


"""simple docstring"""






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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


_lowerCAmelCase							:    Optional[int]					   =	16
_lowerCAmelCase							:    List[Any]					   =	32







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase = 16       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     AutoTokenizer.from_pretrained("bert-base-cased"       )
   _lowerCamelCase   :				Optional[int]							=     load_dataset("glue"			,		"mrpc"       )

   def tokenize_function(_lowerCamelCase       ):
      # max_length=None => use the model max length (it's actually the default)
      _lowerCamelCase   :				Optional[int]							=     tokenizer(examples["sentence1"]			,		examples["sentence2"]			,		truncation=_lowerCamelCase			,		max_length=_lowerCamelCase       )
      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():
      _lowerCamelCase   :				Optional[Any]							=     datasets.map(
          _lowerCamelCase			,		batched=_lowerCamelCase			,		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
   _lowerCamelCase   :				Optional[int]							=     tokenized_datasets.rename_column("label"			,		"labels"       )

   def collate_fn(_lowerCamelCase       ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _lowerCamelCase   :				Optional[int]							=     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":
         _lowerCamelCase   :				Any							=     16
      elif accelerator.mixed_precision != "no":
         _lowerCamelCase   :				Dict							=     8
      else:
         _lowerCamelCase   :				Optional[int]							=     None

      return tokenizer.pad(
          _lowerCamelCase			,		padding="longest"			,		max_length=_lowerCamelCase			,		pad_to_multiple_of=_lowerCamelCase			,		return_tensors="pt"			,		)

   # Instantiate dataloaders.
   _lowerCamelCase   :				Union[str, Any]							=     DataLoader(
       tokenized_datasets["train"]			,		shuffle=_lowerCamelCase			,		collate_fn=_lowerCamelCase			,		batch_size=_lowerCamelCase       )
   _lowerCamelCase   :				int							=     DataLoader(
       tokenized_datasets["validation"]			,		shuffle=_lowerCamelCase			,		collate_fn=_lowerCamelCase			,		batch_size=_lowerCamelCase       )

   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

  _lowerCAmelCase							:    Dict					   =	mocked_dataloaders  # noqa: F811







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     int:
   '''simple docstring'''





   if os.environ.get("TESTING_MOCKED_DATALOADERS"			,		_lowerCamelCase       ) == "1":
      _lowerCamelCase   :				Optional[Any]							=     2
   # Initialize accelerator
   _lowerCamelCase   :				int							=     Accelerator(cpu=args.cpu			,		mixed_precision=args.mixed_precision       )
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _lowerCamelCase   :				Any							=     config["lr"]
   _lowerCamelCase   :				Tuple							=     int(config["num_epochs"]       )
   _lowerCamelCase   :				Union[str, Any]							=     int(config["seed"]       )
   _lowerCamelCase   :				Optional[Any]							=     int(config["batch_size"]       )

   _lowerCamelCase   :				Tuple							=     evaluate.load("glue"			,		"mrpc"       )

   # If the batch size is too big we use gradient accumulation
   _lowerCamelCase   :				Optional[Any]							=     1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _lowerCamelCase   :				Union[str, Any]							=     batch_size // MAX_GPU_BATCH_SIZE
      _lowerCamelCase   :				Dict							=     MAX_GPU_BATCH_SIZE

   set_seed(_lowerCamelCase       )
   _lowerCamelCase,					_lowerCamelCase   :				Any							=     get_dataloaders(_lowerCamelCase			,		_lowerCamelCase       )
   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _lowerCamelCase   :				str							=     AutoModelForSequenceClassification.from_pretrained("bert-base-cased"			,		return_dict=_lowerCamelCase       )

   # 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).
   _lowerCamelCase   :				Tuple							=     model.to(accelerator.device       )

   # Instantiate optimizer
   _lowerCamelCase   :				Any							=     AdamW(params=model.parameters()			,		lr=_lowerCamelCase       )

   # Instantiate scheduler
   _lowerCamelCase   :				Any							=     get_linear_schedule_with_warmup(
       optimizer=_lowerCamelCase			,		num_warmup_steps=100			,		num_training_steps=(len(_lowerCamelCase       ) * 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.
   _lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase   :				int							=     accelerator.prepare(
       _lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )

   # Now we train the model
   for epoch in range(_lowerCamelCase       ):
      model.train()
      for step, batch in enumerate(_lowerCamelCase       ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device       )
         _lowerCamelCase   :				Dict							=     model(**_lowerCamelCase       )
         _lowerCamelCase   :				List[str]							=     outputs.loss
         _lowerCamelCase   :				List[Any]							=     loss / gradient_accumulation_steps
         accelerator.backward(_lowerCamelCase       )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      _lowerCamelCase   :				Any							=     0
      for step, batch in enumerate(_lowerCamelCase       ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device       )
         with torch.no_grad():
            _lowerCamelCase   :				str							=     model(**_lowerCamelCase       )
         _lowerCamelCase   :				Tuple							=     outputs.logits.argmax(dim=-1       )
         _lowerCamelCase,					_lowerCamelCase   :				int							=     accelerator.gather((predictions, batch["labels"])       )
         # New Code #
         # First we check if it's a distributed system
         if accelerator.use_distributed:
            # Then see if we're on the last batch of our eval dataloader
            if step == len(_lowerCamelCase       ) - 1:
               # Last batch needs to be truncated on distributed systems as it contains additional samples
               _lowerCamelCase   :				Dict							=     predictions[: len(eval_dataloader.dataset       ) - samples_seen]
               _lowerCamelCase   :				Optional[Any]							=     references[: len(eval_dataloader.dataset       ) - samples_seen]
            else:
               # Otherwise we add the number of samples seen
               samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
         metric.add_batch(
             predictions=_lowerCamelCase			,		references=_lowerCamelCase			,		)

