




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

_lowerCamelCase      :						Optional[int]							=  16
_lowerCamelCase      :						Optional[Any]							=  32







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = 16		)  ->			List[str]:



       """simple docstring"""

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

       def tokenize_function(lowercase_		):
              # max_length=None => use the model max length (it's actually the default)
              A__ 					=       tokenizer(examples['''sentence1''']					,							examples['''sentence2''']					,							truncation=lowercase_					,							max_length=lowercase_		)
              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__ 					=       datasets.map(
                  lowercase_					,							batched=lowercase_					,							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__ 					=       tokenized_datasets.rename_column('''label'''					,							'''labels'''		)

       def collate_fn(lowercase_		):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              A__ 					=       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__ 					=       16
              elif accelerator.mixed_precision != "no":
                     A__ 					=       8
              else:
                     A__ 					=       None

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

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

       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

  _lowerCamelCase      :						Any							=  mocked_dataloaders  # noqa: F811







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			Tuple:



       """simple docstring"""

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

       A__					,				A__ 					=       get_dataloaders(lowercase_					,							lowercase_		)
       A__ 					=       evaluate.load('''glue'''					,							'''mrpc'''		)

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

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       A__ 					=       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''					,							return_dict=lowercase_		)

       # 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__ 					=       model.to(accelerator.device		)

       # Instantiate optimizer
       A__ 					=       AdamW(params=model.parameters()					,							lr=lowercase_		)

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

       # New Code #
       # We need to initialize the trackers we use. Overall configurations can also be stored
       if args.with_tracking:
              A__ 					=       os.path.split(lowercase_		)[-1].split('''.'''		)[0]
              accelerator.init_trackers(lowercase_					,							lowercase_		)

       # Now we train the model
       for epoch in range(lowercase_		):
              model.train()
              # New Code #
              # For our tracking example, we will log the total loss of each epoch
              if args.with_tracking:
                     A__ 					=       0
              for step, batch in enumerate(lowercase_		):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device		)
                     A__ 					=       model(**lowercase_		)
                     A__ 					=       outputs.loss
                     # New Code #
                     if args.with_tracking:
                            total_loss += loss.detach().float()
                     A__ 					=       loss / gradient_accumulation_steps
                     accelerator.backward(lowercase_		)
                     if step % gradient_accumulation_steps == 0:
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

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

              A__ 					=       metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f"""epoch {epoch}:"""					,							lowercase_		)

              # 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(lowercase_		),
                             '''epoch''': epoch,
                         }					,							step=lowercase_					,							)

    # 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 SCREAMING_SNAKE_CASE							(			)  ->			List[str]:



       """simple docstring"""

       A__ 					=       argparse.ArgumentParser(description='''Simple example of training script.'''		)
       parser.add_argument(
           '''--mixed_precision'''					,							type=lowercase_					,							default=lowercase_					,							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=lowercase_					,							default='''logs'''					,							help='''Location on where to store experiment tracking logs` and relevent project information'''					,							)
       A__ 					=       parser.parse_args()
       A__ 					=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(lowercase_					,							lowercase_		)


if __name__ == "__main__":
  main()




"""simple docstring"""





import math
from typing import Any, Callable, List, Optional, Tuple, Union

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
		from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


UpperCamelCase_       :    Any						      =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase_       :    List[Any]						      =			256



class __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







			snake_case						   =					["melgan"]




			def __init__(					self    :       Dict							, _snake_case    :       SpectrogramNotesEncoder							, _snake_case    :       SpectrogramContEncoder							, _snake_case    :       TaFilmDecoder							, _snake_case    :       DDPMScheduler							, _snake_case    :       OnnxRuntimeModel if is_onnx_available() else Any							, )   ->			None:







								"""simple docstring"""
								super().__init__()

								# From MELGAN
								A_								=    math.log(1e-5						)  # Matches MelGAN training.
								A_								=    4.0  # Largest value for most examples
								A_								=    128

								self.register_modules(
								    notes_encoder=_snake_case							, continuous_encoder=_snake_case							, decoder=_snake_case							, scheduler=_snake_case							, melgan=_snake_case							, )




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Tuple							, _snake_case    :       str=(-1.0, 1.0)							, _snake_case    :       int=False						)   ->			str:







								"""simple docstring"""
								A_			,     A_								=    output_range
								if clip:
													A_								=    torch.clip(_snake_case							, self.min_value							, self.max_value						)
								# Scale to [0, 1].
								A_								=    (features - self.min_value) / (self.max_value - self.min_value)
								# Scale to [min_out, max_out].
								return zero_one * (max_out - min_out) + min_out




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple							, _snake_case    :       Optional[Any]=(-1.0, 1.0)							, _snake_case    :       List[str]=False						)   ->			List[str]:







