
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


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







def 					_UpperCamelCase		(    )   ->				Tuple:


		'''simple docstring'''


		snake_case   :    Union[str, Any]			       =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

		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.
				snake_case       ,snake_case       ,snake_case   :    List[Any]			       =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
		else:
				snake_case       ,snake_case       ,snake_case   :    Tuple			       =		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'''      ,    SCREAMING_SNAKE_CASE__       )

		# Prepare Question-Answering task
		# 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[str]			       =		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      ,    )
		snake_case   :    Optional[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      ,    use_fast=SCREAMING_SNAKE_CASE__      ,    )
		snake_case   :    Tuple			       =		AutoModelForQuestionAnswering.from_pretrained(
		    model_args.model_name_or_path      ,    from_tf=bool('''.ckpt''' in model_args.model_name_or_path       )      ,    config=SCREAMING_SNAKE_CASE__      ,    cache_dir=model_args.cache_dir      ,    )

		# Get datasets
		snake_case   :    str			       =		hasattr(model.config      ,    '''lang2id'''       )
		snake_case   :    Union[str, Any]			       =		(
		    SquadDataset(
		        SCREAMING_SNAKE_CASE__      ,    tokenizer=SCREAMING_SNAKE_CASE__      ,    is_language_sensitive=SCREAMING_SNAKE_CASE__      ,    cache_dir=model_args.cache_dir       )
		    if training_args.do_train
		    else None
		)
		snake_case   :    int			       =		(
		    SquadDataset(
		        SCREAMING_SNAKE_CASE__      ,    tokenizer=SCREAMING_SNAKE_CASE__      ,    mode='''dev'''      ,    is_language_sensitive=SCREAMING_SNAKE_CASE__      ,    cache_dir=model_args.cache_dir      ,    )
		    if training_args.do_eval
		    else None
		)

		# Data collator
		snake_case   :    str			       =		DataCollatorWithPadding(SCREAMING_SNAKE_CASE__      ,    pad_to_multiple_of=8       ) if training_args.fpaa else None

		# Initialize our Trainer
		snake_case   :    Dict			       =		Trainer(
		    model=SCREAMING_SNAKE_CASE__      ,    args=SCREAMING_SNAKE_CASE__      ,    train_dataset=SCREAMING_SNAKE_CASE__      ,    eval_dataset=SCREAMING_SNAKE_CASE__      ,    data_collator=SCREAMING_SNAKE_CASE__      ,    )

		# 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       )




def 					_UpperCamelCase		(    SCREAMING_SNAKE_CASE__       )   ->				List[str]:


		'''simple docstring'''


		main()


if __name__ == "__main__":
						main()




'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowercase__		    =	get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		tempfile.mkdtemp()

				snake_case   :    Optional[int]			       =		SpeechTaTokenizer(UpperCamelCase__	)
				tokenizer.save_pretrained(self.tmpdirname	)

				snake_case   :    Any			       =		{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1e-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				snake_case   :    Tuple			       =		os.path.join(self.tmpdirname   ,     UpperCamelCase__	)
				with open(self.feature_extraction_file   ,     '''w'''   ,     encoding='''utf-8'''	) as fp:
						fp.write(json.dumps(UpperCamelCase__	) + '''\n'''	)



		def 	lowerCAmelCase							(	self							:       Dict   ,     **UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       int	)		->       Optional[Any]:






				"""simple docstring"""







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Any:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_tokenizer()
				snake_case   :    List[str]			       =		self.get_feature_extractor()

				snake_case   :    Dict			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				processor.save_pretrained(self.tmpdirname	)
				snake_case   :    Dict			       =		SpeechTaProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    int			       =		SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				snake_case   :    int			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''	)
				snake_case   :    str			       =		self.get_feature_extractor(do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				snake_case   :    Tuple			       =		SpeechTaProcessor.from_pretrained(
				    self.tmpdirname   ,     bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''   ,     do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    List[str]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Union[str, Any]			       =		floats_list((3, 1000)	)

				snake_case   :    List[str]			       =		feature_extractor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    Any			       =		processor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2	)



		def 	lowerCAmelCase							(	self							:       Dict	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_feature_extractor()
				snake_case   :    Optional[int]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		floats_list((3, 1000)	)

				snake_case   :    str			       =		feature_extractor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    List[Any]			       =		processor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)

				for key in input_feat_extract.keys():
						self.assertAlmostEqual(input_feat_extract[key].sum()   ,     input_processor[key].sum()   ,     delta=1e-2	)



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    Union[str, Any]			       =		self.get_tokenizer()

				snake_case   :    List[str]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		'''This is a test string'''

				snake_case   :    Dict			       =		processor(text=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    str			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		'''This is a test string'''

				snake_case   :    Any			       =		processor(text_target=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

				for key in encoded_tok.keys():
						self.assertListEqual(encoded_tok[key]   ,     encoded_processor[key]	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    Optional[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[int]			       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				snake_case   :    List[str]			       =		processor.batch_decode(UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer.batch_decode(UpperCamelCase__	)

				self.assertListEqual(UpperCamelCase__   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    Tuple			       =		self.get_tokenizer()

				snake_case   :    List[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				self.assertListEqual(
				    processor.model_input_names   ,     feature_extractor.model_input_names   ,     msg='''`processor` and `feature_extractor` model input names do not match'''   ,     )


