





'''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
#
########################################################################


_lowercase       						=			16
_lowercase       						=			32

def    __UpperCamelCase	(	a    : Accelerator					,   a    : int = 16     ) ->int:
 snake_case				=			AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 snake_case				=			load_dataset('''glue'''					,   '''mrpc'''     )

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

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

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

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

 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

   _lowercase       						=			mocked_dataloaders  # noqa: F811

def    __UpperCamelCase	(	a    : str					,   a    : List[Any]     ) ->str:
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''					,   a     ) == "1":
  snake_case				=			2
 # Initialize accelerator
 snake_case				=			Accelerator(cpu=args.cpu					,   mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 snake_case				=			config['''lr''']
 snake_case				=			int(config['''num_epochs''']     )
 snake_case				=			int(config['''seed''']     )
 snake_case				=			int(config['''batch_size''']     )

 snake_case				=			evaluate.load('''glue'''					,   '''mrpc'''     )

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

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

 # 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).
 snake_case				=			model.to(accelerator.device     )

 # Instantiate optimizer
 snake_case				=			AdamW(params=model.parameters()					,   lr=a     )

 # Instantiate scheduler
 snake_case				=			get_linear_schedule_with_warmup(
     optimizer=a					,   num_warmup_steps=100					,   num_training_steps=(len(a     ) * 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.
 snake_case				,		snake_case				,		snake_case				,		snake_case				,		snake_case				=			accelerator.prepare(
     a					,   a					,   a					,   a					,   a     )

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

  model.eval()
  snake_case				=			0
  for step, batch in enumerate(a     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   with torch.no_grad():
    snake_case				=			model(**a     )
   snake_case				=			outputs.logits.argmax(dim=-1     )
   snake_case				,		snake_case				=			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(a     ) - 1:
     # Last batch needs to be truncated on distributed systems as it contains additional samples
     snake_case				=			predictions[: len(eval_dataloader.dataset     ) - samples_seen]
     snake_case				=			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=a					,   references=a					,   )

  snake_case				=			metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"""epoch {epoch}:"""					,   a     )

def    __UpperCamelCase	(	) ->Tuple:
 snake_case				=			argparse.ArgumentParser(description='''Simple example of training script.'''     )
 parser.add_argument(
     '''--mixed_precision'''					,   type=a					,   default=a					,   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.'''     )
 snake_case				=			parser.parse_args()
 snake_case				=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(a					,   a     )


if __name__ == "__main__":
   main()








'''simple docstring'''







import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
   from .features import FeatureType


_lowercase						,				_lowercase						,				_lowercase       						=			False, False, False
@dataclass
class 			_lowercase						:
     _UpperCAmelCase										=					None
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					True
     _UpperCAmelCase										=					None
     # Automatically constructed
     _UpperCAmelCase										=					"dict"
     _UpperCAmelCase										=					pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}					)
     _UpperCAmelCase										=					field(default='''Audio'''				,		init=__a				,		repr=__a					)







     def __call__( self    ) ->	Optional[Any]:
      return self.pa_type







     def     UpperCamelCase			( self    ,   A__    ) ->	dict:
      try:
       import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
      except ImportError as err:
       raise ImportError('''To support encoding audio data, please install \'soundfile\'.'''    ) from err
      if isinstance(A__    ,   A__    ):
       return {"bytes": None, "path": value}
      elif isinstance(A__    ,   A__    ):
       return {"bytes": value, "path": None}
      elif "array" in value:
       # convert the audio array to wav bytes
       snake_case				=			BytesIO()
       sf.write(A__    ,   value['''array''']    ,   value['''sampling_rate''']    ,   format='''wav'''    )
       return {"bytes": buffer.getvalue(), "path": None}
      elif value.get('''path'''    ) is not None and os.path.isfile(value['''path''']    ):
       # we set "bytes": None to not duplicate the data if they're already available locally
       if value["path"].endswith('''pcm'''    ):
        # "PCM" only has raw audio bytes
        if value.get('''sampling_rate'''    ) is None:
         # At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
         raise KeyError('''To use PCM files, please specify a \'sampling_rate\' in Audio object'''    )
        if value.get('''bytes'''    ):
         # If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
         snake_case				=			np.frombuffer(value['''bytes''']    ,   dtype=np.intaa    ).astype(np.floataa    ) / 3_27_67
        else:
         snake_case				=			np.memmap(value['''path''']    ,   dtype='''h'''    ,   mode='''r'''    ).astype(np.floataa    ) / 3_27_67

