






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


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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Accelerator      ,    UpperCamelCase_    :   int = 16						)					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  AutoTokenizer.from_pretrained('bert-base-cased'						)
						lowerCAmelCase__        =  load_dataset('glue'      ,    'mrpc'						)

						def tokenize_function(UpperCamelCase_    :   List[str]						):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__        =  tokenizer(examples['sentence1']      ,    examples['sentence2']      ,    truncation=UpperCamelCase_      ,    max_length=UpperCamelCase_						)
												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(
												    UpperCamelCase_      ,    batched=UpperCamelCase_      ,    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(UpperCamelCase_    :   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(
												    UpperCamelCase_      ,    padding='longest'      ,    max_length=UpperCamelCase_      ,    pad_to_multiple_of=UpperCamelCase_      ,    return_tensors='pt'      ,    )

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

						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

					__snake_case      :      Optional[Any]             =       mocked_dataloaders  # noqa: F811







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str						)					->				List[Any]:

						"""simple docstring"""



						if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,    UpperCamelCase_						) == "1":
												lowerCAmelCase__        =  2
						# New Code #
						lowerCAmelCase__        =  int(args.gradient_accumulation_steps						)
						# Initialize accelerator
						lowerCAmelCase__        =  Accelerator(
						    cpu=args.cpu      ,    mixed_precision=args.mixed_precision      ,    gradient_accumulation_steps=UpperCamelCase_						)
						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
						lowerCAmelCase__        =  config['lr']
						lowerCAmelCase__        =  int(config['num_epochs']						)
						lowerCAmelCase__        =  int(config['seed']						)
						lowerCAmelCase__        =  int(config['batch_size']						)

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

						set_seed(UpperCamelCase_						)
						lowerCAmelCase__  ,    lowerCAmelCase__        =  get_dataloaders(UpperCamelCase_      ,    UpperCamelCase_						)
						# 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=UpperCamelCase_						)

						# 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=UpperCamelCase_						)

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

						# Now we train the model
						for epoch in range(UpperCamelCase_						):
												model.train()
												for step, batch in enumerate(UpperCamelCase_						):
																		# 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(UpperCamelCase_						):
																								lowerCAmelCase__        =  model(**UpperCamelCase_						)
																								lowerCAmelCase__        =  output.loss
																								accelerator.backward(UpperCamelCase_						)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

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

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







def 		_UpperCamelCase				(      )					->				int:

						"""simple docstring"""



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


if __name__ == "__main__":
					main()













# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
					from datasets import load_dataset


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    Any						=   (
						    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
						    '''text to read (in English) and returns a waveform object containing the sound.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												if self.post_processor is None:
																		lowerCAmelCase__        =  'microsoft/speecht5_hifigan'
												super().setup()






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.pre_processor(text=_UpperCamelCase       ,  return_tensors='pt'       ,  truncation=_UpperCamelCase				)

												if speaker_embeddings is None:
																		if not is_datasets_available():
																								raise ImportError('Datasets needs to be installed if not passing speaker embeddings.'				)

																		lowerCAmelCase__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

												return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





