



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase_		 =					1_6
UpperCAmelCase_		 =					3_2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Accelerator	,   _snake_case	:int = 16				)  ->     Optional[Any]:
   _A      =       AutoTokenizer.from_pretrained('''bert-base-cased'''				)
   _A      =       load_dataset('''glue'''	,   '''mrpc'''				)

   def tokenize_function(_snake_case	:Optional[int]				):
      # max_length=None => use the model max length (it's actually the default)
      _A      =       tokenizer(examples['''sentence1''']	,   examples['''sentence2''']	,   truncation=_snake_case	,   max_length=_snake_case				)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      _A      =       datasets.map(
          _snake_case	,   batched=_snake_case	,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,   )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   _A      =       tokenized_datasets.rename_column('''label'''	,   '''labels'''				)

   def collate_fn(_snake_case	:Any				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _A      =       128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _A      =       16
      elif accelerator.mixed_precision != "no":
         _A      =       8
      else:
         _A      =       None

      return tokenizer.pad(
          _snake_case	,   padding='''longest'''	,   max_length=_snake_case	,   pad_to_multiple_of=_snake_case	,   return_tensors='''pt'''	,   )

   # Instantiate dataloaders.
   _A      =       DataLoader(
       tokenized_datasets['''train''']	,   shuffle=_snake_case	,   collate_fn=_snake_case	,   batch_size=_snake_case				)
   _A      =       DataLoader(
       tokenized_datasets['''validation''']	,   shuffle=_snake_case	,   collate_fn=_snake_case	,   batch_size=_snake_case				)

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   UpperCAmelCase_		 =					mocked_dataloaders  # noqa: F811

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:str				)  ->     Optional[int]:
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,   _snake_case				) == "1":
      _A      =       2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      _A      =       Accelerator(
          cpu=args.cpu	,   mixed_precision=args.mixed_precision	,   log_with='''all'''	,   project_dir=args.project_dir				)
   else:
      _A      =       Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _A      =       config['''lr''']
   _A      =       int(config['''num_epochs''']				)
   _A      =       int(config['''seed''']				)
   _A      =       int(config['''batch_size''']				)
   set_seed(_snake_case				)

   _A  ,    _A      =       get_dataloaders(_snake_case	,   _snake_case				)
   _A      =       evaluate.load('''glue'''	,   '''mrpc'''				)

   # If the batch size is too big we use gradient accumulation
   _A      =       1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _A      =       batch_size // MAX_GPU_BATCH_SIZE
      _A      =       MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _A      =       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,   return_dict=_snake_case				)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   _A      =       model.to(accelerator.device				)

   # Instantiate optimizer
   _A      =       AdamW(params=model.parameters()	,   lr=_snake_case				)

   # Instantiate scheduler
   _A      =       get_linear_schedule_with_warmup(
       optimizer=_snake_case	,   num_warmup_steps=100	,   num_training_steps=(len(_snake_case				) * num_epochs) // gradient_accumulation_steps	,   )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   _A  ,    _A  ,    _A  ,    _A  ,    _A      =       accelerator.prepare(
       _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case				)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      _A      =       os.path.split(_snake_case				)[-1].split('''.'''				)[0]
      accelerator.init_trackers(_snake_case	,   _snake_case				)

   # Now we train the model
   for epoch in range(_snake_case				):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         _A      =       0
      for step, batch in enumerate(_snake_case				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device				)
         _A      =       model(**_snake_case				)
         _A      =       outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         _A      =       loss / gradient_accumulation_steps
         accelerator.backward(_snake_case				)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_snake_case				):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device				)
         with torch.no_grad():
            _A      =       model(**_snake_case				)
         _A      =       outputs.logits.argmax(dim=-1				)
         _A  ,    _A      =       accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
         metric.add_batch(
             predictions=_snake_case	,   references=_snake_case	,   )

      _A      =       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''	,   _snake_case				)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': eval_metric['''accuracy'''],
                 '''f1''': eval_metric['''f1'''],
                 '''train_loss''': total_loss.item() / len(_snake_case				),
                 '''epoch''': epoch,
             }	,   step=_snake_case	,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()

def 	SCREAMING_SNAKE_CASE_							(	)  ->     List[Any]:
   _A      =       argparse.ArgumentParser(description='''Simple example of training script.'''				)
   parser.add_argument(
       '''--mixed_precision'''	,   type=_snake_case	,   default=_snake_case	,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,   help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''	,   )
   parser.add_argument('''--cpu'''	,   action='''store_true'''	,   help='''If passed, will train on the CPU.'''				)
   parser.add_argument(
       '''--with_tracking'''	,   action='''store_true'''	,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,   )
   parser.add_argument(
       '''--project_dir'''	,   type=_snake_case	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
   _A      =       parser.parse_args()
   _A      =       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_snake_case	,   _snake_case				)


if __name__ == "__main__":
   main()







