




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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						Optional[int] = None	,					)  ->							Optional[Any]:
				UpperCAmelCase_										=					{}

				if train_file is not None:
								UpperCAmelCase_										=					[train_file]
				if eval_file is not None:
								UpperCAmelCase_										=					[eval_file]
				if test_file is not None:
								UpperCAmelCase_										=					[test_file]

				UpperCAmelCase_										=					datasets.load_dataset('''csv'''	,					data_files=_lowerCAmelCase				)
				UpperCAmelCase_										=					list(ds[list(files.keys()				)[0]].features.keys()				)
				UpperCAmelCase_										=					features_name.pop(_lowerCAmelCase				)
				UpperCAmelCase_										=					list(set(ds[list(files.keys()				)[0]][label_name]				)				)
				UpperCAmelCase_										=					{label: i for i, label in enumerate(_lowerCAmelCase				)}
				UpperCAmelCase_										=					tokenizer.model_input_names
				UpperCAmelCase_										=					{}

				if len(_lowerCAmelCase				) == 1:
								for k in files.keys():
												UpperCAmelCase_										=					ds[k].map(
												    lambda __UpperCamelCase				: 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():
												UpperCAmelCase_										=					ds[k].map(
												    lambda __UpperCamelCase				: 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]:
												UpperCAmelCase_										=					{k: v for k, v in ex.items() if k in input_names}
												UpperCAmelCase_										=					labelaid[ex[label_name]]
												yield (d, label)

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

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

				UpperCAmelCase_										=					(
				    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:
								UpperCAmelCase_										=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

				UpperCAmelCase_										=					(
				    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:
								UpperCAmelCase_										=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

				UpperCAmelCase_										=					(
				    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:
								UpperCAmelCase_										=					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       a					:

					'''simple docstring'''





					lowerCAmelCase    :	Optional[int]       		=  field(metadata={'help': 'Which column contains the label'}      )
					lowerCAmelCase    :	Dict       		=  field(default=_a     ,       metadata={'help': 'The path of the training file'}      )
					lowerCAmelCase    :	List[str]       		=  field(default=_a     ,       metadata={'help': 'The path of the development file'}      )
					lowerCAmelCase    :	Tuple       		=  field(default=_a     ,       metadata={'help': 'The path of the test file'}      )
					lowerCAmelCase    :	Optional[Any]       		=  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    :	Any       		=  field(
					    default=_a     ,       metadata={'help': 'Overwrite the cached training and evaluation sets'}      )



@dataclass
class       a					:

					'''simple docstring'''





					lowerCAmelCase    :	Optional[int]       		=  field(
					    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
					lowerCAmelCase    :	Dict       		=  field(
					    default=_a     ,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
					lowerCAmelCase    :	List[Any]       		=  field(
					    default=_a     ,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
					lowerCAmelCase    :	Union[str, Any]       		=  field(default=_a     ,       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[int]       		=  field(
					    default=_a     ,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     ,       )




def  SCREAMING_SNAKE_CASE     (	)  ->							Union[str, Any]:
				# 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.
				UpperCAmelCase_										=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
				UpperCAmelCase_										=					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.

				UpperCAmelCase_										=					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	,					)

				UpperCAmelCase_										=					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	,					)

				UpperCAmelCase_										=					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():
								UpperCAmelCase_										=					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(__UpperCamelCase      :						Dict				) -> Dict:
								UpperCAmelCase_										=					np.argmax(p.predictions	,					axis=1				)

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

				# Initialize our Trainer
				UpperCAmelCase_										=					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
				UpperCAmelCase_										=					{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''				)
								UpperCAmelCase_										=					trainer.evaluate()
								UpperCAmelCase_										=					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 argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCamelCase      			=						logging.getLogger(__name__)

if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser(
     description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
 )
 parser.add_argument(
     '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
 )
 parser.add_argument(
     '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
 )
 parser.add_argument('--vocab_size', default=3_05_22, type=int)
 _lowerCamelCase      			=						parser.parse_args()

 logger.info(F"Loading data from {args.data_file}")
 with open(args.data_file, 'rb') as fp:
  _lowerCamelCase      			=						pickle.load(fp)

 logger.info('Counting occurrences for MLM.')
 _lowerCamelCase      			=						Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 _lowerCamelCase      			=						[0] * args.vocab_size
 for k, v in counter.items():
  _lowerCamelCase      			=						v

 logger.info(F"Dump to {args.token_counts_dump}")
 with open(args.token_counts_dump, 'wb') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