        snake_case				=			BytesIO(bytes()    )
        sf.write(A__    ,   A__    ,   value['''sampling_rate''']    ,   format='''wav'''    )
        return {"bytes": buffer.getvalue(), "path": None}
       else:
        return {"bytes": None, "path": value.get('''path'''    )}
      elif value.get('''bytes'''    ) is not None or value.get('''path'''    ) is not None:
       # store the audio bytes, and path is used to infer the audio format using the file extension
       return {"bytes": value.get('''bytes'''    ), "path": value.get('''path'''    )}
      else:
       raise ValueError(
           F"""An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."""    )







     def     UpperCamelCase			( self    ,   A__    ,   A__ = None    ) ->	dict:
      if not self.decode:
       raise RuntimeError('''Decoding is disabled for this feature. Please use Audio(decode=True) instead.'''    )

      snake_case				,		snake_case				=			(value['''path'''], BytesIO(value['''bytes''']    )) if value['''bytes'''] is not None else (value['''path'''], None)
      if path is None and file is None:
       raise ValueError(F"""An audio sample should have one of 'path' or 'bytes' but both are None in {value}."""    )

      try:
       import librosa
       import soundfile as sf
      except ImportError as err:
       raise ImportError('''To support decoding audio files, please install \'librosa\' and \'soundfile\'.'''    ) from err

      snake_case				=			xsplitext(A__    )[1][1:].lower() if path is not None else None
      if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
       raise RuntimeError(
           '''Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )
      elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
       raise RuntimeError(
           '''Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '''
           '''You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '''    )

      if file is None:
       snake_case				=			token_per_repo_id or {}
       snake_case				=			path.split('''::'''    )[-1]
       try:
        snake_case				=			string_to_dict(A__    ,   config.HUB_DATASETS_URL    )['''repo_id''']
        snake_case				=			token_per_repo_id[repo_id]
       except (ValueError, KeyError):
        snake_case				=			None

       with xopen(A__    ,   '''rb'''    ,   use_auth_token=A__    ) as f:
        snake_case				,		snake_case				=			sf.read(A__    )

      else:
       snake_case				,		snake_case				=			sf.read(A__    )

      snake_case				=			array.T
      if self.mono:
       snake_case				=			librosa.to_mono(A__    )
      if self.sampling_rate and self.sampling_rate != sampling_rate:
       snake_case				=			librosa.resample(A__    ,   orig_sr=A__    ,   target_sr=self.sampling_rate    )
       snake_case				=			self.sampling_rate

      return {"path": path, "array": array, "sampling_rate": sampling_rate}







     def     UpperCamelCase			( self    ) ->	Union["FeatureType", Dict[str, "FeatureType"]]:
      from .features import Value

      if self.decode:
       raise ValueError('''Cannot flatten a decoded Audio feature.'''    )
      return {
          "bytes": Value('''binary'''    ),
          "path": Value('''string'''    ),
      }







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:
      if pa.types.is_string(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, storage]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_binary(storage.type    ):
       snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([storage, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      elif pa.types.is_struct(storage.type    ) and storage.type.get_all_field_indices('''array'''    ):
       snake_case				=			pa.array([Audio().encode_example(A__    ) if x is not None else None for x in storage.to_pylist()]    )
      elif pa.types.is_struct(storage.type    ):
       if storage.type.get_field_index('''bytes'''    ) >= 0:
        snake_case				=			storage.field('''bytes'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.binary()    )
       if storage.type.get_field_index('''path'''    ) >= 0:
        snake_case				=			storage.field('''path'''    )
       else:
        snake_case				=			pa.array([None] * len(A__    )    ,   type=pa.string()    )
       snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=storage.is_null()    )
      return array_cast(A__    ,   self.pa_type    )







     def     UpperCamelCase			( self    ,   A__    ) ->	pa.StructArray:

      @no_op_if_value_is_null
      def path_to_bytes(A__    ):
       with xopen(A__    ,   '''rb'''    ) as f:
        snake_case				=			f.read()
       return bytes_

      snake_case				=			pa.array(
          [
              (path_to_bytes(x['''path''']    ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
              for x in storage.to_pylist()
          ]    ,   type=pa.binary()    ,   )
      snake_case				=			pa.array(
          [os.path.basename(A__    ) if path is not None else None for path in storage.field('''path'''    ).to_pylist()]    ,   type=pa.string()    ,   )
      snake_case				=			pa.StructArray.from_arrays([bytes_array, path_array]    ,   ['''bytes''', '''path''']    ,   mask=bytes_array.is_null()    )
      return array_cast(A__    ,   self.pa_type    )

