






'''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


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


a_					=	1_6
a_					=	3_2





def 	_a(					UpperCamelCase__			:  Accelerator,		UpperCamelCase__			:  int = 1_6							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=AutoTokenizer.from_pretrained('''bert-base-cased'''							)
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=load_dataset('''glue''',		'''mrpc'''							)

      def tokenize_function(UpperCamelCase__			:  str							):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__       :			Tuple													=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():
            SCREAMING_SNAKE_CASE__       :			Tuple													=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
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=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.
            SCREAMING_SNAKE_CASE__       :			int													=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":
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__       :			str													=8
            else:
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=None

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

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__       :			List[Any]													=DataLoader(
          tokenized_datasets['''train'''],		shuffle=UpperCamelCase__,		collate_fn=UpperCamelCase__,		batch_size=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			str													=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

  a_					=	mocked_dataloaders  # noqa: F811





def 	_a(					UpperCamelCase__			:  Optional[int],		UpperCamelCase__			:  Optional[Any]							):




      '''simple docstring'''






      if os.environ.get('''TESTING_MOCKED_DATALOADERS''',		UpperCamelCase__							) == "1":
            SCREAMING_SNAKE_CASE__       :			Dict													=2
      # New Code #
      SCREAMING_SNAKE_CASE__       :			str													=int(args.gradient_accumulation_steps							)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE__       :			int													=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
      SCREAMING_SNAKE_CASE__       :			str													=config['''lr''']
      SCREAMING_SNAKE_CASE__       :			str													=int(config['''num_epochs''']							)
      SCREAMING_SNAKE_CASE__       :			int													=int(config['''seed''']							)
      SCREAMING_SNAKE_CASE__       :			int													=int(config['''batch_size''']							)

      SCREAMING_SNAKE_CASE__       :			Optional[int]													=evaluate.load('''glue''',		'''mrpc'''							)

      set_seed(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Dict													=get_dataloaders(UpperCamelCase__,		UpperCamelCase__							)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__       :			Any													=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).
      SCREAMING_SNAKE_CASE__       :			Dict													=model.to(accelerator.device							)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=AdamW(params=model.parameters(),		lr=UpperCamelCase__							)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE__       :			Any													=get_linear_schedule_with_warmup(
          optimizer=UpperCamelCase__,		num_warmup_steps=1_0_0,		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.
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Optional[int]													=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__							):
                        SCREAMING_SNAKE_CASE__       :			Dict													=model(**UpperCamelCase__							)
                        SCREAMING_SNAKE_CASE__       :			Optional[int]													=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():
                        SCREAMING_SNAKE_CASE__       :			List[str]													=model(**UpperCamelCase__							)
                  SCREAMING_SNAKE_CASE__       :			Tuple													=outputs.logits.argmax(dim=-1							)
                  SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Tuple													=accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
                  metric.add_batch(
                      predictions=UpperCamelCase__,		references=UpperCamelCase__,		)

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:",		UpperCamelCase__							)





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=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.'''							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=parser.parse_args()
      SCREAMING_SNAKE_CASE__       :			str													={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(UpperCamelCase__,		UpperCamelCase__							)


if __name__ == "__main__":
  main()







'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							StableDiffusionSAGPipeline
 snake_case_												=							TEXT_TO_IMAGE_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_BATCH_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_												=							TEXT_TO_IMAGE_IMAGE_PARAMS
 snake_case_												=							False




 def        __magic_name__		( self		:							str )		->  Union[str, Any]:
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=UNetaDConditionModel(
           block_out_channels=(32, 64)   , layers_per_block=2   , sample_size=32   , in_channels=4   , out_channels=4   , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   , cross_attention_dim=32   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=DDIMScheduler(
           beta_start=0.00085   , beta_end=0.012   , beta_schedule='''scaled_linear'''   , clip_sample=__lowercase   , set_alpha_to_one=__lowercase   , )
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=AutoencoderKL(
           block_out_channels=[32, 64]   , in_channels=3   , out_channels=3   , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']   , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']   , latent_channels=4   , )
       torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=CLIPTextConfig(
           bos_token_id=0   , eos_token_id=2   , hidden_size=32   , intermediate_size=37   , layer_norm_eps=1e-05   , num_attention_heads=4   , num_hidden_layers=5   , pad_token_id=1   , vocab_size=10_00   , )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=CLIPTextModel(__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[str]													=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def        __magic_name__		( self		:							int   , __lowercase		:							Union[str, Any]   , __lowercase		:							Any=0 )		->  Optional[Any]:
       if str(__lowercase ).startswith('''mps''' ):
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.manual_seed(__lowercase )
       else:
             SCREAMING_SNAKE_CASE__       :			Optional[int]													=torch.Generator(device=__lowercase ).manual_seed(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													={
           '''prompt''': '''.''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 1.0,
           '''sag_scale''': 1.0,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        __magic_name__		( self		:							int )		->  str:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3 )






@slow
@require_torch_gpu
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							int )		->  Optional[int]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							int )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Tuple													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sag_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			int													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			str													=np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2




 def        __magic_name__		( self		:							List[Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Tuple													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[str]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sag_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													=output.images

       SCREAMING_SNAKE_CASE__       :			Tuple													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[Any]													=np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-2




 def        __magic_name__		( self		:							str )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=sag_pipe.to(__lowercase )
       sag_pipe.set_progress_bar_config(disable=__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''.'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sag_pipe(
           [prompt]   , width=7_68   , height=5_12   , generator=__lowercase   , guidance_scale=7.5   , sag_scale=1.0   , num_inference_steps=20   , output_type='''np'''   , )

       SCREAMING_SNAKE_CASE__       :			Any													=output.images

       assert image.shape == (1, 5_12, 7_68, 3)