




'''simple docstring'''







import sys
from pathlib import Path


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

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





def 		snake_case_							(_a			:							Optional[int]					,     _a			:							int					,     _a			:							Optional[Any]  ):
    UpperCAmelCase     =	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
A									=list(itertools.product(stages, models.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class     _a						(			_UpperCAmelCase				):
       @parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			str       ):



           '''simple docstring'''

           self.run_and_check(
               stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )
       @require_torch_multi_gpu
       @parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase       )
       def 					A   (			self		:			Dict     ,       lowercase		:			int     ,       lowercase		:			Union[str, Any]       ):



           '''simple docstring'''

           self.run_and_check(
               stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )
       @parameterized.expand(__UpperCamelCase     ,       name_func=__UpperCamelCase       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			int       ):



           '''simple docstring'''

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



           '''simple docstring'''

           self.run_and_check(
               stage=__UpperCamelCase     ,       model=__UpperCamelCase     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           pass
       def 					A   (			self		:			str     ,       lowercase		:			str     ,       lowercase		:			str     ,       lowercase		:			int = 10     ,       lowercase		:			bool = True     ,       lowercase		:			bool = True     ,       lowercase		:			bool = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	models[model]

           UpperCAmelCase     =	self.run_trainer(
               stage=__UpperCamelCase     ,       model_name=__UpperCamelCase     ,       eval_steps=__UpperCamelCase     ,       num_train_epochs=1     ,       distributed=__UpperCamelCase     ,       fpaa=__UpperCamelCase     ,       )

           self.do_checks(__UpperCamelCase       )

           return output_dir
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			str     ,       lowercase		:			str     ,       lowercase		:			int = 10     ,       lowercase		:			int = 1     ,       lowercase		:			bool = True     ,       lowercase		:			bool = True     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.get_auto_remove_tmp_dir('''./xxx'''     ,       after=__UpperCamelCase       )
           UpperCAmelCase     =	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(__UpperCamelCase       )}\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
           UpperCAmelCase     =	f"--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json".split()
           UpperCAmelCase     =	[f"{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"]
           UpperCAmelCase     =	self.get_launcher(__UpperCamelCase       )

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

           return output_dir







       def 					A   (			self		:			Optional[int]     ,       lowercase		:			int=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	min(2     ,       get_gpu_count()       ) if distributed else 1
           return f"deepspeed --num_nodes 1 --num_gpus {num_gpus}".split()










'''simple docstring'''







from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 		snake_case_							(_a			:							List[str]  ):
    for param in module.parameters():
        UpperCAmelCase     =	False





def 		snake_case_							():
    UpperCAmelCase     =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        UpperCAmelCase     =	'''mps'''
    if device == "mps":
        print(
            '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
            ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
            ''' with generations.'''  )
    return device





def 		snake_case_							(_a			:							Optional[Any]  ):
    UpperCAmelCase     =	plt.imshow(_a  )
    fig.axes.get_xaxis().set_visible(_a  )
    fig.axes.get_yaxis().set_visible(_a  )
    plt.show()







def 		snake_case_							():
    UpperCAmelCase     =	datetime.now()
    UpperCAmelCase     =	current_time.strftime('''%H:%M:%S'''  )
    return timestamp




