



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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__		:					Any							=logging.getLogger(__name__)






def      __lowercase       (			a__							,       a__       )    ->			Optional[Any]:
							return (preds == labels).mean()

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




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







@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			str      =  field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()	)}	)
						UpperCamelCase__      :			str      =  field(metadata={'''help''': '''Should contain the data files for the task.'''}	)
						UpperCamelCase__      :			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.'''
						        )
						    }   ,   )
						UpperCamelCase__      :			bool      =  field(
						    default=UpperCamelCase_   ,   metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)







def      __lowercase       (			)    ->			Optional[int]:
							# 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.

							__SCREAMING_SNAKE_CASE									= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= 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 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.local_rank != -1       )							,       training_args.fpaa							,       )
							# 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()
														transformers.utils.logging.enable_default_handler()
														transformers.utils.logging.enable_explicit_format()
							logger.info('Training/evaluation parameters %s'							,       a__       )

							# Set seed
							set_seed(training_args.seed       )

							try:
														__SCREAMING_SNAKE_CASE									= processors[data_args.task_name]()
														__SCREAMING_SNAKE_CASE									= processor.get_labels()
														__SCREAMING_SNAKE_CASE									= len(a__       )
							except KeyError:
														raise ValueError('Task not found: %s' % (data_args.task_name)       )

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

							__SCREAMING_SNAKE_CASE									= AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=a__							,       finetuning_task=data_args.task_name							,       cache_dir=model_args.cache_dir							,       )
							__SCREAMING_SNAKE_CASE									= 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							,       )
							__SCREAMING_SNAKE_CASE									= AutoModelForMultipleChoice.from_pretrained(
							    model_args.model_name_or_path							,       from_tf=bool('.ckpt' in model_args.model_name_or_path       )							,       config=a__							,       cache_dir=model_args.cache_dir							,       )

							# Get datasets
							__SCREAMING_SNAKE_CASE									= (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir							,       tokenizer=a__							,       task=data_args.task_name							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.train							,       )
							    if training_args.do_train
							    else None
							)
							__SCREAMING_SNAKE_CASE									= (
							    MultipleChoiceDataset(
							        data_dir=data_args.data_dir							,       tokenizer=a__							,       task=data_args.task_name							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.dev							,       )
							    if training_args.do_eval
							    else None
							)

							def compute_metrics(a__       ) -> Dict:
														__SCREAMING_SNAKE_CASE									= np.argmax(p.predictions							,       axis=1       )
														return {"acc": simple_accuracy(a__							,       p.label_ids       )}

							# Data collator
							__SCREAMING_SNAKE_CASE									= DataCollatorWithPadding(a__							,       pad_to_multiple_of=8       ) if training_args.fpaa else None

							# Initialize our Trainer
							__SCREAMING_SNAKE_CASE									= Trainer(
							    model=a__							,       args=a__							,       train_dataset=a__							,       eval_dataset=a__							,       compute_metrics=a__							,       data_collator=a__							,       )

							# Training
							if training_args.do_train:
														trainer.train(
														    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
														trainer.save_model()
														# For convenience, we also re-save the tokenizer to the same directory,
														# so that you can share your model easily on huggingface.co/models =)
														if trainer.is_world_master():
																					tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
							__SCREAMING_SNAKE_CASE									= {}
							if training_args.do_eval:
														logger.info('*** Evaluate ***'       )

														__SCREAMING_SNAKE_CASE									= trainer.evaluate()

														__SCREAMING_SNAKE_CASE									= os.path.join(training_args.output_dir							,       'eval_results.txt'       )
														if trainer.is_world_master():
																					with open(a__							,       'w'       ) as writer:
																												logger.info('***** Eval results *****'       )
																												for key, value in result.items():
																																			logger.info('  %s = %s'							,       a__							,       a__       )
																																			writer.write('%s = %s\n' % (key, value)       )

																												results.update(a__       )

							return results






def      __lowercase       (			a__       )    ->			List[str]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
			main()











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






