  _snake_case   :				Optional[int]														=	HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
  _snake_case							,					_snake_case							,					_snake_case   :				List[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.'				)

  # 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(
      F"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1				)}, """
      F"""16-bits training: {training_args.fpaa}"""				)
  logger.info(F"""Training/evaluation parameters {training_args}"""				)

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

  _snake_case   :				List[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    ,							)

  _snake_case							,					_snake_case							,					_snake_case							,					_snake_case   :				Tuple														=	get_tfds(
      train_file=data_args.train_file    ,							eval_file=data_args.dev_file    ,							test_file=data_args.test_file    ,							tokenizer=lowerCAmelCase    ,							label_column_id=data_args.label_column_id    ,							max_seq_length=data_args.max_seq_length    ,							)

  _snake_case   :				Any														=	AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,							num_labels=len(lowerCAmelCase				)    ,							labelaid=lowerCAmelCase    ,							idalabel={id: label for label, id in labelaid.items()}    ,							finetuning_task='text-classification'    ,							cache_dir=model_args.cache_dir    ,							)

  with training_args.strategy.scope():
    _snake_case   :				List[Any]														=	TFAutoModelForSequenceClassification.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    ,							)

  def compute_metrics(lowerCAmelCase:	EvalPrediction				) -> Dict:
    _snake_case   :				str														=	np.argmax(p.predictions    ,							axis=1				)

    return {"acc": (preds == p.label_ids).mean()}

  # Initialize our Trainer
  _snake_case   :				Dict														=	TFTrainer(
      model=lowerCAmelCase    ,							args=lowerCAmelCase    ,							train_dataset=lowerCAmelCase    ,							eval_dataset=lowerCAmelCase    ,							compute_metrics=lowerCAmelCase    ,							)

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

  # Evaluation
  _snake_case   :				Any														=	{}
  if training_args.do_eval:
    logger.info('*** Evaluate ***'				)
    _snake_case   :				Dict														=	trainer.evaluate()
    _snake_case   :				List[Any]														=	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(F"""  {key} = {value}"""				)
        writer.write(F"""{key} = {value}\n"""				)

      results.update(lowerCAmelCase				)

  return results


if __name__ == "__main__":
      main()











import csv

import tweepy

# Twitter API credentials
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""
lowerCAmelCase_						   =    """"""


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		None:
  # authorize twitter, initialize tweepy
  _snake_case   :				Optional[Any]														=	tweepy.OAuthHandler(lowerCAmelCase    ,							lowerCAmelCase				)
  auth.set_access_token(lowerCAmelCase    ,							lowerCAmelCase				)
  _snake_case   :				List[Any]														=	tweepy.API(lowerCAmelCase				)

  # initialize a list to hold all the tweepy Tweets
  _snake_case   :				Any														=	[]

  # make initial request for most recent tweets (200 is the maximum allowed count)
  _snake_case   :				List[str]														=	api.user_timeline(screen_name=lowerCAmelCase    ,							count=2_00				)

  # save most recent tweets
  alltweets.extend(lowerCAmelCase				)

  # save the id of the oldest tweet less one
  _snake_case   :				List[Any]														=	alltweets[-1].id - 1

  # keep grabbing tweets until there are no tweets left to grab
  while len(lowerCAmelCase				) > 0:
    print(F"""getting tweets before {oldest}"""				)

    # all subsequent requests use the max_id param to prevent duplicates
    _snake_case   :				Tuple														=	api.user_timeline(
        screen_name=lowerCAmelCase    ,							count=2_00    ,							max_id=lowerCAmelCase				)

    # save most recent tweets
    alltweets.extend(lowerCAmelCase				)

    # update the id of the oldest tweet less one
    _snake_case   :				List[str]														=	alltweets[-1].id - 1

    print(F"""...{len(lowerCAmelCase				)} tweets downloaded so far"""				)

  # transform the tweepy tweets into a 2D array that will populate the csv
  _snake_case   :				int														=	[[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

  # write the csv
  with open(F"""new_{screen_name}_tweets.csv"""    ,							'w'				) as f:
    _snake_case   :				Any														=	csv.writer(lowerCAmelCase				)
    writer.writerow(['id', 'created_at', 'text']				)
    writer.writerows(lowerCAmelCase				)


if __name__ == "__main__":
      # pass in the username of the account you want to download
      get_all_tweets("""FirePing32""")




