






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


UpperCamelCase    							=      16
UpperCamelCase    							=      32


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Accelerator					,							__lowerCAmelCase							:					int = 16							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					AutoTokenizer.from_pretrained("""bert-base-cased"""							)
   __UpperCamelCase	:			Optional[int]					=					load_dataset("""glue"""					,							"""mrpc"""							)

   def tokenize_function(__lowerCAmelCase							:					Dict							):
      # max_length=None => use the model max length (it's actually the default)
      __UpperCamelCase	:			Tuple					=					tokenizer(examples["""sentence1"""]					,							examples["""sentence2"""]					,							truncation=__lowerCAmelCase					,							max_length=__lowerCAmelCase							)
      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():
      __UpperCamelCase	:			Optional[Any]					=					datasets.map(
          __lowerCAmelCase					,							batched=__lowerCAmelCase					,							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
   __UpperCamelCase	:			int					=					tokenized_datasets.rename_column("""label"""					,							"""labels"""							)

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

      return tokenizer.pad(
          __lowerCAmelCase					,							padding="""longest"""					,							max_length=__lowerCAmelCase					,							pad_to_multiple_of=__lowerCAmelCase					,							return_tensors="""pt"""					,							)

   # Instantiate dataloaders.
   __UpperCamelCase	:			Dict					=					DataLoader(
       tokenized_datasets["""train"""]					,							shuffle=__lowerCAmelCase					,							collate_fn=__lowerCAmelCase					,							batch_size=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DataLoader(
       tokenized_datasets["""validation"""]					,							shuffle=__lowerCAmelCase					,							collate_fn=__lowerCAmelCase					,							batch_size=__lowerCAmelCase							)

   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    							=      mocked_dataloaders  # noqa: F811


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Union[str, Any]					,							__lowerCAmelCase							:					Optional[int]							)     ->  Optional[int]:
   # For testing only
   if os.environ.get("""TESTING_MOCKED_DATALOADERS"""					,							__lowerCAmelCase							) == "1":
      __UpperCamelCase	:			Tuple					=					2
   # Initialize accelerator
   __UpperCamelCase	:			Union[str, Any]					=					Accelerator(cpu=args.cpu					,							mixed_precision=args.mixed_precision							)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   __UpperCamelCase	:			List[Any]					=					config["""lr"""]
   __UpperCamelCase	:			Any					=					int(config["""num_epochs"""]							)
   __UpperCamelCase	:			Union[str, Any]					=					int(config["""seed"""]							)
   __UpperCamelCase	:			Optional[Any]					=					int(config["""batch_size"""]							)

   __UpperCamelCase	:			str					=					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=__lowerCAmelCase							)
   def inner_training_loop(__lowerCAmelCase							:					Tuple							):
      # 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(__lowerCAmelCase							)

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      __UpperCamelCase	:			List[str]					=					AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""					,							return_dict=__lowerCAmelCase							)

      # 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).
      __UpperCamelCase	:			Union[str, Any]					=					model.to(accelerator.device							)

      # Instantiate optimizer
      __UpperCamelCase	:			List[str]					=					AdamW(params=model.parameters()					,							lr=__lowerCAmelCase							)
      __UpperCamelCase	,     __UpperCamelCase	:			Optional[int]					=					get_dataloaders(__lowerCAmelCase					,							__lowerCAmelCase							)

      # Instantiate scheduler
      __UpperCamelCase	:			Optional[int]					=					get_linear_schedule_with_warmup(
          optimizer=__lowerCAmelCase					,							num_warmup_steps=100					,							num_training_steps=(len(__lowerCAmelCase							) * 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.
      __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			List[str]					=					accelerator.prepare(
          __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      # Now we train the model
      for epoch in range(__lowerCAmelCase							):
         model.train()
         for step, batch in enumerate(__lowerCAmelCase							):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device							)
            __UpperCamelCase	:			List[str]					=					model(**__lowerCAmelCase							)
            __UpperCamelCase	:			List[Any]					=					outputs.loss
            accelerator.backward(__lowerCAmelCase							)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

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

         __UpperCamelCase	:			Tuple					=					metric.compute()
         # Use accelerator.print to print only on the main process.
         accelerator.print(f'epoch {epoch}:'					,							__lowerCAmelCase							)

    # 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 				__lowerCamelCase     (							)     ->  List[str]:
   __UpperCamelCase	:			Any					=					argparse.ArgumentParser(description="""Simple example of training script."""							)
   parser.add_argument(
       """--mixed_precision"""					,							type=__lowerCAmelCase					,							default=__lowerCAmelCase					,							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."""							)
   __UpperCamelCase	:			Optional[Any]					=					parser.parse_args()
   __UpperCamelCase	:			List[Any]					=					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
   training_function(__lowerCAmelCase					,							__lowerCAmelCase							)


if __name__ == "__main__":
       main()











import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 		_A						( UpperCAmelCase_	,				unittest.TestCase ):
  lowercase_						:			int						=					KandinskyVaaPipeline
  lowercase_						:			Union[str, Any]						=					[
      '''image_embeds''',
      '''negative_image_embeds''',
  ]
  lowercase_						:			int						=					['''image_embeds''', '''negative_image_embeds''']
  lowercase_						:			str						=					[
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  lowercase_						:			str						=					False

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     return 32

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     return self.time_input_dim * 4

  @property
  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return 1_00

  @property
  def    a					(    self						:	str			):
     """simple docstring"""

     torch.manual_seed(0			)

