






'''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
#
########################################################################


__UpperCamelCase      :       str      			=      16
__UpperCamelCase      :       List[Any]      			=      32



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Accelerator,					SCREAMING_SNAKE_CASE__:	int = 16    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    AutoTokenizer.from_pretrained('bert-base-cased'    )
   __a				     =    load_dataset('glue',					'mrpc'    )

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

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

   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

 __UpperCamelCase      :       Any      			=      mocked_dataloaders  # noqa: F811



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			List[str]:

   """simple docstring"""
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS',					SCREAMING_SNAKE_CASE__    ) == "1":
      __a				     =    2
   # New Code #
   __a				     =    int(args.gradient_accumulation_steps    )
   # Initialize accelerator
   __a				     =    Accelerator(
       cpu=args.cpu,					mixed_precision=args.mixed_precision,					gradient_accumulation_steps=SCREAMING_SNAKE_CASE__    )
   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
   __a				     =    config['lr']
   __a				     =    int(config['num_epochs']    )
   __a				     =    int(config['seed']    )
   __a				     =    int(config['batch_size']    )

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

   set_seed(SCREAMING_SNAKE_CASE__    )
   __a     ,			__a				     =    get_dataloaders(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
   # 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=SCREAMING_SNAKE_CASE__    )

   # 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=SCREAMING_SNAKE_CASE__    )

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

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

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

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



def    __UpperCAmelCase	(				)  ->			Dict:

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


if __name__ == "__main__":
 main()







'''simple docstring'''



import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      _lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=IFPipeline
      __a  					=TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
      __a  					=TEXT_TO_IMAGE_BATCH_PARAMS
      __a  					=PipelineTesterMixin.required_optional_params - {"latents"}




      def  __UpperCamelCase							(  self      )							->int:



         '''simple docstring'''
         return self._get_dummy_components()




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=0      )							->Tuple:



         '''simple docstring'''
         if str(lowerCamelCase      ).startswith('mps'      ):
            __a				     =    torch.manual_seed(lowerCamelCase      )
         else:
            __a				     =    torch.Generator(device=lowerCamelCase      ).manual_seed(lowerCamelCase      )

         __a				     =    {
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         self._test_save_load_optional_components()




      @unittest.skipIf(torch_device != 'cuda' ,   reason='float16 requires CUDA'      )
      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2      )




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         self._test_save_load_local()




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2 ,   )






      @unittest.skipIf(
          torch_device != 'cuda' or not is_xformers_available() ,   reason='XFormers attention is only available with CUDA and `xformers` installed' ,   )
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3      )







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




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def  __UpperCamelCase							(  self      )							->Any:



         '''simple docstring'''
         # if

         __a				     =    IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa      )

         __a				     =    IFSuperResolutionPipeline.from_pretrained(
             'DeepFloyd/IF-II-L-v1.0' ,   variant='fp16' ,   torch_dtype=torch.floataa ,   text_encoder=lowerCamelCase ,   tokenizer=lowerCamelCase      )

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('cuda'      )

         __a     ,			__a				     =    pipe_a.encode_prompt('anime turtle' ,   device='cuda'      )

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         __a				     =    None
         __a				     =    None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         __a				     =    IFImgaImgPipeline(**pipe_a.components      )
         __a				     =    IFImgaImgSuperResolutionPipeline(**pipe_a.components      )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if_imgaimg(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         __a				     =    IFInpaintingPipeline(**pipe_a.components      )
         __a				     =    IFInpaintingSuperResolutionPipeline(**pipe_a.components      )

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()      )

         self._test_if_inpainting(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->List[Any]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (64, 64, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (64, 64, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[int]:



         '''simple docstring'''
         # pipeline 1

         _start_torch_memory_measurement()

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )
         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   num_inference_steps=2 ,   generator=lowerCamelCase ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (64, 64, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )

         # pipeline 2

         _start_torch_memory_measurement()

         __a				     =    torch.Generator(device='cpu'      ).manual_seed(0      )

         __a				     =    floats_tensor((1, 3, 64, 64) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(0      )      ).to(lowerCamelCase      )
         __a				     =    floats_tensor((1, 3, 256, 256) ,   rng=random.Random(1      )      ).to(lowerCamelCase      )

         __a				     =    pipe_a(
             prompt_embeds=lowerCamelCase ,   negative_prompt_embeds=lowerCamelCase ,   image=lowerCamelCase ,   mask_image=lowerCamelCase ,   original_image=lowerCamelCase ,   generator=lowerCamelCase ,   num_inference_steps=2 ,   output_type='np' ,   )

         __a				     =    output.images[0]

         assert image.shape == (256, 256, 3)

         __a				     =    torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         __a				     =    load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'      )
         assert_mean_pixel_difference(lowerCamelCase ,   lowerCamelCase      )







def    __UpperCAmelCase	(				)  ->			Dict:

   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()