


# 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 (      SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE						,				SCREAMING_SNAKE_CASE     ):


		"""simple docstring"""




		lowercase__            =      parameterized.to_safe_name('''_'''.join(str(SCREAMING_SNAKE_CASE     ) 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        _a							(			UpperCamelCase__			):

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





									"""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       lowerCamelCase_ (							self:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Optional[Any]							)			->   List[Any]:





									"""simple docstring"""







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

							@parameterized.expand(UpperCamelCase_    ,						name_func=UpperCamelCase_							)
							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Optional[int]    ,						UpperCamelCase_:	Dict							)			->   Dict:





									"""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       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int							)			->   str:





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Union[str, Any]    ,						UpperCamelCase_:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







									pass

							def       lowerCamelCase_ (							self:	Optional[Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int = 10    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	bool = True    ,						)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      models[model]

									lowercase__            =      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       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	int = 10    ,						UpperCamelCase_:	int = 1    ,						UpperCamelCase_:	bool = True    ,						UpperCamelCase_:	bool = True    ,						)			->   List[str]:





									"""simple docstring"""







									lowercase__            =      self.get_auto_remove_tmp_dir('''./xxx'''    ,						after=UpperCamelCase_							)
									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(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
									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(UpperCamelCase_							)

									lowercase__            =      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       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Union[str, Any]=False							)			->   str:





									"""simple docstring"""







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








# 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 						lowercase							(   UpperCamelCase__,UpperCamelCase__		):
     _a							 =						1

     @register_to_config
     def __init__(  self		,     _a=2000		,     _a=0.1		,     _a=20		,     _a=1e-3				)						->							List[Any]:
           _A							:     Dict			=						None
           _A							:     List[Any]			=						None
           _A							:     Dict			=						None

     def 					a__	(  self		,     _a		,     _a = None				)						->							Union[str, Any]:
           _A							:     Union[str, Any]			=						torch.linspace(1		,     self.config.sampling_eps		,     _a		,     device=_a				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a=None				)						->							Dict:
           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
           _A							:     Any			=						(
               -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
           )
           _A							:     List[Any]			=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff				)				)
           _A							:     List[str]			=						std.flatten()
           while len(std.shape				) < len(score.shape				):
                 _A							:     List[Any]			=						std.unsqueeze(-1				)
           _A							:     int			=						-score / std

           # compute
           _A							:     Tuple			=						-1.0 / len(self.timesteps				)

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

           _A							:     Tuple			=						torch.sqrt(_a				)
           _A							:     Dict			=						drift - diffusion**2 * score
           _A							:     Dict			=						x + drift * dt

           # add noise
           _A							:     Any			=						randn_tensor(x.shape		,     layout=x.layout		,     generator=_a		,     device=x.device		,     dtype=x.dtype				)
           _A							:     str			=						x_mean + diffusion * math.sqrt(-dt				) * noise

           return x, x_mean



     def __len__(  self				)						->							Optional[Any]:
           return self.config.num_train_timesteps

