




"""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


a      =							False, False, False






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


						def __call__(							self				:			str					):
										return self.pa_type


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			int					):




										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
														lowerCAmelCase  =				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!)
																						lowerCAmelCase  =				np.frombuffer(value["""bytes"""]						,						dtype=np.intaa					).astype(np.floataa					) / 3_2767
																		else:
																						lowerCAmelCase  =				np.memmap(value["""path"""]						,						dtype="""h"""						,						mode="""r"""					).astype(np.floataa					) / 3_2767

																		lowerCAmelCase  =				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   __lowercase (							self				:			int						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Any = None					):




										if not self.decode:
														raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""					)

										lowerCAmelCase  =				(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

										lowerCAmelCase  =				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:
														lowerCAmelCase  =				token_per_repo_id or {}
														lowerCAmelCase  =				path.split("""::"""					)[-1]
														try:
																		lowerCAmelCase  =				string_to_dict(A_						,						config.HUB_DATASETS_URL					)["repo_id"]
																		lowerCAmelCase  =				token_per_repo_id[repo_id]
														except (ValueError, KeyError):
																		lowerCAmelCase  =				None

														with xopen(A_						,						"""rb"""						,						use_auth_token=A_					) as f:
																		lowerCAmelCase  =				sf.read(A_					)

										else:
														lowerCAmelCase  =				sf.read(A_					)

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

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


						def   __lowercase (							self				:			Optional[Any]					):
										from .features import Value

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


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			Optional[Any]					):




										if pa.types.is_string(storage.type					):
														lowerCAmelCase  =				pa.array([None] * len(A_					)						,						type=pa.binary()					)
														lowerCAmelCase  =				pa.StructArray.from_arrays([bytes_array, storage]						,						["""bytes""", """path"""]						,						mask=storage.is_null()					)
										elif pa.types.is_binary(storage.type					):
														lowerCAmelCase  =				pa.array([None] * len(A_					)						,						type=pa.string()					)
														lowerCAmelCase  =				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"""					):
														lowerCAmelCase  =				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:
																		lowerCAmelCase  =				storage.field("""bytes"""					)
														else:
																		lowerCAmelCase  =				pa.array([None] * len(A_					)						,						type=pa.binary()					)
														if storage.type.get_field_index("""path"""					) >= 0:
																		lowerCAmelCase  =				storage.field("""path"""					)
														else:
																		lowerCAmelCase  =				pa.array([None] * len(A_					)						,						type=pa.string()					)
														lowerCAmelCase  =				pa.StructArray.from_arrays([bytes_array, path_array]						,						["""bytes""", """path"""]						,						mask=storage.is_null()					)
										return array_cast(A_						,						self.pa_type					)




						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			List[str]					):




										@no_op_if_value_is_null
										def path_to_bytes(lowerCAmelCase				:			List[str]					):
														with xopen(A_						,						"""rb"""					) as f:
																		lowerCAmelCase  =				f.read()
														return bytes_

										lowerCAmelCase  =				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()						,						)
										lowerCAmelCase  =				pa.array(
										    [os.path.basename(A_					) if path is not None else None for path in storage.field("""path"""					).to_pylist()]						,						type=pa.string()						,						)
										lowerCAmelCase  =				pa.StructArray.from_arrays([bytes_array, path_array]						,						["""bytes""", """path"""]						,						mask=bytes_array.is_null()					)
										return array_cast(A_						,						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


########################################################################
# 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
#
########################################################################

a      =							1_6
a      =							3_2





def 							lowercase					(snake_case__   : Accelerator      ,       snake_case__   : int = 16 )     ->  Dict:


				'''simple docstring'''






				lowerCAmelCase  =				AutoTokenizer.from_pretrained("""bert-base-cased""" )
				lowerCAmelCase  =				load_dataset("""glue"""      ,       """mrpc""" )

				def tokenize_function(snake_case__   : List[Any] ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase  =				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():
								lowerCAmelCase  =				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
				lowerCAmelCase  =				tokenized_datasets.rename_column("""label"""      ,       """labels""" )

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

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

				# Instantiate dataloaders.
				lowerCAmelCase  =				DataLoader(
				    tokenized_datasets["""train"""]      ,       shuffle=snake_case__      ,       collate_fn=snake_case__      ,       batch_size=snake_case__ )
				lowerCAmelCase  =				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

		a      =							mocked_dataloaders  # noqa: F811





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Tuple )     ->  int:


				'''simple docstring'''






				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""      ,       snake_case__ ) == "1":
								lowerCAmelCase  =				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:
								lowerCAmelCase  =				Accelerator(
								    cpu=args.cpu      ,       mixed_precision=args.mixed_precision      ,       log_with="""all"""      ,       project_dir=args.project_dir )
				else:
								lowerCAmelCase  =				Accelerator(cpu=args.cpu      ,       mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase  =				config["""lr"""]
				lowerCAmelCase  =				int(config["""num_epochs"""] )
				lowerCAmelCase  =				int(config["""seed"""] )
				lowerCAmelCase  =				int(config["""batch_size"""] )
				set_seed(snake_case__ )

				lowerCAmelCase ,						lowerCAmelCase  =				get_dataloaders(snake_case__      ,       snake_case__ )
				lowerCAmelCase  =				evaluate.load("""glue"""      ,       """mrpc""" )

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

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase  =				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).
				lowerCAmelCase  =				model.to(accelerator.device )

				# Instantiate optimizer
				lowerCAmelCase  =				AdamW(params=model.parameters()      ,       lr=snake_case__ )

				# Instantiate scheduler
				lowerCAmelCase  =				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.
				lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				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:
								lowerCAmelCase  =				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:
												lowerCAmelCase  =				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 )
												lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase  =				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():
																lowerCAmelCase  =				model(**snake_case__ )
												lowerCAmelCase  =				outputs.logits.argmax(dim=-1 )
												lowerCAmelCase ,						lowerCAmelCase  =				accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
												metric.add_batch(
												    predictions=snake_case__      ,       references=snake_case__      ,       )

								lowerCAmelCase  =				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 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				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"""      ,       )
				lowerCAmelCase  =				parser.parse_args()
				lowerCAmelCase  =				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(snake_case__      ,       snake_case__ )


if __name__ == "__main__":
		main()





