






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()












# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


a_		:						Dict             =    Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

a_		:						List[str]             =    {'base': 'patrickvonplaten/wav2vec2_tiny_random', 'robust': 'patrickvonplaten/wav2vec2_tiny_random_robust'}

a_		:						Union[str, Any]             =    'zero2'
a_		:						List[Any]             =    'zero3'
a_		:						List[str]             =    [ZEROa, ZEROa]






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# customize the test name generator function as we want both params to appear in the sub-test
			# name, as by default it shows only the first param
			SCREAMING_SNAKE_CASE						= parameterized.to_safe_name('_'.join(str(_UpperCAmelCase) for x in param.args))
			return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
a_		:						List[Any]             =    list(itertools.product(stages, models.keys()))







@slow
@require_deepspeed
@require_torch_gpu
class 		_snake_case  (  A__					):






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[str]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       int:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			@require_torch_multi_gpu
			@parameterized.expand(a		,					name_func=a)
			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						self.run_and_check(
						    stage=a		,					model=a		,					distributed=a		,					fpaa=a		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Dict:
						# XXX: run_asr is premature and doesn't save any results
						# so all we check for now is that the process didn't fail
						pass






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = True		,					a = True		,					a = True		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= models[model]

						SCREAMING_SNAKE_CASE						= self.run_trainer(
						    stage=a		,					model_name=a		,					eval_steps=a		,					num_train_epochs=1		,					distributed=a		,					fpaa=a		,					)

						self.do_checks(a)

						return output_dir






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a = 10		,					a = 1		,					a = True		,					a = True		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.get_auto_remove_tmp_dir('./xxx'		,					after=a)
						SCREAMING_SNAKE_CASE						= f'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

						if fpaa:
									args.extend(['--fp16'])

						# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
						# hence the separate config files
						SCREAMING_SNAKE_CASE						= f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
						SCREAMING_SNAKE_CASE						= [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
						SCREAMING_SNAKE_CASE						= self.get_launcher(a)

						SCREAMING_SNAKE_CASE						= launcher + script + args + ds_args
						# keep for quick debug
						# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
						execute_subprocess_async(a		,					env=self.get_env())

						return output_dir







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Optional[int]:
						# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
						# - it won't be able to handle that
						# 2. for now testing with just 2 gpus max (since some quality tests may give different
						# results with mode gpus because we use very little data)
						SCREAMING_SNAKE_CASE						= min(2		,					get_gpu_count()) if distributed else 1
						return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()