								"""simple docstring"""
								A_			,     A_								=    input_range
								A_								=    torch.clip(_snake_case							, _snake_case							, _snake_case						) if clip else outputs
								# Scale to [0, 1].
								A_								=    (outputs - min_out) / (max_out - min_out)
								# Scale to [self.min_value, self.max_value].
								return zero_one * (self.max_value - self.min_value) + self.min_value




			def   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								A_								=    input_tokens > 0
								A_			,     A_								=    self.notes_encoder(
								    encoder_input_tokens=_snake_case							, encoder_inputs_mask=_snake_case						)

								A_			,     A_								=    self.continuous_encoder(
								    encoder_inputs=_snake_case							, encoder_inputs_mask=_snake_case						)

								return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       int							, _snake_case    :       Tuple						)   ->			Optional[int]:







								"""simple docstring"""
								A_								=    noise_time
								if not torch.is_tensor(_snake_case						):
													A_								=    torch.tensor([timesteps]							, dtype=torch.long							, device=input_tokens.device						)
								elif torch.is_tensor(_snake_case						) and len(timesteps.shape						) == 0:
													A_								=    timesteps[None].to(input_tokens.device						)

								# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
								A_								=    timesteps * torch.ones(input_tokens.shape[0]							, dtype=timesteps.dtype							, device=timesteps.device						)

								A_								=    self.decoder(
								    encodings_and_masks=_snake_case							, decoder_input_tokens=_snake_case							, decoder_noise_time=_snake_case						)
								return logits






			@torch.no_grad()
			def __call__(					self    :       List[Any]							, _snake_case    :       List[List[int]]							, _snake_case    :       Optional[torch.Generator] = None							, _snake_case    :       int = 100							, _snake_case    :       bool = True							, _snake_case    :       str = "numpy"							, _snake_case    :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None							, _snake_case    :       int = 1							, )   ->			Union[AudioPipelineOutput, Tuple]:







								"""simple docstring"""
								if (callback_steps is None) or (
								    callback_steps is not None and (not isinstance(_snake_case							, _snake_case						) or callback_steps <= 0)
								):
													raise ValueError(
													    F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
													    F' {type(_snake_case						)}.'						)

								A_								=    np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]							, dtype=np.floataa						)
								A_								=    np.zeros([1, 0, self.n_dims]							, np.floataa						)
								A_								=    torch.ones((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)

								for i, encoder_input_tokens in enumerate(_snake_case						):
													if i == 0:
																		A_								=    torch.from_numpy(pred_mel[:1].copy()						).to(
																		    device=self.device							, dtype=self.decoder.dtype						)
																		# The first chunk has no previous context.
																		A_								=    torch.zeros((1, TARGET_FEATURE_LENGTH)							, dtype=_snake_case							, device=self.device						)
													else:
																		# The full song pipeline does not feed in a context feature, so the mask
																		# will be all 0s after the feature converter. Because we know we're
																		# feeding in a full context chunk from the previous prediction, set it
																		# to all 1s.
																		A_								=    ones

													A_								=    self.scale_features(
													    _snake_case							, output_range=[-1.0, 1.0]							, clip=_snake_case						)

													A_								=    self.encode(
													    input_tokens=torch.IntTensor([encoder_input_tokens]						).to(device=self.device						)							, continuous_inputs=_snake_case							, continuous_mask=_snake_case							, )

													# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
													A_								=    randn_tensor(
													    shape=encoder_continuous_inputs.shape							, generator=_snake_case							, device=self.device							, dtype=self.decoder.dtype							, )

													# set step values
													self.scheduler.set_timesteps(_snake_case						)

													# Denoising diffusion loop
													for j, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																		A_								=    self.decode(
																		    encodings_and_masks=_snake_case							, input_tokens=_snake_case							, noise_time=t / self.scheduler.config.num_train_timesteps							, )

																		# Compute previous output: x_t -> x_t-1
																		A_								=    self.scheduler.step(_snake_case							, _snake_case							, _snake_case							, generator=_snake_case						).prev_sample

													A_								=    self.scale_to_features(_snake_case							, input_range=[-1.0, 1.0]						)
													A_								=    mel[:1]
													A_								=    mel.cpu().float().numpy()

													A_								=    np.concatenate([full_pred_mel, pred_mel[:1]]							, axis=1						)

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																		callback(_snake_case							, _snake_case						)

													logger.info("Generated segment"							, _snake_case						)

								if output_type == "numpy" and not is_onnx_available():
													raise ValueError(
													    "Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."						)
								elif output_type == "numpy" and self.melgan is None:
													raise ValueError(
													    "Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."						)

								if output_type == "numpy":
													A_								=    self.melgan(input_features=full_pred_mel.astype(np.floataa						)						)
								else:
													A_								=    full_pred_mel

								if not return_dict:
													return (output,)

								return AudioPipelineOutput(audios=_snake_case						)




