

"""simple docstring"""






import sys
from pathlib import Path


lowerCamelCase__					:	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)

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

lowerCamelCase__					:	Optional[Any]				    =	'''zero2'''
lowerCamelCase__					:	Tuple				    =	'''zero3'''
lowerCamelCase__					:	str				    =	[ZEROa, ZEROa]
def    UpperCamelCase       (      _lowerCAmelCase  :    Optional[int],	_lowerCAmelCase  :    List[str],	_lowerCAmelCase  :    Union[str, Any]			)					->      Union[str, Any]:
	# 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
	_UpperCAmelCase     :				Union[str, Any]          =       parameterized.to_safe_name("""_""".join(str(lowerCamelCase__			) for x in param.args			)			)
	return f'''{func.__name__}_{param_based_name}'''


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






@slow
@require_deepspeed
@require_torch_gpu
class      _UpperCAmelCase      (		__UpperCAmelCase):
			@parameterized.expand(lowerCamelCase__   ,		name_func=lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A   ,		_A  )     ->     Dict:






				'''simple docstring'''







				self.run_and_check(
				    stage=lowerCamelCase__   ,		model=lowerCamelCase__   ,		distributed=lowerCamelCase__   ,		fpaa=lowerCamelCase__   ,		)
			@require_torch_multi_gpu
			@parameterized.expand(lowerCamelCase__   ,		name_func=lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A   ,		_A  )     ->     Optional[int]:






				'''simple docstring'''







				self.run_and_check(
				    stage=lowerCamelCase__   ,		model=lowerCamelCase__   ,		distributed=lowerCamelCase__   ,		fpaa=lowerCamelCase__   ,		)
			@parameterized.expand(lowerCamelCase__   ,		name_func=lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A   ,		_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				self.run_and_check(
				    stage=lowerCamelCase__   ,		model=lowerCamelCase__   ,		distributed=lowerCamelCase__   ,		fpaa=lowerCamelCase__   ,		)
			@require_torch_multi_gpu
			@parameterized.expand(lowerCamelCase__   ,		name_func=lowerCamelCase__  )
			def 				__snake_case			(		self   ,		_A   ,		_A  )     ->     Union[str, Any]:






				'''simple docstring'''







				self.run_and_check(
				    stage=lowerCamelCase__   ,		model=lowerCamelCase__   ,		distributed=lowerCamelCase__   ,		fpaa=lowerCamelCase__   ,		)
			def 				__snake_case			(		self   ,		_A  )     ->     Any:






				'''simple docstring'''







				pass
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A = 10   ,		_A = True   ,		_A = True   ,		_A = True   ,		)     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       models[model]

				_UpperCAmelCase     :				Any          =       self.run_trainer(
				    stage=lowerCamelCase__   ,		model_name=lowerCamelCase__   ,		eval_steps=lowerCamelCase__   ,		num_train_epochs=1   ,		distributed=lowerCamelCase__   ,		fpaa=lowerCamelCase__   ,		)

				self.do_checks(lowerCamelCase__  )

				return output_dir
			def 				__snake_case			(		self   ,		_A   ,		_A   ,		_A = 10   ,		_A = 1   ,		_A = True   ,		_A = True   ,		)     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				Tuple          =       self.get_auto_remove_tmp_dir("""./xxx"""   ,		after=lowerCamelCase__  )
				_UpperCAmelCase     :				List[Any]          =       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(lowerCamelCase__  )}
            --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
				_UpperCAmelCase     :				str          =       f'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
				_UpperCAmelCase     :				Optional[Any]          =       [f'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
				_UpperCAmelCase     :				Dict          =       self.get_launcher(lowerCamelCase__  )

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

				return output_dir
			def 				__snake_case			(		self   ,		_A=False  )     ->     Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       min(2   ,		get_gpu_count()  ) if distributed else 1
				return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()









"""simple docstring"""


from decimal import Decimal, getcontext
from math import ceil, factorial


def    _lowerCamelCase   (     lowerCamelCase__	:							int  ):

 if not isinstance(lowerCamelCase__       ,		lowerCamelCase__  ):
  raise TypeError("""Undefined for non-integers"""  )
 elif precision < 1:
  raise ValueError("""Undefined for non-natural numbers"""  )

 lowercase__       :       List[str]				=						precision
 lowercase__       :       Optional[int]				=						ceil(precision / 14  )
 lowercase__       :       Union[str, Any]				=						42_68_80 * Decimal(1_00_05  ).sqrt()
 lowercase__       :       Optional[Any]				=						1
 lowercase__       :       Optional[Any]				=						13_59_14_09
 lowercase__       :       List[Any]				=						Decimal(lowerCamelCase__  )
 for k in range(1       ,		lowerCamelCase__  ):
  lowercase__       :       Optional[int]				=						factorial(6 * k  ) // (factorial(3 * k  ) * factorial(lowerCamelCase__  ) ** 3)
  linear_term += 5_45_14_01_34
  exponential_term *= -26_25_37_41_26_40_76_80_00
  partial_sum += Decimal(multinomial_term * linear_term  ) / exponential_term
 return str(constant_term / partial_sum  )[:-1]


if __name__ == "__main__":
   __snake_case    					=					50
   print(F"The first {n} digits of pi is: {pi(n)}")