


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


__lowercase	:						Optional[int]       =	16
__lowercase	:						List[str]       =	32






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Accelerator						,      _SCREAMING_SNAKE_CASE      :   int = 16   ):
					__a			:       Union[str, Any]					    =  AutoTokenizer.from_pretrained('bert-base-cased'   )
					__a			:       Optional[Any]					    =  load_dataset('glue'						,      'mrpc'   )

					def tokenize_function(_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
										# max_length=None => use the model max length (it's actually the default)
										__a			:       str					    =  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			:       List[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[str]					    =  tokenized_datasets.rename_column('label'						,      'labels'   )

					def collate_fn(_SCREAMING_SNAKE_CASE      :   Dict   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__a			:       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":
															__a			:       List[Any]					    =  16
										elif accelerator.mixed_precision != "no":
															__a			:       Any					    =  8
										else:
															__a			:       Optional[int]					    =  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			:       Union[str, Any]					    =  DataLoader(
					    tokenized_datasets['train']						,      shuffle=_SCREAMING_SNAKE_CASE						,      collate_fn=_SCREAMING_SNAKE_CASE						,      batch_size=_SCREAMING_SNAKE_CASE   )
					__a			:       List[str]					    =  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

	__lowercase	:						Tuple       =	mocked_dataloaders  # noqa: F811






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   Dict   ):
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS'						,      _SCREAMING_SNAKE_CASE   ) == "1":
										__a			:       Optional[int]					    =  2
					# New Code #
					__a			:       Union[str, Any]					    =  int(args.gradient_accumulation_steps   )
					__a			:       Dict					    =  int(args.local_sgd_steps   )
					# Initialize accelerator
					__a			:       Tuple					    =  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			:       Dict					    =  config['lr']
					__a			:       Any					    =  int(config['num_epochs']   )
					__a			:       int					    =  int(config['seed']   )
					__a			:       List[str]					    =  int(config['batch_size']   )

					__a			:       Any					    =  evaluate.load('glue'						,      'mrpc'   )

					set_seed(_SCREAMING_SNAKE_CASE   )
					__a    ,				__a			:       int					    =  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			:       Dict					    =  model.to(accelerator.device   )

					# Instantiate optimizer
					__a			:       int					    =  AdamW(params=model.parameters()						,      lr=_SCREAMING_SNAKE_CASE   )

					# Instantiate scheduler
					__a			:       Any					    =  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			:       Any					    =  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			:       int					    =  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			:       Dict					    =  model(**_SCREAMING_SNAKE_CASE   )
															__a			:       Tuple					    =  outputs.logits.argmax(dim=-1   )
															__a    ,				__a			:       Union[str, Any]					    =  accelerator.gather_for_metrics((predictions, batch['labels'])   )
															metric.add_batch(
															    predictions=_SCREAMING_SNAKE_CASE						,      references=_SCREAMING_SNAKE_CASE						,      )

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






def   lowerCamelCase							():
					__a			:       Optional[Any]					    =  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			:       Optional[Any]					    =  parser.parse_args()
					__a			:       str					    =  {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
					training_function(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()











import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_a      	=  logging.get_logger(__name__)





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "AutoTokenizer"
	UpperCamelCase__								=      ["tokenizer"]

	UpperCamelCase__								=      {
	    "semantic_prompt": 1,
	    "coarse_prompt": 2,
	    "fine_prompt": 2,
	}

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=None    ):
							"""simple docstring"""







							super().__init__(UpperCAmelCase    )

							_UpperCAmelCase									=			speaker_embeddings

	@classmethod
	def        UpperCamelCase    (							cls       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							if speaker_embeddings_dict_path is not None:
													_UpperCAmelCase									=			get_file_from_repo(
													    UpperCAmelCase       ,    UpperCAmelCase       ,    subfolder=kwargs.pop('subfolder'       ,    UpperCAmelCase    )       ,    cache_dir=kwargs.pop('cache_dir'       ,    UpperCAmelCase    )       ,    force_download=kwargs.pop('force_download'       ,    UpperCAmelCase    )       ,    proxies=kwargs.pop('proxies'       ,    UpperCAmelCase    )       ,    resume_download=kwargs.pop('resume_download'       ,    UpperCAmelCase    )       ,    local_files_only=kwargs.pop('local_files_only'       ,    UpperCAmelCase    )       ,    use_auth_token=kwargs.pop('use_auth_token'       ,    UpperCAmelCase    )       ,    revision=kwargs.pop('revision'       ,    UpperCAmelCase    )       ,    )
													if speaker_embeddings_path is None:
																			logger.warning(
																			    F"""`{os.path.join(UpperCAmelCase       ,    UpperCAmelCase    )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""    )
																			_UpperCAmelCase									=			None
													else:
																			with open(UpperCAmelCase    ) as speaker_embeddings_json:
																									_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			None

