
"""simple docstring"""




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


lowercase__		:Tuple			  =   logging.get_logger(__name__)







class     lowercase		( lowerCamelCase__		):
							lowercase_	:	Any	 ='AutoTokenizer'
							lowercase_	:	Union[str, Any]	 =['tokenizer']

							lowercase_	:	List[Any]	 ={
							    'semantic_prompt': 1,
							    'coarse_prompt': 2,
							    'fine_prompt': 2,
							}




							def __init__(    self       ,A__       ,A__=None):
												super().__init__(A__)

												lowercase						     =						speaker_embeddings




							@classmethod
							def 			A__				(    cls       ,A__       ,A__="speaker_embeddings_path.json"       ,**A__):







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

												lowercase						     =						AutoTokenizer.from_pretrained(A__       ,**A__)

												return cls(tokenizer=A__       ,speaker_embeddings=A__)




							def 			A__				(    self       ,A__       ,A__="speaker_embeddings_path.json"       ,A__="speaker_embeddings"       ,A__ = False       ,**A__       ,):







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

																	lowercase						     =						{}

																	lowercase						     =						save_directory

																	for prompt_key in self.speaker_embeddings:
																						if prompt_key != "repo_or_path":
																											lowercase						     =						self._load_voice_preset(A__)

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

																											lowercase						     =						tmp_dict

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

												super().save_pretrained(A__       ,A__       ,**A__)




							def 			A__				(    self       ,A__ = None       ,**A__):
												lowercase						     =						self.speaker_embeddings[voice_preset]

												lowercase						     =						{}
												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}].')

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

																	lowercase						     =						np.load(A__)

												return voice_preset_dict




							def 			A__				(    self       ,A__ = None):







												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       ,A__=None       ,A__=None       ,A__="pt"       ,A__=2_5_6       ,A__=False       ,A__=True       ,A__=False       ,**A__       ,):







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

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

																						lowercase						     =						np.load(A__)

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

												lowercase						     =						self.tokenizer(
												    A__       ,return_tensors=A__       ,padding='''max_length'''       ,max_length=A__       ,return_attention_mask=A__       ,return_token_type_ids=A__       ,add_special_tokens=A__       ,**A__       ,)

												if voice_preset is not None:
																	lowercase						     =						voice_preset

												return encoded_text






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
# and perform 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__		:Union[str, Any]			  =   16
lowercase__		:Optional[Any]			  =   32



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__ = 16		):
					'''simple docstring'''
					lowercase						     =						AutoTokenizer.from_pretrained('''bert-base-cased'''		)
					lowercase						     =						load_dataset('''glue'''			,      '''mrpc'''		)

					def tokenize_function(lowerCAmelCase__		):
										# max_length=None => use the model max length (it's actually the default)
										lowercase						     =						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():
										lowercase						     =						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
					lowercase						     =						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.
										lowercase						     =						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":
															lowercase						     =						16
										elif accelerator.mixed_precision != "no":
															lowercase						     =						8
										else:
															lowercase						     =						None

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

					# Instantiate dataloaders.
					lowercase						     =						DataLoader(
					    tokenized_datasets['''train''']			,      shuffle=lowerCAmelCase__			,      collate_fn=lowerCAmelCase__			,      batch_size=lowerCAmelCase__		)
					lowercase						     =						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

							lowercase__		:List[str]			  =   mocked_dataloaders  # noqa: F811



def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					# For testing only
					if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,      lowerCAmelCase__		) == "1":
										lowercase						     =						2
					# New Code #
					lowercase						     =						int(args.gradient_accumulation_steps		)
					# Initialize accelerator
					lowercase						     =						Accelerator(
					    cpu=args.cpu			,      mixed_precision=args.mixed_precision			,      gradient_accumulation_steps=lowerCAmelCase__		)
					if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
										raise NotImplementedError(
										    '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''		)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowercase						     =						config['''lr''']
					lowercase						     =						int(config['''num_epochs''']		)
					lowercase						     =						int(config['''seed''']		)
					lowercase						     =						int(config['''batch_size''']		)

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

					set_seed(lowerCAmelCase__		)
					lowercase     ,						lowercase						     =						get_dataloaders(lowerCAmelCase__			,      lowerCAmelCase__		)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowercase						     =						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).
					lowercase						     =						model.to(accelerator.device		)

					# Instantiate optimizer
					lowercase						     =						AdamW(params=model.parameters()			,      lr=lowerCAmelCase__		)

					# Instantiate scheduler
					lowercase						     =						get_linear_schedule_with_warmup(
					    optimizer=lowerCAmelCase__			,      num_warmup_steps=100			,      num_training_steps=(len(lowerCAmelCase__		) * 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.
					lowercase     ,						lowercase     ,						lowercase     ,						lowercase     ,						lowercase						     =						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		)
															# 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(lowerCAmelCase__		):
																				lowercase						     =						model(**lowerCAmelCase__		)
																				lowercase						     =						output.loss
																				accelerator.backward(lowerCAmelCase__		)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										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():
																				lowercase						     =						model(**lowerCAmelCase__		)
															lowercase						     =						outputs.logits.argmax(dim=-1		)
															lowercase     ,						lowercase						     =						accelerator.gather_for_metrics((predictions, batch['''labels'''])		)
															metric.add_batch(
															    predictions=lowerCAmelCase__			,      references=lowerCAmelCase__			,      )

										lowercase						     =						metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}:'			,      lowerCAmelCase__		)



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						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.'''			,      )
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps'''			,      type=lowerCAmelCase__			,      default=1			,      help='''The number of minibatches to be ran before gradients are accumulated.'''			,      )
					parser.add_argument('''--cpu'''			,      action='''store_true'''			,      help='''If passed, will train on the CPU.'''		)
					lowercase						     =						parser.parse_args()
					lowercase						     =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(lowerCAmelCase__			,      lowerCAmelCase__		)


if __name__ == "__main__":
							main()




