




'''simple docstring'''






import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
						import torch

A_ 			=		logging.get_logger(__name__)






def 		UpperCamelCase__			(		)						->      Optional[Any]:
						return importlib.util.find_spec('deepspeed'		) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig






class 					lowercase_			(	_a       ):





					def __init__(    self    :				int    ,       __lowerCamelCase    :				List[Any] ):
											# set global weakref object
											set_hf_deepspeed_config(self )
											dep_version_check('accelerate' )
											dep_version_check('deepspeed' )
											super().__init__(lowerCamelCase__ )






class 					lowercase_			(	_a       ):





					def __init__(    self    :				List[str]    ,       __lowerCamelCase    :				Optional[int] ):
											super().__init__(lowerCamelCase__ )
											snake_case__					:      List[str]  			=						None
											snake_case__					:      Dict  			=						[]





					def  _lowerCAmelCase			(    self    :				Optional[int] ):
											if self._dtype is None:
																	raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype' )
											return self._dtype





					def  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				Optional[int] ):
											snake_case__					:      int  			=						self.get_value(lowerCamelCase__ )
											if val is None:
																	return False
											else:
																	return val == "auto"





					def  _lowerCAmelCase			(    self    :				str    ,       __lowerCamelCase    :				str    ,       __lowerCamelCase    :				Union[str, Any]    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Any=True ):
											snake_case__					:      Union[str, Any]  			=						self.find_config_node(lowerCamelCase__ )
											if config is None:
																	return

											if config.get(lowerCamelCase__ ) == "auto":
																	snake_case__					:      Optional[Any]  			=						hf_val
																	return

											if not must_match:
																	return

											snake_case__					:      List[str]  			=						config.get(lowerCamelCase__ )
											if ds_val is not None and ds_val != hf_val:
																	self.mismatches.append(F"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}" )

					A_       							= partialmethod(_a	,					must_match=_a       )





					def  _lowerCAmelCase			(    self    :				Union[str, Any]    ,       __lowerCamelCase    :				Tuple ):
											snake_case__					:      Any  			=						args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
											self.fill_match(
											    'train_micro_batch_size_per_gpu'    ,       args.per_device_train_batch_size    ,       'per_device_train_batch_size' )
											self.fill_match('gradient_accumulation_steps'    ,       args.gradient_accumulation_steps    ,       'gradient_accumulation_steps' )
											self.fill_match('train_batch_size'    ,       lowerCamelCase__    ,       'train_batch_size (calculated)' )
											self.fill_match('gradient_clipping'    ,       args.max_grad_norm    ,       'max_grad_norm' )

											self.fill_match('optimizer.params.lr'    ,       args.learning_rate    ,       'learning_rate' )
											self.fill_match('optimizer.params.betas'    ,       [args.adam_betaa, args.adam_betaa]    ,       'adam_beta1+adam_beta2' )
											self.fill_match('optimizer.params.eps'    ,       args.adam_epsilon    ,       'adam_epsilon' )
											self.fill_match('optimizer.params.weight_decay'    ,       args.weight_decay    ,       'weight_decay' )

											self.fill_only('scheduler.params.warmup_min_lr'    ,       0 )  # not a trainer arg
											self.fill_match('scheduler.params.warmup_max_lr'    ,       args.learning_rate    ,       'learning_rate' )
											# total_num_steps - will get set in trainer_config_finalize

											# fp16
											if args.fpaa or args.fpaa_full_eval:
																	snake_case__					:      Optional[int]  			=						"apex" if args.fpaa_backend == "apex" else "amp"
											else:
																	snake_case__					:      Union[str, Any]  			=						None

											if args.save_on_each_node:
																	# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
																	snake_case__					:      Any  			=						self.config.get('checkpoint'    ,       {} )
																	snake_case__					:      Dict  			=						args.save_on_each_node

											# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
											# any here unless the user did the work
											self.fill_match(
											    'fp16.enabled'    ,       ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == 'amp')    ,       'fp16|fp16_full_eval+fp16_backend(amp)'    ,       )

											# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
											# ZeRO features
											self.fill_match('amp.enabled'    ,       fpaa_backend == 'apex'    ,       'fp16+fp16_backend(apex)' )
											self.fill_match('amp.opt_level'    ,       args.fpaa_opt_level    ,       'fp16_opt_level' )

											self.fill_match('bf16.enabled'    ,       (args.bfaa or args.bfaa_full_eval)    ,       'bf16|bf16_full_eval' )

											# deepspeed's default mode is fp16 unless there is a config that says differently
											if self.is_true('bf16.enabled' ):
																	snake_case__					:      Union[str, Any]  			=						torch.bfloataa
											elif self.is_false('fp16.enabled' ):
																	snake_case__					:      Optional[int]  			=						torch.floataa
											else:
																	snake_case__					:      Optional[int]  			=						torch.floataa





					def  _lowerCAmelCase			(    self    :				Any    ,       __lowerCamelCase    :				int    ,       __lowerCamelCase    :				Any    ,       __lowerCamelCase    :				Tuple ):
											snake_case__					:      Optional[Any]  			=						[
											    "zero_optimization.reduce_bucket_size",
											    "zero_optimization.stage3_prefetch_bucket_size",
											    "zero_optimization.stage3_param_persistence_threshold",
											]
											snake_case__					:      List[Any]  			=						[x for x in hidden_size_based_keys if self.is_auto(lowerCamelCase__ )]

											if len(lowerCamelCase__ ) > 0:
																	if hasattr(model.config    ,       'hidden_size' ):
																							snake_case__					:      Dict  			=						model.config.hidden_size
																	elif hasattr(model.config    ,       'hidden_sizes' ):
																							# if there are many hidden sizes pick the largest one
																							snake_case__					:      List[str]  			=						max(model.config.hidden_sizes )
																	else:
																							raise ValueError(
																							    'The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '
																							    'therefore it\'s not possible to automatically fill out the following `auto` entries '
																							    F"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
																							    '`auto` values for these keys with an integer value of your choice.' )

																	self.fill_only('zero_optimization.reduce_bucket_size'    ,       hidden_size * hidden_size )
																	if self.is_zeroa():
																							# automatically assign the optimal config values based on model config
																							self.fill_only('zero_optimization.stage3_prefetch_bucket_size'    ,       0.9 * hidden_size * hidden_size )
																							self.fill_only('zero_optimization.stage3_param_persistence_threshold'    ,       10 * hidden_size )

        # scheduler
											self.fill_match('scheduler.params.total_num_steps'    ,       lowerCamelCase__    ,       'num_training_steps (calculated)' )
											self.fill_match('scheduler.params.warmup_num_steps'    ,       args.get_warmup_steps(lowerCamelCase__ )    ,       'warmup_steps' )

											if len(self.mismatches ) > 0:
																	snake_case__					:      Dict  			=						"\n".join(self.mismatches )
																	raise ValueError(
																	    'Please correct the following DeepSpeed config values that mismatch TrainingArguments'
																	    F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'." )



A_ 			=		None






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE		)						->      List[str]:
						# this is a special weakref global object to allow us to get to Deepspeed config from APIs
						# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
						global _hf_deepspeed_config_weak_ref
						# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
						snake_case__					:      Tuple  			=						weakref.ref(__SCREAMING_SNAKE_CASE		)






def 		UpperCamelCase__			(		)						->      Any:
						# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
						global _hf_deepspeed_config_weak_ref
						snake_case__					:      Any  			=						None






def 		UpperCamelCase__			(		)						->      str:
						if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
												return _hf_deepspeed_config_weak_ref().is_zeroa()
						else:
												return False






def 		UpperCamelCase__			(		)						->      List[str]:
						if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
												return _hf_deepspeed_config_weak_ref().config
						else:
												return None






