






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 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_ = None	,     ):






				'''simple docstring'''
				lowercase__     				=    {}

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

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

				if len(lowerCamelCase_       ) == 1:
								for k in files.keys():
												lowercase__     				=    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():
												lowercase__     				=    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]:
												lowercase__     				=    {k: v for k, v in ex.items() if k in input_names}
												lowercase__     				=    labelaid[ex[label_name]]
												yield (d, label)

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

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

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

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

				lowercase__     				=    (
				    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:
								lowercase__     				=    test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]       )       )       )

				return train_ds, val_ds, test_ds, labelaid


A__			:    List[Any]         =				logging.getLogger(__name__)
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= field(metadata={"""help""": """Which column contains the label"""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """The path of the training file"""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """The path of the development file"""}			)
				lowercase__       							= field(default=A__	,metadata={"""help""": """The path of the test file"""}			)
				lowercase__       							= 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."""
				        )
				    }	,)
				lowercase__       							= field(
				    default=A__	,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)



@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




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






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)       )
				lowercase__				,		lowercase__				,		lowercase__     				=    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.

				lowercase__     				=    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	,     )

				lowercase__				,		lowercase__				,		lowercase__				,		lowercase__     				=    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	,     )

				lowercase__     				=    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():
								lowercase__     				=    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_       ) -> Dict:
								lowercase__     				=    np.argmax(p.predictions	,     axis=1       )

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

				# Initialize our Trainer
				lowercase__     				=    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
				lowercase__     				=    {}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***'''       )
								lowercase__     				=    trainer.evaluate()
								lowercase__     				=    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 json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_	,     '''wb'''       ) as f:
								f.write(json.dumps(lowerCamelCase_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_map_filter()