							_UpperCAmelCase									=			AutoTokenizer.from_pretrained(UpperCAmelCase       ,    **UpperCAmelCase    )

							return cls(tokenizer=UpperCAmelCase       ,    speaker_embeddings=UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase="speaker_embeddings_path.json"       ,    UpperCAmelCase="speaker_embeddings"       ,    UpperCAmelCase = False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if self.speaker_embeddings is not None:
													os.makedirs(os.path.join(UpperCAmelCase       ,    UpperCAmelCase       ,    'v2'    )       ,    exist_ok=UpperCAmelCase    )

													_UpperCAmelCase									=			{}

													_UpperCAmelCase									=			save_directory

													for prompt_key in self.speaker_embeddings:
																			if prompt_key != "repo_or_path":
																									_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

																									_UpperCAmelCase									=			{}
																									for key in self.speaker_embeddings[prompt_key]:
																															np.save(
																															    os.path.join(
																															        embeddings_dict['repo_or_path']       ,    UpperCAmelCase       ,    F"""{prompt_key}_{key}"""    )       ,    voice_preset[key]       ,    allow_pickle=UpperCAmelCase       ,    )
																															_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    F"""{prompt_key}_{key}.npy"""    )

																									_UpperCAmelCase									=			tmp_dict

													with open(os.path.join(UpperCAmelCase       ,    UpperCAmelCase    )       ,    'w'    ) as fp:
																			json.dump(UpperCAmelCase       ,    UpperCAmelCase    )

							super().save_pretrained(UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.speaker_embeddings[voice_preset]

							_UpperCAmelCase									=			{}
							for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset_paths:
																			raise ValueError(
																			    F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""    )

													_UpperCAmelCase									=			get_file_from_repo(
													    self.speaker_embeddings.get('repo_or_path'       ,    '/'    )       ,    voice_preset_paths[key]       ,    subfolder=kwargs.pop('subfolder'       ,    UpperCAmelCase    )       ,    cache_dir=kwargs.pop('cache_dir'       ,    UpperCAmelCase    )       ,    force_download=kwargs.pop('force_download'       ,    UpperCAmelCase    )       ,    proxies=kwargs.pop('proxies'       ,    UpperCAmelCase    )       ,    resume_download=kwargs.pop('resume_download'       ,    UpperCAmelCase    )       ,    local_files_only=kwargs.pop('local_files_only'       ,    UpperCAmelCase    )       ,    use_auth_token=kwargs.pop('use_auth_token'       ,    UpperCAmelCase    )       ,    revision=kwargs.pop('revision'       ,    UpperCAmelCase    )       ,    )
													if path is None:
																			raise ValueError(
																			    F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"       ,    "/"    )       ,    voice_preset_paths[key]    )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""    )

													_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							return voice_preset_dict

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = None    ):
							"""simple docstring"""







							for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
													if key not in voice_preset:
																			raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""    )

													if not isinstance(voice_preset[key]       ,    np.ndarray    ):
																			raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."""    )

													if len(voice_preset[key].shape    ) != self.preset_shape[key]:
																			raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]    )}D ndarray."""    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase=None       ,    UpperCAmelCase="pt"       ,    UpperCAmelCase=256       ,    UpperCAmelCase=False       ,    UpperCAmelCase=True       ,    UpperCAmelCase=False       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if voice_preset is not None and not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													if (
													    isinstance(UpperCAmelCase       ,    UpperCAmelCase    )
													    and self.speaker_embeddings is not None
													    and voice_preset in self.speaker_embeddings
													):
																			_UpperCAmelCase									=			self._load_voice_preset(UpperCAmelCase    )

													else:
																			if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) and not voice_preset.endswith('.npz'    ):
																									_UpperCAmelCase									=			voice_preset + '.npz'

																			_UpperCAmelCase									=			np.load(UpperCAmelCase    )

							if voice_preset is not None:
													self._validate_voice_preset_dict(UpperCAmelCase       ,    **UpperCAmelCase    )
													_UpperCAmelCase									=			BatchFeature(data=UpperCAmelCase       ,    tensor_type=UpperCAmelCase    )

							_UpperCAmelCase									=			self.tokenizer(
							    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    padding='max_length'       ,    max_length=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

							if voice_preset is not None:
													_UpperCAmelCase									=			voice_preset

							return encoded_text
