






"""simple docstring"""






import sys
from pathlib import Path


UpperCAmelCase_     :				int         =				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)

UpperCAmelCase_     :				List[str]         =				{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

UpperCAmelCase_     :				Union[str, Any]         =				"""zero2"""
UpperCAmelCase_     :				str         =				"""zero3"""
UpperCAmelCase_     :				str         =				[ZEROa, ZEROa]



def       _A    (__a					,       __a					,       __a							)				-> Dict:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	parameterized.to_safe_name('''_'''.join(str(__a							) for x in param.args							)							)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
UpperCAmelCase_     :				str         =				list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 							lowerCAmelCase__       (    UpperCAmelCase__				):






      '''simple docstring'''

      @parameterized.expand(lowercase_		,			name_func=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]		,			lowercase_  :						str		,			lowercase_  :						str):




          '''simple docstring'''


          self.run_and_check(
              stage=lowercase_		,			model=lowercase_		,			distributed=lowercase_		,			fpaa=lowercase_		,			)
      @require_torch_multi_gpu
      @parameterized.expand(lowercase_		,			name_func=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						str		,			lowercase_  :						Union[str, Any]		,			lowercase_  :						List[Any]):




          '''simple docstring'''


          self.run_and_check(
              stage=lowercase_		,			model=lowercase_		,			distributed=lowercase_		,			fpaa=lowercase_		,			)
      @parameterized.expand(lowercase_		,			name_func=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Any		,			lowercase_  :						int		,			lowercase_  :						Any):




          '''simple docstring'''


          self.run_and_check(
              stage=lowercase_		,			model=lowercase_		,			distributed=lowercase_		,			fpaa=lowercase_		,			)
      @require_torch_multi_gpu
      @parameterized.expand(lowercase_		,			name_func=lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]		,			lowercase_  :						Tuple		,			lowercase_  :						Any):




          '''simple docstring'''


          self.run_and_check(
              stage=lowercase_		,			model=lowercase_		,			distributed=lowercase_		,			fpaa=lowercase_		,			)
      def _SCREAMING_SNAKE_CASE				(		self  :						int		,			lowercase_  :						List[Any]):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						int = 10		,			lowercase_  :						bool = True		,			lowercase_  :						bool = True		,			lowercase_  :						bool = True		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	models[model]

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.run_trainer(
              stage=lowercase_		,			model_name=lowercase_		,			eval_steps=lowercase_		,			num_train_epochs=1		,			distributed=lowercase_		,			fpaa=lowercase_		,			)

          self.do_checks(lowercase_)

          return output_dir
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]		,			lowercase_  :						str		,			lowercase_  :						str		,			lowercase_  :						int = 10		,			lowercase_  :						int = 1		,			lowercase_  :						bool = True		,			lowercase_  :						bool = True		,			):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	self.get_auto_remove_tmp_dir('''./xxx'''		,			after=lowercase_)
          SCREAMING_SNAKE_CASE_   :						Dict							=	F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.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_   :						List[Any]							=	F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	[F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	self.get_launcher(lowercase_)

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

          return output_dir



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]		,			lowercase_  :						Union[str, Any]=False):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	min(2		,			get_gpu_count()) if distributed else 1
          return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()










"""simple docstring"""






import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser



def       _A    (__a							)				-> Optional[int]:
    """simple docstring"""






    SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	{}
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	tokenizer(example['''content''']					,       truncation=__a							)['''input_ids''']
    SCREAMING_SNAKE_CASE_   :						List[Any]							=	len(example['''content''']							) / len(output['''input_ids''']							)
    return output


UpperCAmelCase_     :				Tuple         =				HfArgumentParser(PretokenizationArguments)
UpperCAmelCase_     :				Tuple         =				parser.parse_args()
if args.num_workers is None:
  UpperCAmelCase_     :				Tuple         =				multiprocessing.cpu_count()
UpperCAmelCase_     :				str         =				AutoTokenizer.from_pretrained(args.tokenizer_dir)

UpperCAmelCase_     :				Tuple         =				time.time()
UpperCAmelCase_     :				List[Any]         =				load_dataset(args.dataset_name, split="""train""")
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				Dict         =				time.time()
UpperCAmelCase_     :				Dict         =				ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

UpperCAmelCase_     :				str         =				time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')