     __UpperCamelCase	:			int					=					{
         """in_channels""": 4,
         # Out channels is double in channels because predicts mean and variance
         """out_channels""": 8,
         """addition_embed_type""": """image""",
         """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
         """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
         """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
         """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
         """layers_per_block""": 1,
         """encoder_hid_dim""": self.text_embedder_hidden_size,
         """encoder_hid_dim_type""": """image_proj""",
         """cross_attention_dim""": self.cross_attention_dim,
         """attention_head_dim""": 4,
         """resnet_time_scale_shift""": """scale_shift""",
         """class_embed_type""": None,
     }

     __UpperCamelCase	:			Any					=					UNetaDConditionModel(**lowerCamelCase__			)
     return model

  @property
  def    a					(    self						:	Union[str, Any]			):
     """simple docstring"""

     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
         "in_channels": 3,
         "latent_channels": 4,
         "layers_per_block": 1,
         "norm_num_groups": 8,
         "norm_type": "spatial",
         "num_vq_embeddings": 12,
         "out_channels": 3,
         "up_block_types": [
             "AttnUpDecoderBlock2D",
             "UpDecoderBlock2D",
         ],
         "vq_embed_dim": 4,
     }

  @property
  def    a					(    self						:	int			):
     """simple docstring"""

     torch.manual_seed(0			)
     __UpperCamelCase	:			Tuple					=					VQModel(**self.dummy_movq_kwargs			)
     return model

  def    a					(    self						:	Tuple			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					self.dummy_unet
     __UpperCamelCase	:			Optional[int]					=					self.dummy_movq

     __UpperCamelCase	:			List[str]					=					DDIMScheduler(
         num_train_timesteps=10_00   ,				beta_schedule="""linear"""   ,				beta_start=0.0_0085   ,				beta_end=0.012   ,				clip_sample=lowerCamelCase__   ,				set_alpha_to_one=lowerCamelCase__   ,				steps_offset=1   ,				prediction_type="""epsilon"""   ,				thresholding=lowerCamelCase__   ,				)

     __UpperCamelCase	:			str					=					{
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }
     return components

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	Optional[Any]   ,				lowerCamelCase__						:	Any=0			):
     """simple docstring"""

     __UpperCamelCase	:			Tuple					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(lowerCamelCase__			)			).to(lowerCamelCase__			)
     __UpperCamelCase	:			Optional[int]					=					floats_tensor((1, self.text_embedder_hidden_size)   ,				rng=random.Random(seed + 1			)			).to(
         lowerCamelCase__			)
     if str(lowerCamelCase__			).startswith("""mps"""			):
        __UpperCamelCase	:			Optional[int]					=					torch.manual_seed(lowerCamelCase__			)
     else:
        __UpperCamelCase	:			int					=					torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
     __UpperCamelCase	:			Dict					=					{
         """image_embeds""": image_embeds,
         """negative_image_embeds""": negative_image_embeds,
         """generator""": generator,
         """height""": 64,
         """width""": 64,
         """guidance_scale""": 4.0,
         """num_inference_steps""": 2,
         """output_type""": """np""",
     }
     return inputs

  def    a					(    self						:	Dict			):
     """simple docstring"""

     __UpperCamelCase	:			Dict					=					"""cpu"""

     __UpperCamelCase	:			Dict					=					self.get_dummy_components()

     __UpperCamelCase	:			Union[str, Any]					=					self.pipeline_class(**lowerCamelCase__			)
     __UpperCamelCase	:			int					=					pipe.to(lowerCamelCase__			)

     pipe.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			List[Any]					=					pipe(**self.get_dummy_inputs(lowerCamelCase__			)			)
     __UpperCamelCase	:			List[Any]					=					output.images

     __UpperCamelCase	:			int					=					pipe(
         **self.get_dummy_inputs(lowerCamelCase__			)   ,				return_dict=lowerCamelCase__   ,				)[0]

     __UpperCamelCase	:			List[str]					=					image[0, -3:, -3:, -1]
     __UpperCamelCase	:			Tuple					=					image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     __UpperCamelCase	:			int					=					np.array(
         [0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046]			)

     assert (
         np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
     ), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'

     assert (
         np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
     ), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







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

  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()

  def    a					(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			int					=					load_numpy(
         """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
         """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""			)

     __UpperCamelCase	:			List[str]					=					KandinskyVaaPriorPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-prior"""   ,				torch_dtype=torch.floataa			)
     pipe_prior.to(lowerCamelCase__			)

     __UpperCamelCase	:			int					=					KandinskyVaaPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-2-decoder"""   ,				torch_dtype=torch.floataa			)
     __UpperCamelCase	:			Optional[int]					=					pipeline.to(lowerCamelCase__			)
     pipeline.set_progress_bar_config(disable=lowerCamelCase__			)

     __UpperCamelCase	:			Optional[Any]					=					"""red cat, 4k photo"""

     __UpperCamelCase	:			Tuple					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					pipe_prior(
         lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=5   ,				negative_prompt=""""""   ,				).to_tuple()

     __UpperCamelCase	:			Any					=					torch.Generator(device="""cuda"""			).manual_seed(0			)
     __UpperCamelCase	:			Union[str, Any]					=					pipeline(
         image_embeds=lowerCamelCase__   ,				negative_image_embeds=lowerCamelCase__   ,				generator=lowerCamelCase__   ,				num_inference_steps=1_00   ,				output_type="""np"""   ,				)

     __UpperCamelCase	:			Any					=					output.images[0]

     assert image.shape == (5_12, 5_12, 3)

     assert_mean_pixel_difference(lowerCamelCase__   ,				lowerCamelCase__			)



