

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


lowerCAmelCase_      , lowerCAmelCase_      , lowerCAmelCase_			      =      False, False, False





@dataclass
class       snake_case_  :







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  Optional[int]		     =							None
			SCREAMING_SNAKE_CASE	:  bool		     =							True
			SCREAMING_SNAKE_CASE	:  bool		     =							True
			SCREAMING_SNAKE_CASE	:  Optional[str]		     =							None
			# Automatically constructed
			SCREAMING_SNAKE_CASE	:  ClassVar[str]		     =							"dict"
			SCREAMING_SNAKE_CASE	:  ClassVar[Any]		     =							pa.struct({"bytes": pa.binary(), "path": pa.string()}   )
			SCREAMING_SNAKE_CASE	:  str		     =							field(default="Audio"      ,			init=__A      ,			repr=__A   )







			def __call__(	self					:							Dict				) ->Any:
									return self.pa_type







			def 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							Union[str, bytes, dict]				) ->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(_UpperCamelCase				,  _UpperCamelCase				):
															return {"bytes": None, "path": value}
									elif isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															return {"bytes": value, "path": None}
									elif "array" in value:
															# convert the audio array to wav bytes
															snake_case_  =      BytesIO()
															sf.write(_UpperCamelCase				,  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_2_7_6_7
																					else:
																											snake_case_  =      np.memmap(value['''path''']				,  dtype='''h'''				,  mode='''r'''				).astype(np.floataa				) / 3_2_7_6_7

																					snake_case_  =      BytesIO(bytes()				)
																					sf.write(_UpperCamelCase				,  _UpperCamelCase				,  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 							snake_case__(	self					:							Optional[int]				,  _UpperCamelCase					:							dict				,  _UpperCamelCase					:							Optional[Dict[str, Union[str, bool, None]]] = 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(_UpperCamelCase				)[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(_UpperCamelCase				,  config.HUB_DATASETS_URL				)['''repo_id''']
																					snake_case_  =      token_per_repo_id[repo_id]
															except (ValueError, KeyError):
																					snake_case_  =      None

															with xopen(_UpperCamelCase				,  '''rb'''				,  use_auth_token=_UpperCamelCase				) as f:
																					snake_case_,       snake_case_  =      sf.read(_UpperCamelCase				)

									else:
															snake_case_,       snake_case_  =      sf.read(_UpperCamelCase				)

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

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







			def 							snake_case__(	self					:							Optional[Any]				) ->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 							snake_case__(	self					:							Any				,  _UpperCamelCase					:							Union[pa.StringArray, pa.StructArray]				) ->pa.StructArray:
									if pa.types.is_string(storage.type				):
															snake_case_  =      pa.array([None] * len(_UpperCamelCase				)				,  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(_UpperCamelCase				)				,  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(_UpperCamelCase				) 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(_UpperCamelCase				)				,  type=pa.binary()				)
															if storage.type.get_field_index('''path'''				) >= 0:
																					snake_case_  =      storage.field('''path'''				)
															else:
																					snake_case_  =      pa.array([None] * len(_UpperCamelCase				)				,  type=pa.string()				)
															snake_case_  =      pa.StructArray.from_arrays([bytes_array, path_array]				,  ['''bytes''', '''path''']				,  mask=storage.is_null()				)
									return array_cast(_UpperCamelCase				,  self.pa_type				)







			def 							snake_case__(	self					:							List[Any]				,  _UpperCamelCase					:							pa.StructArray				) ->pa.StructArray:

									@no_op_if_value_is_null
									def path_to_bytes(_UpperCamelCase					:							Any				):
															with xopen(_UpperCamelCase				,  '''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(_UpperCamelCase				) 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(_UpperCamelCase				,  self.pa_type				)



'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 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 							=  16
_UpperCamelCase 							=  32


def 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE = 16							):




     '''simple docstring'''





