"""simple docstring"""



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


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





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = 16):
     __snake_case				=							AutoTokenizer.from_pretrained('''bert-base-cased''')
     __snake_case				=							load_dataset('''glue''',      '''mrpc''')

     def tokenize_function(snake_case):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case				=							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
     # starting with the main process first:
     with accelerator.main_process_first():
          __snake_case				=							datasets.map(
              __snake_case,      batched=__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
     __snake_case				=							tokenized_datasets.rename_column('''label''',      '''labels''')

     def collate_fn(snake_case):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          __snake_case				=							1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               __snake_case				=							16
          elif accelerator.mixed_precision != "no":
               __snake_case				=							8
          else:
               __snake_case				=							None

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

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

       __lowercase		:		Tuple														=							mocked_dataloaders  # noqa: F811





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

     __snake_case				=							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=__snake_case)
     def inner_training_loop(snake_case):
          # 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(__snake_case)

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

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

          # Instantiate scheduler
          __snake_case				=							get_linear_schedule_with_warmup(
              optimizer=__snake_case,      num_warmup_steps=1_00,      num_training_steps=(len(__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.
          __snake_case		,	__snake_case		,	__snake_case		,	__snake_case		,	__snake_case				=							accelerator.prepare(
              __snake_case,      __snake_case,      __snake_case,      __snake_case,      __snake_case)

          # Now we train the model
          for epoch in range(__snake_case):
               model.train()
               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)
                    __snake_case				=							model(**__snake_case)
                    __snake_case				=							outputs.loss
                    accelerator.backward(__snake_case)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               model.eval()
               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():
                         __snake_case				=							model(**__snake_case)
                    __snake_case				=							outputs.logits.argmax(dim=-1)
                    __snake_case		,	__snake_case				=							accelerator.gather_for_metrics((predictions, batch['''labels''']))
                    metric.add_batch(
                        predictions=__snake_case,      references=__snake_case,      )

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

    # 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    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							argparse.ArgumentParser(description='''Simple example of training script.''')
     parser.add_argument(
         '''--mixed_precision''',      type=__snake_case,      default=__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.''',      )
     parser.add_argument('''--cpu''',      action='''store_true''',      help='''If passed, will train on the CPU.''')
     __snake_case				=							parser.parse_args()
     __snake_case				=							{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(__snake_case,      __snake_case)


if __name__ == "__main__":
       main()



"""simple docstring"""



import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



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




    """simple docstring"""




    def  lowercase					(		self							:     Tuple    )    ->       str:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def  lowercase					(		self							:     int    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
    def  lowercase					(		self							:     Optional[Any]    )    ->       Tuple:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_euler'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe([prompt]	,      generator=A_	,      guidance_scale=9.0	,      num_inference_steps=20	,      output_type='''np'''    )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-1







    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         __snake_case				=							StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''    )
         __snake_case				=							sd_pipe.to(A_    )
         sd_pipe.set_progress_bar_config(disable=A_    )

         sd_pipe.set_scheduler('''sample_dpmpp_2m'''    )

         __snake_case				=							'''A painting of a squirrel eating a burger'''
         __snake_case				=							torch.manual_seed(0    )
         __snake_case				=							sd_pipe(
             [prompt]	,      generator=A_	,      guidance_scale=7.5	,      num_inference_steps=15	,      output_type='''np'''	,      use_karras_sigmas=A_	,      )

         __snake_case				=							output.images

         __snake_case				=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __snake_case				=							np.array(
             [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]    )

         assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2