'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 			UpperCAmelCase  (						a__    ,						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    StableDiffusionInpaintPipeline
       SCREAMING_SNAKE_CASE							=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
       SCREAMING_SNAKE_CASE							=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       SCREAMING_SNAKE_CASE							=    frozenset(
           []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       SCREAMING_SNAKE_CASE							=    frozenset([]					)

       def _lowerCAmelCase(						self )  ->  Tuple:
        torch.manual_seed(0 )
        lowercase__				:    Tuple  =					UNetaDConditionModel(
            block_out_channels=(32, 64)	,   layers_per_block=2	,   sample_size=32	,   in_channels=9	,   out_channels=4	,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,   cross_attention_dim=32	,   attention_head_dim=(2, 4)	,   use_linear_projection=__lowerCAmelCase	,   )
        lowercase__				:    Dict  =					PNDMScheduler(skip_prk_steps=__lowerCAmelCase )
        torch.manual_seed(0 )
        lowercase__				:    Dict  =					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	,   sample_size=128	,   )
        torch.manual_seed(0 )
        lowercase__				:    str  =					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=1000	,   hidden_act='''gelu'''	,   projection_dim=512	,   )
        lowercase__				:    str  =					CLIPTextModel(__lowerCAmelCase )
        lowercase__				:    Dict  =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

        lowercase__				:    Union[str, Any]  =					{
            '''unet''': unet,
            '''scheduler''': scheduler,
            '''vae''': vae,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''safety_checker''': None,
            '''feature_extractor''': None,
        }
        return components

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0 )  ->  Any:
        # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
        lowercase__				:    Optional[Any]  =					floats_tensor((1, 3, 32, 32)	,   rng=random.Random(__lowerCAmelCase ) ).to(__lowerCAmelCase )
        lowercase__				:    List[str]  =					image.cpu().permute(0	,   2	,   3	,   1 )[0]
        lowercase__				:    Any  =					Image.fromarray(np.uinta(__lowerCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
        lowercase__				:    List[Any]  =					Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((64, 64) )
        if str(__lowerCAmelCase ).startswith('''mps''' ):
         lowercase__				:    List[str]  =					torch.manual_seed(__lowerCAmelCase )
        else:
         lowercase__				:    Any  =					torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
        lowercase__				:    List[str]  =					{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': init_image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''guidance_scale''': 6.0,
            '''output_type''': '''numpy''',
        }
        return inputs

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    Any  =					'''cpu'''  # ensure determinism for the device-dependent torch.Generator
        lowercase__				:    Any  =					self.get_dummy_components()
        lowercase__				:    Union[str, Any]  =					StableDiffusionInpaintPipeline(**__lowerCAmelCase )
        lowercase__				:    str  =					sd_pipe.to(__lowerCAmelCase )
        sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )

        lowercase__				:    str  =					self.get_dummy_inputs(__lowerCAmelCase )
        lowercase__				:    Dict  =					sd_pipe(**__lowerCAmelCase ).images
        lowercase__				:    List[str]  =					image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        lowercase__				:    Optional[Any]  =					np.array([0.4_7_2_7, 0.5_7_3_5, 0.3_9_4_1, 0.5_4_4_6, 0.5_9_2_6, 0.4_3_9_4, 0.5_0_6_2, 0.4_6_5_4, 0.4_4_7_6] )

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


       def _lowerCAmelCase(						self )  ->  str:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



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





       '''simple docstring'''



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

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[Any]  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png''' )
        lowercase__				:    Dict  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
        lowercase__				:    Any  =					load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench.npy''' )

        lowercase__				:    Dict  =					'''stabilityai/stable-diffusion-2-inpainting'''
        lowercase__				:    Union[str, Any]  =					StableDiffusionInpaintPipeline.from_pretrained(__lowerCAmelCase	,   safety_checker=__lowerCAmelCase )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )
        pipe.enable_attention_slicing()

        lowercase__				:    Optional[Any]  =					'''Face of a yellow cat, high resolution, sitting on a park bench'''

        lowercase__				:    int  =					torch.manual_seed(0 )
        lowercase__				:    Union[str, Any]  =					pipe(
            prompt=__lowerCAmelCase	,   image=__lowerCAmelCase	,   mask_image=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   output_type='''np'''	,   )
        lowercase__				:    Tuple  =					output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image ).max() < 9E-3