     __A	:       Tuple      =							AutoTokenizer.from_pretrained("bert-base-cased"							)
     __A	:       List[str]      =							load_dataset("glue"       ,					"mrpc"							)

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

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

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

     # Instantiate dataloaders.
     __A	:       Optional[int]      =							DataLoader(
         tokenized_datasets["train"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_SNAKE_CASE							)
     __A	:       Tuple      =							DataLoader(
         tokenized_datasets["validation"]       ,					shuffle=SCREAMING_SNAKE_CASE       ,					collate_fn=SCREAMING_SNAKE_CASE       ,					batch_size=SCREAMING_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 		_lowercase (SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,					SCREAMING_SNAKE_CASE							) == "1":
          __A	:       int      =							2
     # New Code #
     __A	:       List[str]      =							int(args.gradient_accumulation_steps							)
     __A	:       int      =							int(args.local_sgd_steps							)
     # Initialize accelerator
     __A	:       int      =							Accelerator(
         cpu=args.cpu       ,					mixed_precision=args.mixed_precision       ,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE							)
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"							)
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     __A	:       Tuple      =							config["lr"]
     __A	:       Union[str, Any]      =							int(config["num_epochs"]							)
     __A	:       Optional[int]      =							int(config["seed"]							)
     __A	:       Optional[Any]      =							int(config["batch_size"]							)

     __A	:       Optional[int]      =							evaluate.load("glue"       ,					"mrpc"							)

     set_seed(SCREAMING_SNAKE_CASE							)
     __A  ,__A	:       Tuple      =							get_dataloaders(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     __A	:       List[str]      =							AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,					return_dict=SCREAMING_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	:       Union[str, Any]      =							model.to(accelerator.device							)

     # Instantiate optimizer
     __A	:       str      =							AdamW(params=model.parameters()       ,					lr=SCREAMING_SNAKE_CASE							)

     # Instantiate scheduler
     __A	:       Optional[int]      =							get_linear_schedule_with_warmup(
         optimizer=SCREAMING_SNAKE_CASE       ,					num_warmup_steps=100       ,					num_training_steps=(len(SCREAMING_SNAKE_CASE							) * num_epochs)       ,					)

     # 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	:       List[str]      =							accelerator.prepare(
         SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)

     # Now we train the model
     for epoch in range(SCREAMING_SNAKE_CASE							):
          model.train()
          with LocalSGD(
              accelerator=SCREAMING_SNAKE_CASE       ,					model=SCREAMING_SNAKE_CASE       ,					local_sgd_steps=SCREAMING_SNAKE_CASE       ,					enabled=local_sgd_steps is not None							) as local_sgd:
               for step, batch in enumerate(SCREAMING_SNAKE_CASE							):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device							)
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(SCREAMING_SNAKE_CASE							):
                         __A	:       List[Any]      =							model(**SCREAMING_SNAKE_CASE							)
                         __A	:       List[str]      =							output.loss
                         accelerator.backward(SCREAMING_SNAKE_CASE							)
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

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

          __A	:       List[Any]      =							metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:"       ,					SCREAMING_SNAKE_CASE							)


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Dict      =							argparse.ArgumentParser(description="Simple example of training script."							)
     parser.add_argument(
         "--mixed_precision"       ,					type=SCREAMING_SNAKE_CASE       ,					default=SCREAMING_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."       ,					)
     # New Code #
     parser.add_argument(
         "--gradient_accumulation_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=1       ,					help="The number of minibatches to be ran before gradients are accumulated."       ,					)
     parser.add_argument(
         "--local_sgd_steps"       ,					type=SCREAMING_SNAKE_CASE       ,					default=8       ,					help="Number of local SGD steps or None to disable local SGD"							)
     parser.add_argument("--cpu"       ,					action="store_true"       ,					help="If passed, will train on the CPU."							)
     __A	:       List[str]      =							parser.parse_args()
     __A	:       List[str]      =							{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(SCREAMING_SNAKE_CASE       ,					SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
      main()

