




"""simple docstring"""






import sys
from pathlib import Path


UpperCamelCase__						=			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(4_2)

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

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





def    UpperCAmelCase		(		snake_case			:					int      ,       snake_case			:					Tuple      ,       snake_case			:					Dict    ):
					# 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
					_lowerCAmelCase:Tuple									=  parameterized.to_safe_name('''_'''.join(str(snake_case    ) for x in param.args    )    )
					return F'{func.__name__}_{param_based_name}'


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





@slow
@require_deepspeed
@require_torch_gpu
class   a__			(       UpperCamelCase_  ):





						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Optional[Any]     ,a__      :	Optional[int])						->       List[str]:




											"""simple docstring"""







											self.run_and_check(
											    stage=a__     ,model=a__     ,distributed=a__     ,fpaa=a__     ,)





						@require_torch_multi_gpu
						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Tuple     ,a__      :	Union[str, Any])						->       Optional[int]:




											"""simple docstring"""







											self.run_and_check(
											    stage=a__     ,model=a__     ,distributed=a__     ,fpaa=a__     ,)





						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	str     ,a__      :	Dict     ,a__      :	List[Any])						->       Dict:




											"""simple docstring"""







											self.run_and_check(
											    stage=a__     ,model=a__     ,distributed=a__     ,fpaa=a__     ,)





						@require_torch_multi_gpu
						@parameterized.expand(a__     ,name_func=a__)
						def       __UpperCamelCase     (							self      :	Optional[Any]     ,a__      :	List[str]     ,a__      :	List[str])						->       str:




											"""simple docstring"""







											self.run_and_check(
											    stage=a__     ,model=a__     ,distributed=a__     ,fpaa=a__     ,)





						def       __UpperCamelCase     (							self      :	List[Any]     ,a__      :	Tuple)						->       Union[str, Any]:




											"""simple docstring"""







											pass





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	str     ,a__      :	str     ,a__      :	int = 10     ,a__      :	bool = True     ,a__      :	bool = True     ,a__      :	bool = True     ,)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:List[Any]									=  models[model]

											_lowerCAmelCase:str									=  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       __UpperCamelCase     (							self      :	str     ,a__      :	str     ,a__      :	str     ,a__      :	int = 10     ,a__      :	int = 1     ,a__      :	bool = True     ,a__      :	bool = True     ,)						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.get_auto_remove_tmp_dir('''./xxx'''     ,after=a__)
											_lowerCAmelCase:int									=  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(a__)}\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
											_lowerCAmelCase:List[Any]									=  F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
											_lowerCAmelCase:int									=  [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
											_lowerCAmelCase:List[str]									=  self.get_launcher(a__)

											_lowerCAmelCase:Tuple									=  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       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	List[str]=False)						->       int:




											"""simple docstring"""







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












"""simple docstring"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class   a__			(       UpperCamelCase_					,	UpperCamelCase_  ):
						snake_case__             = 1





						@register_to_config
						def __init__(							self      :	Union[str, Any]     ,a__      :	List[str]=2000     ,a__      :	Tuple=0.1     ,a__      :	Union[str, Any]=20     ,a__      :	int=1E-3)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  None
											_lowerCAmelCase:List[Any]									=  None
											_lowerCAmelCase:Dict									=  None





						def       __UpperCamelCase     (							self      :	int     ,a__      :	Any     ,a__      :	Union[str, torch.device] = None)						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:str									=  torch.linspace(1     ,self.config.sampling_eps     ,a__     ,device=a__)





						def       __UpperCamelCase     (							self      :	Union[str, Any]     ,a__      :	str     ,a__      :	List[Any]     ,a__      :	Union[str, Any]     ,a__      :	Any=None)						->       Tuple:




											"""simple docstring"""







											if self.timesteps is None:
																raise ValueError(
																    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''')

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											_lowerCAmelCase:Union[str, Any]									=  (
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											_lowerCAmelCase:Dict									=  torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff))
											_lowerCAmelCase:Optional[Any]									=  std.flatten()
											while len(std.shape) < len(score.shape):
																_lowerCAmelCase:str									=  std.unsqueeze(-1)
											_lowerCAmelCase:Optional[int]									=  -score / std

											# compute
											_lowerCAmelCase:Optional[Any]									=  -1.0 / len(self.timesteps)

											_lowerCAmelCase:Union[str, Any]									=  self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											_lowerCAmelCase:Any									=  beta_t.flatten()
											while len(beta_t.shape) < len(x.shape):
																_lowerCAmelCase:List[str]									=  beta_t.unsqueeze(-1)
											_lowerCAmelCase:Tuple									=  -0.5 * beta_t * x

											_lowerCAmelCase:str									=  torch.sqrt(a__)
											_lowerCAmelCase:Union[str, Any]									=  drift - diffusion**2 * score
											_lowerCAmelCase:List[str]									=  x + drift * dt

											# add noise
											_lowerCAmelCase:Any									=  randn_tensor(x.shape     ,layout=x.layout     ,generator=a__     ,device=x.device     ,dtype=x.dtype)
											_lowerCAmelCase:Union[str, Any]									=  x_mean + diffusion * math.sqrt(-dt) * noise

											return x, x_mean







						def __len__(							self      :	int)						->       Tuple:




											"""simple docstring"""







											return self.config.num_train_timesteps