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    List[Any]  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png''' )
        lowercase__				:    Optional[int]  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
        lowercase__				:    Any  =					load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
            '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

        lowercase__				:    Union[str, Any]  =					'''stabilityai/stable-diffusion-2-inpainting'''
        lowercase__				:    Tuple  =					StableDiffusionInpaintPipeline.from_pretrained(
            __lowerCAmelCase	,   torch_dtype=torch.floataa	,   safety_checker=__lowerCAmelCase	,   )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )
        pipe.enable_attention_slicing()

        lowercase__				:    Any  =					'''Face of a yellow cat, high resolution, sitting on a park bench'''

        lowercase__				:    Tuple  =					torch.manual_seed(0 )
        lowercase__				:    Tuple  =					pipe(
            prompt=__lowerCAmelCase	,   image=__lowerCAmelCase	,   mask_image=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   output_type='''np'''	,   )
        lowercase__				:    str  =					output.images[0]

        assert image.shape == (512, 512, 3)
        assert np.abs(expected_image - image ).max() < 5E-1


       def _lowerCAmelCase(						self )  ->  Dict:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        lowercase__				:    int  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-inpaint/init_image.png''' )
        lowercase__				:    str  =					load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

        lowercase__				:    Any  =					'''stabilityai/stable-diffusion-2-inpainting'''
        lowercase__				:    Optional[Any]  =					PNDMScheduler.from_pretrained(__lowerCAmelCase	,   subfolder='''scheduler''' )
        lowercase__				:    Optional[int]  =					StableDiffusionInpaintPipeline.from_pretrained(
            __lowerCAmelCase	,   safety_checker=__lowerCAmelCase	,   scheduler=__lowerCAmelCase	,   torch_dtype=torch.floataa	,   )
        pipe.to(__lowerCAmelCase )
        pipe.set_progress_bar_config(disable=__lowerCAmelCase )
        pipe.enable_attention_slicing(1 )
        pipe.enable_sequential_cpu_offload()

        lowercase__				:    List[Any]  =					'''Face of a yellow cat, high resolution, sitting on a park bench'''

        lowercase__				:    Union[str, Any]  =					torch.manual_seed(0 )
        lowercase__				:    List[Any]  =					pipe(
            prompt=__lowerCAmelCase	,   image=__lowerCAmelCase	,   mask_image=__lowerCAmelCase	,   generator=__lowerCAmelCase	,   num_inference_steps=2	,   output_type='''np'''	,   )

        lowercase__				:    Optional[Any]  =					torch.cuda.max_memory_allocated()
        # make sure that less than 2.65 GB is allocated
        assert mem_bytes < 2.6_5 * 10**9


'''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,
# specifically showcasing the experiment tracking capability,
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

__a: Optional[Any]          =   16
__a: Any          =   32
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase = 16     ):
 lowercase__				:    Dict  =					AutoTokenizer.from_pretrained('''bert-base-cased'''     )
 lowercase__				:    Optional[int]  =					load_dataset('''glue'''		,     '''mrpc'''     )

 def tokenize_function(UpperCAmelCase     ):
  # max_length=None => use the model max length (it's actually the default)
  lowercase__				:    Optional[Any]  =					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():
  lowercase__				:    List[Any]  =					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
 lowercase__				:    str  =					tokenized_datasets.rename_column('''label'''		,     '''labels'''     )

 def collate_fn(UpperCAmelCase     ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  lowercase__				:    List[str]  =					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":
   lowercase__				:    List[str]  =					16
  elif accelerator.mixed_precision != "no":
   lowercase__				:    Dict  =					8
  else:
   lowercase__				:    Optional[int]  =					None

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

 # Instantiate dataloaders.
 lowercase__				:    str  =					DataLoader(
     tokenized_datasets['''train''']		,     shuffle=UpperCAmelCase		,     collate_fn=UpperCAmelCase		,     batch_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					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: Tuple          =   mocked_dataloaders  # noqa: F811
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''		,     UpperCAmelCase     ) == "1":
  lowercase__				:    Optional[int]  =					2
 # Initialize Accelerator