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[Any]:
						from accelerate.utils import DummyOptim, DummyScheduler

						snake_case__					:      Optional[int]  			=						hf_deepspeed_config.config

						# Optimizer + Scheduler
						# Currently supported combos:
						# 1. DS scheduler + DS optimizer: Yes
						# 2. HF scheduler + HF optimizer: Yes
						# 3. DS scheduler + HF optimizer: Yes
						# 4. HF scheduler + DS optimizer: No
						#
						# Unless Offload is enabled in which case it's:
						# 1. DS scheduler + DS optimizer: Yes
						# 2. HF scheduler + HF optimizer: Mostly*
						# 3. DS scheduler + HF optimizer: Mostly*
						# 4. HF scheduler + DS optimizer: No
						#
						# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

						snake_case__					:      Optional[int]  			=						None
						if "optimizer" in config:
												if args.adafactor:
																		raise ValueError(
																		    '--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '
																		    'Only one optimizer can be configured.'		)
												snake_case__					:      List[str]  			=						DummyOptim(params=__SCREAMING_SNAKE_CASE		)
						else:
												if hf_deepspeed_config.is_offload():
																		logger.info(
																		    'Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'
																		    ' custom optimizer has both CPU and GPU implementation (except LAMB)'		)

												# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
												# But trainer uses AdamW by default.
												snake_case__					:      Optional[int]  			=						trainer.create_optimizer()
												# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
												snake_case__					:      Optional[Any]  			=						True

						snake_case__					:      int  			=						None
						if "scheduler" in config:
												snake_case__					:      Union[str, Any]  			=						DummyScheduler(__SCREAMING_SNAKE_CASE		)
						else:
												if isinstance(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		):
																		raise ValueError(
																		    'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
																		    'Please configure a scheduler in the DeepSpeed config.'		)
												snake_case__					:      Union[str, Any]  			=						trainer.create_scheduler(num_training_steps=__SCREAMING_SNAKE_CASE							,			optimizer=__SCREAMING_SNAKE_CASE		)

						return optimizer, lr_scheduler






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=False		)						->      List[str]:
						from deepspeed.utils import logger as ds_logger

						snake_case__					:      str  			=						trainer.model
						snake_case__					:      Dict  			=						trainer.args

						snake_case__					:      str  			=						trainer.accelerator.state.deepspeed_plugin.hf_ds_config

						# resume config update - some bits like `model` and `num_training_steps` only become available during train
						hf_deepspeed_config.trainer_config_finalize(__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

						# set the Deepspeed log level consistent with the Trainer
						ds_logger.setLevel(args.get_process_log_level()		)

						if inference:
												# only Z3 makes sense for the inference
												if not hf_deepspeed_config.is_zeroa():
																		raise ValueError('ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'		)

												# in case the training config is re-used for inference
												hf_deepspeed_config.del_config_sub_tree('optimizer'		)
												hf_deepspeed_config.del_config_sub_tree('lr_scheduler'		)
												snake_case__					:      int  			=						None, None
												snake_case__					:      Optional[Any]  			=						None
						else:
												snake_case__					:      str  			=						None  # important for when deepspeed_init is used as re-init
												snake_case__					:      str  			=						list(filter(lambda __SCREAMING_SNAKE_CASE		: p.requires_grad							,			model.parameters()		)		)
												snake_case__					:      List[str]  			=						deepspeed_optim_sched(
												    __SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)

						# keep for quick debug:
						# from pprint import pprint; pprint(config)

						return optimizer, lr_scheduler






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[str]:
						# it's possible that the user is trying to resume from model_path, which doesn't necessarily
						# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
						# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
						# path contains what looks like a deepspeed checkpoint
						import glob

						snake_case__					:      Optional[int]  			=						sorted(glob.glob(f"{checkpoint_path}/global_step*"		)		)

						if len(__SCREAMING_SNAKE_CASE		) > 0:
												logger.info(f"Attempting to resume from {checkpoint_path}"		)
												# this magically updates self.optimizer and self.lr_scheduler
												snake_case__					:      Dict  			=						deepspeed_engine.load_checkpoint(
												    __SCREAMING_SNAKE_CASE							,			load_optimizer_states=__SCREAMING_SNAKE_CASE							,			load_lr_scheduler_states=__SCREAMING_SNAKE_CASE		)
												if load_path is None:
																		raise ValueError(f"[deepspeed] failed to resume from checkpoint {checkpoint_path}"		)
						else:
												raise ValueError(f"Can't find a valid checkpoint at {checkpoint_path}"		)






'''simple docstring'''






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
      from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


A_ 			=		get_tests_dir("fixtures/test_sentencepiece.model")