      _lowerCamelCase   :				int							=     metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F"""epoch {epoch}:"""			,		_lowerCamelCase       )







def        lowerCamelCase_(		)    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     argparse.ArgumentParser(description="Simple example of training script."       )
   parser.add_argument(
       "--mixed_precision"			,		type=_lowerCamelCase			,		default=_lowerCamelCase			,		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."       )
   _lowerCamelCase   :				List[str]							=     parser.parse_args()
   _lowerCamelCase   :				List[Any]							=     {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(_lowerCamelCase			,		_lowerCamelCase       )


if __name__ == "__main__":
  main()





'''simple docstring'''







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase      :   Optional[int]      =       logging.get_logger(__name__)

lowercase      :   Union[str, Any]      =       {
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     List[Any]          =					"""unispeech-sat"""







     def __init__(				self							,__a=32							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.1							,__a=0.1							,__a=0.02							,__a=1E-5							,__a="group"							,__a="gelu"							,__a=(512, 512, 512, 512, 512, 512, 512)							,__a=(5, 2, 2, 2, 2, 2, 2)							,__a=(10, 3, 3, 3, 3, 2, 2)							,__a=False							,__a=128							,__a=16							,__a=False							,__a=True							,__a=0.05							,__a=10							,__a=2							,__a=0.0							,__a=10							,__a=0							,__a=320							,__a=2							,__a=0.1							,__a=100							,__a=256							,__a=256							,__a=0.1							,__a="mean"							,__a=False							,__a=False							,__a=256							,__a=(512, 512, 512, 512, 1_500)							,__a=(5, 3, 3, 1, 1)							,__a=(1, 2, 3, 1, 1)							,__a=512							,__a=0							,__a=1							,__a=2							,__a=504							,**__a							,)      ->				str:
          super().__init__(**__a							,pad_token_id=__a							,bos_token_id=__a							,eos_token_id=__a							)
          snake_case			:      List[Any]        =			hidden_size
          snake_case			:      Any        =			feat_extract_norm
          snake_case			:      Dict        =			feat_extract_activation
          snake_case			:      List[Any]        =			list(__a							)
          snake_case			:      int        =			list(__a							)
          snake_case			:      Dict        =			list(__a							)
          snake_case			:      int        =			conv_bias
          snake_case			:      Optional[Any]        =			num_conv_pos_embeddings
          snake_case			:      Any        =			num_conv_pos_embedding_groups
          snake_case			:      Dict        =			len(self.conv_dim							)
          snake_case			:      str        =			num_hidden_layers
          snake_case			:      Optional[Any]        =			intermediate_size
          snake_case			:      Optional[Any]        =			hidden_act
          snake_case			:      Union[str, Any]        =			num_attention_heads
          snake_case			:      str        =			hidden_dropout
          snake_case			:      Tuple        =			attention_dropout
          snake_case			:      Tuple        =			activation_dropout
          snake_case			:      List[Any]        =			feat_proj_dropout
          snake_case			:      List[str]        =			final_dropout
          snake_case			:      Union[str, Any]        =			layerdrop
          snake_case			:      Optional[int]        =			layer_norm_eps
          snake_case			:      int        =			initializer_range
          snake_case			:      List[Any]        =			vocab_size
          snake_case			:      Any        =			num_clusters
          snake_case			:      Dict        =			do_stable_layer_norm
          snake_case			:      int        =			use_weighted_layer_sum

          if (
              (len(self.conv_stride							) != self.num_feat_extract_layers)
              or (len(self.conv_kernel							) != self.num_feat_extract_layers)
              or (len(self.conv_dim							) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
                   """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
                   F''' {len(self.conv_dim							)}`, `len(config.conv_stride) = {len(self.conv_stride							)}`,'''
                   F''' `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          snake_case			:      Dict        =			apply_spec_augment
          snake_case			:      Optional[int]        =			mask_time_prob
          snake_case			:      int        =			mask_time_length
          snake_case			:      int        =			mask_time_min_masks
          snake_case			:      int        =			mask_feature_prob
          snake_case			:      Optional[Any]        =			mask_feature_length
          snake_case			:      List[Any]        =			mask_feature_min_masks

          # parameters for pretraining with codevector quantized representations
          snake_case			:      Optional[Any]        =			num_codevectors_per_group
          snake_case			:      Optional[int]        =			num_codevector_groups
          snake_case			:      Any        =			contrastive_logits_temperature
          snake_case			:      List[str]        =			feat_quantizer_dropout
          snake_case			:      Dict        =			num_negatives
          snake_case			:      int        =			codevector_dim
          snake_case			:      Optional[int]        =			proj_codevector_dim
          snake_case			:      Union[str, Any]        =			diversity_loss_weight

          # ctc loss
          snake_case			:      Union[str, Any]        =			ctc_loss_reduction
          snake_case			:      Optional[int]        =			ctc_zero_infinity

          # SequenceClassification-specific parameter. Feel free to ignore for other classes.
          snake_case			:      Dict        =			classifier_proj_size

          # XVector-specific parameters. Feel free to ignore for other classes.
          snake_case			:      int        =			list(__a							)
          snake_case			:      Tuple        =			list(__a							)
          snake_case			:      List[Any]        =			list(__a							)
          snake_case			:      Dict        =			xvector_output_dim







     @property
     def 			snake_case_      (				self							)      ->				List[str]:
          return functools.reduce(operator.mul							,self.conv_stride							,1							)



