






'''simple docstring'''



import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Accelerator,					SCREAMING_SNAKE_CASE__:	int = 16,					SCREAMING_SNAKE_CASE__:	str = "bert-base-cased"    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    AutoTokenizer.from_pretrained(__snake_case    )
   __a				     =    load_dataset('glue',					'mrpc'    )

   def tokenize_function(SCREAMING_SNAKE_CASE__:	Tuple    ):
      # max_length=None => use the model max length (it's actually the default)
      __a				     =    tokenizer(examples['sentence1'],					examples['sentence2'],					truncation=__snake_case,					max_length=__snake_case    )
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   __a				     =    datasets.map(
       __snake_case,					batched=__snake_case,					remove_columns=['idx', 'sentence1', 'sentence2'],					load_from_cache_file=__snake_case    )

   # 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__:	Dict    ):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      if accelerator.distributed_type == DistributedType.TPU:
         return tokenizer.pad(__snake_case,					padding='max_length',					max_length=128,					return_tensors='pt'    )
      return tokenizer.pad(__snake_case,					padding='longest',					return_tensors='pt'    )

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

   return train_dataloader, eval_dataloader



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

   """simple docstring"""
   model.eval()
   __a				     =    0
   for step, batch in enumerate(__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(**__snake_case    )
      __a				     =    outputs.logits.argmax(dim=-1    )
      # It is slightly faster to call this once, than multiple times
      __a				     =    accelerator.gather(
          (predictions, batch['labels'])    )  # If we are in a multiprocess environment, the last batch has duplicates
      if accelerator.use_distributed:
         if step == len(__snake_case    ) - 1:
            __a				     =    predictions[: len(eval_dataloader.dataset    ) - samples_seen]
            __a				     =    references[: len(eval_dataloader.dataset    ) - samples_seen]
         else:
            samples_seen += references.shape[0]
      metric.add_batch(
          predictions=__snake_case,					references=__snake_case,					)

   __a				     =    metric.compute()
   return eval_metric["accuracy"]



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

   """simple docstring"""
   __a				     =    Accelerator()

   # 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				     =    args.model_name_or_path

   set_seed(__snake_case    )
   __a				     =    get_dataloaders(__snake_case,					__snake_case,					__snake_case    )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   __a				     =    AutoModelForSequenceClassification.from_pretrained(__snake_case,					return_dict=__snake_case    )

   # Instantiate optimizer
   __a				     =    (
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   __a				     =    optimizer_cls(params=model.parameters(),					lr=__snake_case    )

   if accelerator.state.deepspeed_plugin is not None:
      __a				     =    accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      __a				     =    1
   __a				     =    (len(__snake_case    ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      __a				     =    get_linear_schedule_with_warmup(
          optimizer=__snake_case,					num_warmup_steps=0,					num_training_steps=__snake_case,					)
   else:
      __a				     =    DummyScheduler(__snake_case,					total_num_steps=__snake_case,					warmup_num_steps=0    )

   # 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				     =    accelerator.prepare(
       __snake_case,					__snake_case,					__snake_case,					__snake_case,					__snake_case    )

   # We need to keep track of how many total steps we have iterated over
   __a				     =    0
   # We also need to keep track of the stating epoch so files are named properly
   __a				     =    0
   __a				     =    evaluate.load('glue',					'mrpc'    )
   __a				     =    num_epochs

   if args.partial_train_epoch is not None:
      __a				     =    args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint    )
      __a				     =    args.resume_from_checkpoint.split('epoch_'    )[1]
      __a				     =    ""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      __a				     =    int(__snake_case    ) + 1
      __a				     =    evaluation_loop(__snake_case,					__snake_case,					__snake_case,					__snake_case    )
      accelerator.print('resumed checkpoint performance:',					__snake_case    )
      accelerator.print('resumed checkpoint\'s scheduler\'s lr:',					lr_scheduler.get_lr()[0]    )
      accelerator.print('resumed optimizers\'s lr:',					optimizer.param_groups[0]['lr']    )
      with open(os.path.join(args.output_dir,					f"""state_{starting_epoch-1}.json"""    ),					'r'    ) as f:
         __a				     =    json.load(__snake_case    )
         assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
         assert (
             resumed_state["lr"] == lr_scheduler.get_lr()[0]
         ), "Scheduler learning rate mismatch, loading from checkpoint failed"
         assert (
             resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
         ), "Optimizer learning rate mismatch, loading from checkpoint failed"
         assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
         return

    # Now we train the model
   __a				     =    {}
   for epoch in range(__snake_case,					__snake_case    ):
      model.train()
      for step, batch in enumerate(__snake_case    ):
         __a				     =    model(**__snake_case    )
         __a				     =    outputs.loss
         __a				     =    loss / gradient_accumulation_steps
         accelerator.backward(__snake_case    )
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      __a				     =    f"""epoch_{epoch}"""
      __a				     =    os.path.join(args.output_dir,					__snake_case    )
      accelerator.save_state(__snake_case    )
      __a				     =    evaluation_loop(__snake_case,					__snake_case,					__snake_case,					__snake_case    )
      __a				     =    accuracy
      __a				     =    lr_scheduler.get_lr()[0]
      __a				     =    optimizer.param_groups[0]["lr"]
      __a				     =    epoch
      __a				     =    overall_step
      accelerator.print(f"""epoch {epoch}:""",					__snake_case    )

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         with open(os.path.join(args.output_dir,					f"""state_{epoch}.json"""    ),					'w'    ) as f:
            json.dump(__snake_case,					__snake_case    )



def    __UpperCAmelCase	(				)  ->			Any:

   """simple docstring"""
   __a				     =    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'    )
   parser.add_argument(
       '--model_name_or_path',					type=__snake_case,					default='bert-base-cased',					help='Path to pretrained model or model identifier from huggingface.co/models.',					required=__snake_case,					)
   parser.add_argument(
       '--output_dir',					type=__snake_case,					default='.',					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',					)
   parser.add_argument(
       '--resume_from_checkpoint',					type=__snake_case,					default=__snake_case,					help='If the training should continue from a checkpoint folder.',					)
   parser.add_argument(
       '--partial_train_epoch',					type=__snake_case,					default=__snake_case,					help='If passed, the training will stop after this number of epochs.',					)
   parser.add_argument(
       '--num_epochs',					type=__snake_case,					default=2,					help='Number of train epochs.',					)
   __a				     =    parser.parse_args()
   __a				     =    {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

   training_function(__snake_case,					__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()