@require_torch
@require_torchaudio
@require_sentencepiece
class 					lowercase_			(	unittest.TestCase       ):





     def  _lowerCAmelCase			(    self    :				List[str] ):
           snake_case__					:      Dict  			=						tempfile.mkdtemp()

           snake_case__					:      Union[str, Any]  			=						['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
           snake_case__					:      str  			=						dict(zip(__lowerCamelCase    ,       range(len(__lowerCamelCase ) ) ) )
           snake_case__					:      Union[str, Any]  			=						Path(self.tmpdirname )
           save_json(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['vocab_file'] )
           if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(__lowerCamelCase    ,       save_dir / VOCAB_FILES_NAMES['spm_file'] )

           snake_case__					:      Optional[Any]  			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
           tokenizer.save_pretrained(self.tmpdirname )

           snake_case__					:      str  			=						{
               'feature_size': 24,
               'num_mel_bins': 24,
               'padding_value': 0.0,
               'sampling_rate': 16000,
               'return_attention_mask': False,
               'do_normalize': True,
           }
           save_json(__lowerCamelCase    ,       save_dir / FEATURE_EXTRACTOR_NAME )





     def  _lowerCAmelCase			(    self    :				str    ,       **__lowerCamelCase    :				int ):
           return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				int    ,       **__lowerCamelCase    :				List[Any] ):
           return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           shutil.rmtree(self.tmpdirname )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Dict  			=						self.get_tokenizer()
           snake_case__					:      Any  			=						self.get_feature_extractor()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           processor.save_pretrained(self.tmpdirname )
           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(self.tmpdirname )

           self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer.get_vocab() )
           self.assertIsInstance(processor.tokenizer    ,       __lowerCamelCase )

           self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string() )
           self.assertIsInstance(processor.feature_extractor    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any] ):
           snake_case__					:      Any  			=						SpeechaTextProcessor(
               tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor() )
           processor.save_pretrained(self.tmpdirname )

           snake_case__					:      Tuple  			=						self.get_tokenizer(bos_token='(BOS)'    ,       eos_token='(EOS)' )
           snake_case__					:      int  			=						self.get_feature_extractor(do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           snake_case__					:      Any  			=						SpeechaTextProcessor.from_pretrained(
               self.tmpdirname    ,       bos_token='(BOS)'    ,       eos_token='(EOS)'    ,       do_normalize=__lowerCamelCase    ,       padding_value=1.0 )

           self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab() )
           self.assertIsInstance(processor.tokenizer    ,       __lowerCamelCase )

           self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string() )
           self.assertIsInstance(processor.feature_extractor    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Any ):
           snake_case__					:      int  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      List[str]  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      List[str]  			=						floats_list((3, 1000) )

           snake_case__					:      Tuple  			=						feature_extractor(__lowerCamelCase    ,       return_tensors='np' )
           snake_case__					:      str  			=						processor(__lowerCamelCase    ,       return_tensors='np' )

           for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1E-2 )





     def  _lowerCAmelCase			(    self    :				Dict ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      Optional[Any]  			=						self.get_tokenizer()

           snake_case__					:      Tuple  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      Optional[int]  			=						'This is a test string'

           snake_case__					:      int  			=						processor(text=__lowerCamelCase )

           snake_case__					:      Tuple  			=						tokenizer(__lowerCamelCase )

           for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key] )





     def  _lowerCAmelCase			(    self    :				int ):
           snake_case__					:      Optional[int]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      str  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           snake_case__					:      str  			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           snake_case__					:      List[str]  			=						processor.batch_decode(__lowerCamelCase )
           snake_case__					:      Optional[int]  			=						tokenizer.batch_decode(__lowerCamelCase )

           self.assertListEqual(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				str ):
           snake_case__					:      List[str]  			=						self.get_feature_extractor()
           snake_case__					:      int  			=						self.get_tokenizer()

           snake_case__					:      int  			=						SpeechaTextProcessor(tokenizer=__lowerCamelCase    ,       feature_extractor=__lowerCamelCase )

           self.assertListEqual(
               processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg='`processor` and `feature_extractor` model input names do not match'    ,       )
