

'''simple docstring'''






# 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


lowerCAmelCase__      		=			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)

lowerCAmelCase__      		=			{'''base''': '''patrickvonplaten/wav2vec2_tiny_random''', '''robust''': '''patrickvonplaten/wav2vec2_tiny_random_robust'''}

lowerCAmelCase__      		=			'''zero2'''
lowerCAmelCase__      		=			'''zero3'''
lowerCAmelCase__      		=			[ZEROa, ZEROa]


def 	_A  (					A__  ,       A__  ,       A__   ):






	"""simple docstring"""



	__lowercase													=				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
lowerCAmelCase__      		=			list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class     lowercase_			(__lowerCamelCase   ):




		"""simple docstring"""




		@parameterized.expand(SCREAMING_SNAKE_CASE_      ,name_func=SCREAMING_SNAKE_CASE_    )
		def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Dict      ,lowercase__     :  Union[str, Any]    ):
			self.run_and_check(
			    stage=SCREAMING_SNAKE_CASE_      ,model=SCREAMING_SNAKE_CASE_      ,distributed=SCREAMING_SNAKE_CASE_      ,fpaa=SCREAMING_SNAKE_CASE_      ,)






		@require_torch_multi_gpu
		@parameterized.expand(SCREAMING_SNAKE_CASE_      ,name_func=SCREAMING_SNAKE_CASE_    )
		def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  int      ,lowercase__     :  Dict    ):
			self.run_and_check(
			    stage=SCREAMING_SNAKE_CASE_      ,model=SCREAMING_SNAKE_CASE_      ,distributed=SCREAMING_SNAKE_CASE_      ,fpaa=SCREAMING_SNAKE_CASE_      ,)






		@parameterized.expand(SCREAMING_SNAKE_CASE_      ,name_func=SCREAMING_SNAKE_CASE_    )
		def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]    ):
			self.run_and_check(
			    stage=SCREAMING_SNAKE_CASE_      ,model=SCREAMING_SNAKE_CASE_      ,distributed=SCREAMING_SNAKE_CASE_      ,fpaa=SCREAMING_SNAKE_CASE_      ,)






		@require_torch_multi_gpu
		@parameterized.expand(SCREAMING_SNAKE_CASE_      ,name_func=SCREAMING_SNAKE_CASE_    )
		def       SCREAMING_SNAKE_CASE   (   self     :  Dict      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  Dict    ):
			self.run_and_check(
			    stage=SCREAMING_SNAKE_CASE_      ,model=SCREAMING_SNAKE_CASE_      ,distributed=SCREAMING_SNAKE_CASE_      ,fpaa=SCREAMING_SNAKE_CASE_      ,)






		def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Tuple    ):
			# 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     :  Optional[Any]      ,lowercase__     :  Dict      ,lowercase__     :  int      ,lowercase__     :  Any = 1_0      ,lowercase__     :  str = True      ,lowercase__     :  Union[str, Any] = True      ,lowercase__     :  Union[str, Any] = True      ,):
			__lowercase													=				models[model]

			__lowercase													=				self.run_trainer(
			    stage=SCREAMING_SNAKE_CASE_      ,model_name=SCREAMING_SNAKE_CASE_      ,eval_steps=SCREAMING_SNAKE_CASE_      ,num_train_epochs=1      ,distributed=SCREAMING_SNAKE_CASE_      ,fpaa=SCREAMING_SNAKE_CASE_      ,)

			self.do_checks(SCREAMING_SNAKE_CASE_    )

			return output_dir






		def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Tuple      ,lowercase__     :  int      ,lowercase__     :  str = 1_0      ,lowercase__     :  int = 1      ,lowercase__     :  Any = True      ,lowercase__     :  Tuple = True      ,):
			__lowercase													=				self.get_auto_remove_tmp_dir('''./xxx'''      ,after=SCREAMING_SNAKE_CASE_    )
			__lowercase													=				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(SCREAMING_SNAKE_CASE_    )}\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
			__lowercase													=				F"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
			__lowercase													=				[F"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
			__lowercase													=				self.get_launcher(SCREAMING_SNAKE_CASE_    )

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

			return output_dir






		def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  Any=False    ):
			# 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)
			__lowercase													=				min(2      ,get_gpu_count()    ) if distributed else 1
			return F"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()




'''simple docstring'''






def 	_A  (					A__   ):






 """simple docstring"""



 if not nums:  # Makes sure that the list is not empty
  raise ValueError('''List is empty'''   )

 __lowercase													=				sum(A__   ) / len(A__   )  # Calculate the average
 return sum(abs(x - average   ) for x in nums   ) / len(A__   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()

