




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase=1024):
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= [], []

			SCREAMING_SNAKE_CASE						= list(zip(_UpperCAmelCase							,       _UpperCAmelCase))
			SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= sorted_examples[0]

			def is_too_big(_UpperCAmelCase):
						return tok(_UpperCAmelCase							,       return_tensors='pt').input_ids.shape[1] > max_tokens

			for src, tgt in tqdm(sorted_examples[1:]):
						SCREAMING_SNAKE_CASE						= new_src + ' ' + src
						SCREAMING_SNAKE_CASE						= new_tgt + ' ' + tgt
						if is_too_big(_UpperCAmelCase) or is_too_big(_UpperCAmelCase):  # cant fit, finalize example
									finished_src.append(_UpperCAmelCase)
									finished_tgt.append(_UpperCAmelCase)
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= src, tgt
						else:  # can fit, keep adding
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= cand_src, cand_tgt

    # cleanup
			if new_src:
						assert new_tgt
						finished_src.append(_UpperCAmelCase)
						finished_tgt.append(_UpperCAmelCase)
			return finished_src, finished_tgt






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= Path(_UpperCAmelCase)
			save_path.mkdir(exist_ok=_UpperCAmelCase)
			for split in ["train"]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
						SCREAMING_SNAKE_CASE						= [x.rstrip() for x in Path(_UpperCAmelCase).open().readlines()]
						SCREAMING_SNAKE_CASE						= [x.rstrip() for x in Path(_UpperCAmelCase).open().readlines()]
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pack_examples(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase)
						print(F'''packed {split} split from {len(_UpperCAmelCase)} examples -> {len(_UpperCAmelCase)}.''')
						Path(save_path / F'''{split}.source''').open('w').write('\n'.join(_UpperCAmelCase))
						Path(save_path / F'''{split}.target''').open('w').write('\n'.join(_UpperCAmelCase))
			for split in ["val", "test"]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
						shutil.copyfile(_UpperCAmelCase							,       save_path / F'''{split}.source''')
						shutil.copyfile(_UpperCAmelCase							,       save_path / F'''{split}.target''')






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= argparse.ArgumentParser()
			parser.add_argument('--tok_name'							,       type=_UpperCAmelCase							,       help='like facebook/bart-large-cnn,t5-base, etc.')
			parser.add_argument('--max_seq_len'							,       type=_UpperCAmelCase							,       default=128)
			parser.add_argument('--data_dir'							,       type=_UpperCAmelCase)
			parser.add_argument('--save_path'							,       type=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= parser.parse_args()
			SCREAMING_SNAKE_CASE						= AutoTokenizer.from_pretrained(args.tok_name)
			return pack_data_dir(_UpperCAmelCase							,       Path(args.data_dir)							,       args.max_seq_len							,       args.save_path)


if __name__ == "__main__":
				packer_cli()












from typing import TYPE_CHECKING

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


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