 # New Code #
 # We pass in "all" to `log_with` to grab all available trackers in the environment
 # Note: If using a custom `Tracker` class, should be passed in here such as:
 # >>> log_with = ["all", MyCustomTrackerClassInstance()]
 if args.with_tracking:
  lowercase__				:    Union[str, Any]  =					Accelerator(
      cpu=args.cpu		,     mixed_precision=args.mixed_precision		,     log_with='''all'''		,     project_dir=args.project_dir     )
 else:
  lowercase__				:    Any  =					Accelerator(cpu=args.cpu		,     mixed_precision=args.mixed_precision     )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 lowercase__				:    int  =					config['''lr''']
 lowercase__				:    Optional[int]  =					int(config['''num_epochs''']     )
 lowercase__				:    Optional[Any]  =					int(config['''seed''']     )
 lowercase__				:    int  =					int(config['''batch_size''']     )
 set_seed(UpperCAmelCase     )

 lowercase__ ,							lowercase__				:    str  =					get_dataloaders(UpperCAmelCase		,     UpperCAmelCase     )
 lowercase__				:    Union[str, Any]  =					evaluate.load('''glue'''		,     '''mrpc'''     )

 # If the batch size is too big we use gradient accumulation
 lowercase__				:    Any  =					1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  lowercase__				:    Union[str, Any]  =					batch_size // MAX_GPU_BATCH_SIZE
  lowercase__				:    Any  =					MAX_GPU_BATCH_SIZE

 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 lowercase__				:    Optional[int]  =					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).
 lowercase__				:    List[str]  =					model.to(accelerator.device     )

 # Instantiate optimizer
 lowercase__				:    List[Any]  =					AdamW(params=model.parameters()		,     lr=UpperCAmelCase     )

 # Instantiate scheduler
 lowercase__				:    List[str]  =					get_linear_schedule_with_warmup(
     optimizer=UpperCAmelCase		,     num_warmup_steps=100		,     num_training_steps=(len(UpperCAmelCase     ) * num_epochs) // gradient_accumulation_steps		,     )

 # 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.
 lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[str]  =					accelerator.prepare(
     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )

 # New Code #
 # We need to initialize the trackers we use. Overall configurations can also be stored
 if args.with_tracking:
  lowercase__				:    Optional[Any]  =					os.path.split(UpperCAmelCase     )[-1].split('''.'''     )[0]
  accelerator.init_trackers(UpperCAmelCase		,     UpperCAmelCase     )

 # Now we train the model
 for epoch in range(UpperCAmelCase     ):
  model.train()
  # New Code #
  # For our tracking example, we will log the total loss of each epoch
  if args.with_tracking:
   lowercase__				:    str  =					0
  for step, batch in enumerate(UpperCAmelCase     ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device     )
   lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    List[str]  =					outputs.loss
   # New Code #
   if args.with_tracking:
    total_loss += loss.detach().float()
   lowercase__				:    List[str]  =					loss / gradient_accumulation_steps
   accelerator.backward(UpperCAmelCase     )
   if step % gradient_accumulation_steps == 0:
    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` (the default).
   batch.to(accelerator.device     )
   with torch.no_grad():
    lowercase__				:    List[str]  =					model(**UpperCAmelCase     )
   lowercase__				:    Optional[int]  =					outputs.logits.argmax(dim=-1     )
   lowercase__ ,							lowercase__				:    Optional[int]  =					accelerator.gather_for_metrics((predictions, batch['''labels'''])     )
   metric.add_batch(
       predictions=UpperCAmelCase		,     references=UpperCAmelCase		,     )

  lowercase__				:    int  =					metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F"""epoch {epoch}:"""		,     UpperCAmelCase     )

  # New Code #
  # To actually log, we call `Accelerator.log`
  # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
  if args.with_tracking:
   accelerator.log(
       {
           '''accuracy''': eval_metric['''accuracy'''],
           '''f1''': eval_metric['''f1'''],
           '''train_loss''': total_loss.item() / len(UpperCAmelCase     ),
           '''epoch''': epoch,
       }		,     step=UpperCAmelCase		,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
 if args.with_tracking:
  accelerator.end_training()
def 			__UpperCamelCase       (   ):
 lowercase__				:    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.'''		,     )
 parser.add_argument('''--cpu'''		,     action='''store_true'''		,     help='''If passed, will train on the CPU.'''     )
 parser.add_argument(
     '''--with_tracking'''		,     action='''store_true'''		,     help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''		,     )
 parser.add_argument(
     '''--project_dir'''		,     type=UpperCAmelCase		,     default='''logs'''		,     help='''Location on where to store experiment tracking logs` and relevent project information'''		,     )
 lowercase__				:    str  =					parser.parse_args()
 lowercase__				:    Tuple  =					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
 training_function(UpperCAmelCase		,     UpperCAmelCase     )


if __name__ == "__main__":
  main()