"""simple docstring"""


import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
	from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
	A	:  List[Any]            =     True
	from torch.cuda.amp import autocast


A	:  Any            =     logging.getLogger(__name__)
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(
							    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,)
							__UpperCAmelCase		:       Optional[bool]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Whether to freeze the feature extractor layers of the model."""}						)
							__UpperCAmelCase		:       Optional[bool]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Whether to log verbose messages or not."""}			,)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=2.0			,metadata={"""help""": """Maximum temperature for gumbel softmax."""}						)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=0.5			,metadata={"""help""": """Minimum temperature for gumbel softmax."""}						)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=0.999_995			,metadata={"""help""": """Decay of gumbel temperature during training."""}						)


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			logging.basicConfig(
			    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"       ,					datefmt="%m/%d/%Y %H:%M:%S"       ,					handlers=[logging.StreamHandler(sys.stdout )]       ,					)
			__lowerCAmelCase							       =     logging.WARNING
			if model_args.verbose_logging:
						__lowerCAmelCase							       =     logging.DEBUG
			elif trainer_utils.is_main_process(training_args.local_rank ):
						__lowerCAmelCase							       =     logging.INFO
			logger.setLevel(_UpperCamelCase )
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}						)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""train"""			,metadata={
							        """help""": """The name of the training data set split to use (via the datasets library). Defaults to 'train'"""
							    }			,)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""validation"""			,metadata={
							        """help""": (
							            """The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"""
							        )
							    }			,)
							__UpperCAmelCase		:       Optional[str]							 =field(
							    default="""file"""			,metadata={"""help""": """Column in the dataset that contains speech file path. Defaults to 'file'"""}			,)
							__UpperCAmelCase		:       bool							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""}						)
							__UpperCAmelCase		:       Optional[int]							 =field(
							    default=1			,metadata={
							        """help""": """The percentage of the train set used as validation set in case there's no validation split"""
							    }			,)
							__UpperCAmelCase		:       Optional[int]							 =field(
							    default=lowerCAmelCase__			,metadata={"""help""": """The number of processes to use for the preprocessing."""}			,)
							__UpperCAmelCase		:       Optional[float]							 =field(
							    default=20.0			,metadata={"""help""": """Filter audio files that are longer than `max_duration_in_seconds` seconds"""}						)
@dataclass
class     _UpperCamelCase    :
							'''simple docstring'''







							__UpperCAmelCase		:       WavaVecaForPreTraining
							__UpperCAmelCase		:       WavaVecaFeatureExtractor
							__UpperCAmelCase		:       Union[bool, str]							 ="longest"
							__UpperCAmelCase		:       Optional[int]							 =None
							__UpperCAmelCase		:       Optional[int]							 =None

							def __call__(	self       ,  __a      ):
										# reformat list to dict and set to pytorch format
										__lowerCAmelCase							       =     self.feature_extractor.pad(
										    __a       ,  max_length=self.max_length       ,  padding=self.padding       ,  pad_to_multiple_of=self.pad_to_multiple_of       ,  return_tensors="pt"       ,  )
										__lowerCAmelCase							       =     self.model._get_feat_extract_output_lengths(batch["input_values"].shape[-1]      )

										__lowerCAmelCase							       =     batch["input_values"].shape[0]

										# make sure that no loss is computed on padded inputs
										if batch["attention_mask"] is not None:
													# compute real output lengths according to convolution formula
													__lowerCAmelCase							       =     self.model._get_feat_extract_output_lengths(batch["attention_mask"].sum(-1      )      ).to(
													    torch.long      )

													__lowerCAmelCase							       =     torch.zeros(
													    (batch_size, mask_indices_seq_length)       ,  dtype=torch.long       ,  device=batch["input_values"].device      )

													# these two operations makes sure that all values
													# before the output lengths indices are attended to
													__lowerCAmelCase							       =     1
													__lowerCAmelCase							       =     attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

										# sample randomly masked indices
										__lowerCAmelCase							       =     _compute_mask_indices(
										    (batch_size, mask_indices_seq_length)       ,  self.model.config.mask_time_prob       ,  self.model.config.mask_time_length       ,  attention_mask=__a       ,  min_masks=2       ,  )

										return batch
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  *__a       ,  __a=1       ,  __a=0       ,  __a=1.0       ,  **__a      ):
										super().__init__(*__a       ,  **__a      )
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     max_gumbel_temp
										__lowerCAmelCase							       =     min_gumbel_temp
										__lowerCAmelCase							       =     gumbel_temp_decay



							def      snake_case							(	self       ,  __a       ,  __a      ):
										model.train()
										__lowerCAmelCase							       =     self._prepare_inputs(__a      )

										if self.use_amp:
													with autocast():
																__lowerCAmelCase							       =     self.compute_loss(__a       ,  __a      )
										else:
													__lowerCAmelCase							       =     self.compute_loss(__a       ,  __a      )

