


import argparse
import os

# New Code #
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.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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__  :Dict             =							1_6
lowerCAmelCase__  :str             =							3_2

def 	lowerCAmelCase__			(      a__:		Accelerator      ,  a__:		int = 1_6	)  ->		Any:


							'''simple docstring'''





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

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

							def collate_fn(a__:		Dict	):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_UpperCAmelCase					=      1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					_UpperCAmelCase					=      1_6
														elif accelerator.mixed_precision != "no":
																					_UpperCAmelCase					=      8
														else:
																					_UpperCAmelCase					=      None

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

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

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

def 	lowerCAmelCase__			(      a__:		Tuple      ,  a__:		Optional[Any]	)  ->		Union[str, Any]:


							'''simple docstring'''





							if os.environ.get('TESTING_MOCKED_DATALOADERS'      ,  a__	) == "1":
														_UpperCAmelCase					=      2
							# Initialize accelerator
							_UpperCAmelCase					=      Accelerator(cpu=args.cpu      ,  mixed_precision=args.mixed_precision	)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_UpperCAmelCase					=      config['lr']
							_UpperCAmelCase					=      int(config['num_epochs']	)
							_UpperCAmelCase					=      int(config['seed']	)
							_UpperCAmelCase					=      int(config['batch_size']	)

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

							# New Code #
							# We now can define an inner training loop function. It should take a batch size as the only parameter,
							# and build the dataloaders in there.
							# It also gets our decorator
							@find_executable_batch_size(starting_batch_size=a__	)
							def inner_training_loop(a__:		List[str]	):
														# And now just move everything below under this function
														# We need to bring in the Accelerator object from earlier
														nonlocal accelerator
														# And reset all of its attributes that could hold onto any memory:
														accelerator.free_memory()

														# Then we can declare the model, optimizer, and everything else:
														set_seed(a__	)

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

														# 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).
														_UpperCAmelCase					=      model.to(accelerator.device	)

														# Instantiate optimizer
														_UpperCAmelCase					=      AdamW(params=model.parameters()      ,  lr=a__	)
														_UpperCAmelCase		,	_UpperCAmelCase					=      get_dataloaders(a__      ,  a__	)

														# Instantiate scheduler
														_UpperCAmelCase					=      get_linear_schedule_with_warmup(
														    optimizer=a__      ,  num_warmup_steps=1_0_0      ,  num_training_steps=(len(a__	) * 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.
														_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      accelerator.prepare(
														    a__      ,  a__      ,  a__      ,  a__      ,  a__	)

														# Now we train the model
														for epoch in range(a__	):
																					model.train()
																					for step, batch in enumerate(a__	):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device	)
																												_UpperCAmelCase					=      model(**a__	)
																												_UpperCAmelCase					=      outputs.loss
																												accelerator.backward(a__	)
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

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

																					_UpperCAmelCase					=      metric.compute()
																					# Use accelerator.print to print only on the main process.
																					accelerator.print(F'''epoch {epoch}:'''      ,  a__	)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
							inner_training_loop()

def 	lowerCAmelCase__			(      )  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      argparse.ArgumentParser(description='Simple example of training script.'	)
							parser.add_argument(
							    '--mixed_precision'      ,  type=a__      ,  default=a__      ,  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.'	)
							_UpperCAmelCase					=      parser.parse_args()
							_UpperCAmelCase					=      {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
							training_function(a__      ,  a__	)


if __name__ == "__main__":
		main()




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available

@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  Union[List[np.ndarray], torch.FloatTensor]


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
		from .pipeline_text_to_video_synth import TextToVideoSDPipeline
		from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
		from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