										if self.args.n_gpu > 1 or self.deepspeed:
													if model.module.config.ctc_loss_reduction == "mean":
																__lowerCAmelCase							       =     loss.mean()
													elif model.module.config.ctc_loss_reduction == "sum":
																__lowerCAmelCase							       =     loss.sum() / (inputs["mask_time_indices"]).sum()
													else:
																raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

										if self.args.gradient_accumulation_steps > 1:
													__lowerCAmelCase							       =     loss / self.args.gradient_accumulation_steps

										if self.use_amp:
													self.scaler.scale(__a      ).backward()
										elif self.use_apex:
													with amp.scale_loss(__a       ,  self.optimizer      ) as scaled_loss:
																scaled_loss.backward()
										elif self.deepspeed:
													self.deepspeed.backward(__a      )
										else:
													loss.backward()

										self.num_update_step += 1
										# make sure gumbel softmax temperature is decayed
										if self.args.n_gpu > 1 or self.deepspeed:
													model.module.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step       ,  self.min_gumbel_temp      )      )
										else:
													model.set_gumbel_temperature(
													    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step       ,  self.min_gumbel_temp      )      )

										return loss.detach()


def _lowerCamelCase      (						):

			'''simple docstring'''




			__lowerCAmelCase							       =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

			__lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase							       =     parser.parse_args_into_dataclasses()
			configure_logger(_UpperCamelCase       ,					_UpperCamelCase )

			# Downloading and loading a dataset from the hub.
			__lowerCAmelCase							       =     load_dataset(data_args.dataset_name       ,					data_args.dataset_config_name       ,					cache_dir=model_args.cache_dir )

			if "validation" not in datasets.keys():
						# make sure only "validation" and "train" keys remain"
						__lowerCAmelCase							       =     DatasetDict()
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"       ,					cache_dir=model_args.cache_dir       ,					)
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"       ,					cache_dir=model_args.cache_dir       ,					)
			else:
						# make sure only "validation" and "train" keys remain"
						__lowerCAmelCase							       =     DatasetDict()
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split="validation"       ,					cache_dir=model_args.cache_dir       ,					)
						__lowerCAmelCase							       =     load_dataset(
						    data_args.dataset_name       ,					data_args.dataset_config_name       ,					split=f"{data_args.train_split_name}"       ,					cache_dir=model_args.cache_dir       ,					)

			# only normalized-inputs-training is supported
			__lowerCAmelCase							       =     WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					do_normalize=_UpperCamelCase )

			def prepare_dataset(_UpperCamelCase ):
						# check that all files have the correct sampling rate
						__lowerCAmelCase       ,      __lowerCAmelCase							       =     librosa.load(batch[data_args.speech_file_column]       ,					sr=feature_extractor.sampling_rate )
						return batch

			# load audio files into numpy arrays
			__lowerCAmelCase							       =     datasets.map(
			    _UpperCamelCase       ,					num_proc=data_args.preprocessing_num_workers       ,					remove_columns=datasets["train"].column_names )

			# filter audio files that are too long
			__lowerCAmelCase							       =     vectorized_datasets.filter(
			    lambda _UpperCamelCase : len(data["speech"] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )

			def normalize(_UpperCamelCase ):
						return feature_extractor(batch["speech"]       ,					sampling_rate=feature_extractor.sampling_rate )

			# normalize and transform to `BatchFeatures`
			__lowerCAmelCase							       =     vectorized_datasets.map(
			    _UpperCamelCase       ,					batched=_UpperCamelCase       ,					num_proc=data_args.preprocessing_num_workers       ,					load_from_cache_file=not data_args.overwrite_cache       ,					remove_columns=vectorized_datasets["train"].column_names       ,					)

			# pretraining is only supported for "newer" stable layer norm architecture
			# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
			__lowerCAmelCase							       =     WavaVecaConfig.from_pretrained(
			    model_args.model_name_or_path       ,					cache_dir=model_args.cache_dir       ,					gradient_checkpointing=training_args.gradient_checkpointing       ,					)

			if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
						raise ValueError(
						    "PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"
						    " ``config.feat_extract_norm='layer'" )

			__lowerCAmelCase							       =     WavaVecaForPreTraining(_UpperCamelCase )

			__lowerCAmelCase							       =     DataCollatorForWavaVecaPretraining(model=_UpperCamelCase       ,					feature_extractor=_UpperCamelCase )

			__lowerCAmelCase							       =     WavaVecaPreTrainer(
			    model=_UpperCamelCase       ,					data_collator=_UpperCamelCase       ,					args=_UpperCamelCase       ,					train_dataset=vectorized_datasets["train"]       ,					eval_dataset=vectorized_datasets["validation"]       ,					tokenizer=_UpperCamelCase       ,					max_gumbel_temp=model_args.max_gumbel_temperature       ,					min_gumbel_temp=model_args.min_gumbel_temperature       ,					gumbel_temp_decay=model_args.gumbel_temperature_decay       ,					)
			trainer.train()


if __name__ == "__main__":
	main()




