
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic 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)
#
# 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    			=					32


def 		_lowerCamelCase       (   _a			,   _a = 1_6  ):
  """simple docstring"""




  _lowerCamelCase      =						AutoTokenizer.from_pretrained('''bert-base-cased'''  )
  _lowerCamelCase      =						load_dataset('''glue'''			,   '''mrpc'''  )

  def tokenize_function(_a  ):
    # max_length=None => use the model max length (it's actually the default)
    _lowerCamelCase      =						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():
    _lowerCamelCase      =						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
  _lowerCamelCase      =						tokenized_datasets.rename_column('''label'''			,   '''labels'''  )

  def collate_fn(_a  ):
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowerCamelCase      =						1_6
    elif accelerator.mixed_precision != "no":
      _lowerCamelCase      =						8
    else:
      _lowerCamelCase      =						None

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

  # Instantiate dataloaders.
  _lowerCamelCase      =						DataLoader(
      tokenized_datasets['''train''']			,   shuffle=__lowerCamelCase			,   collate_fn=__lowerCamelCase			,   batch_size=__lowerCamelCase  )
  _lowerCamelCase      =						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       (   _a			,   _a  ):
  """simple docstring"""




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

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

  # New Code #
  # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
  # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
  # half each time. From this, we can calculate the number of gradient accumulation steps needed
  # and modify the Accelerator object as a result
  @find_executable_batch_size(starting_batch_size=int(__lowerCamelCase  )  )
  def inner_training_loop(_a  ):
    # Since we need to modify the outside accelerator object, we need to bring it
    # to the local scope
    nonlocal accelerator

    # We can calculate the number of gradient accumulation steps based on the current
    # batch size vs the starting batch size
    _lowerCamelCase      =						observed_batch_size // batch_size

    # And then set it in the Accelerator directly:
    _lowerCamelCase      =						num_gradient_accumulation_steps

    # Next we need to free all of the stored model references in the Accelerator each time
    accelerator.free_memory()

    # And set the seed so our results are reproducable each reset
    set_seed(__lowerCamelCase  )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowerCamelCase      =						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).
    _lowerCamelCase      =						model.to(accelerator.device  )

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

    # Instantiate scheduler
    _lowerCamelCase      =						get_linear_schedule_with_warmup(
        optimizer=__lowerCamelCase			,   num_warmup_steps=1_0_0			,   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.
    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase      =						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  ):
        # And perform gradient accumulation
        with accelerator.accumulate(__lowerCamelCase  ):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device  )
          _lowerCamelCase      =						model(**__lowerCamelCase  )
          _lowerCamelCase      =						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():
          _lowerCamelCase      =						model(**__lowerCamelCase  )
        _lowerCamelCase      =						outputs.logits.argmax(dim=-1  )
        _lowerCamelCase    ,    _lowerCamelCase      =						accelerator.gather_for_metrics((predictions, batch['''labels'''])  )
        metric.add_batch(
            predictions=__lowerCamelCase			,   references=__lowerCamelCase			,   )

      _lowerCamelCase      =						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       (   ):
  """simple docstring"""




  _lowerCamelCase      =						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.'''  )
  _lowerCamelCase      =						parser.parse_args()
  # New Code #
  # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
  _lowerCamelCase      =						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 2_5_6}
  training_function(__lowerCamelCase			,   __lowerCamelCase  )


if __name__ == "__main__":
  main()





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =KandinskyPipeline
   lowerCamelCase_            =[
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   lowerCamelCase_            =['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   lowerCamelCase_            =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase_            =False


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Union[str, Any]:
         return 32


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return 32


   @property
   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Any:
         return self.time_input_dim


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return self.time_input_dim * 4


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return 100


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       List[str]:
         lowercase_     			=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
         return tokenizer


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         torch.manual_seed(0)
         lowercase_     			=   MCLIPConfig(
             numDims=self.cross_attention_dim ,		transformerDimensions=self.text_embedder_hidden_size ,		hidden_size=self.text_embedder_hidden_size ,		intermediate_size=37 ,		num_attention_heads=4 ,		num_hidden_layers=5 ,		vocab_size=1005 ,		)

         lowercase_     			=   MultilingualCLIP(__lowerCAmelCase)
         lowercase_     			=   text_encoder.eval()

         return text_encoder


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       Any:
         torch.manual_seed(0)

         lowercase_     			=   {
             "in_channels": 4,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 8,
             "addition_embed_type": "text_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": "text_image_proj",
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": None,
         }

         lowercase_     			=   UNetaDConditionModel(**__lowerCAmelCase)
         return model


   @property
   def __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         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 __UpperCAmelCase							( self					:						Optional[Any])   ->       Tuple:
         torch.manual_seed(0)
         lowercase_     			=   VQModel(**self.dummy_movq_kwargs)
         return model


   def __UpperCAmelCase							( self					:						Tuple)   ->       Union[str, Any]:
         lowercase_     			=   self.dummy_text_encoder
         lowercase_     			=   self.dummy_tokenizer
         lowercase_     			=   self.dummy_unet
         lowercase_     			=   self.dummy_movq

         lowercase_     			=   DDIMScheduler(
             num_train_timesteps=1000 ,		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 ,		)

         lowercase_     			=   {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						List[str]=0)   ->       List[Any]:
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(seed + 1)).to(__lowerCAmelCase)
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     			=   torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     			=   torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
         lowercase_     			=   {
             "prompt": "horse",
             "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 __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         lowercase_     			=   "cpu"

         lowercase_     			=   self.get_dummy_components()

         lowercase_     			=   self.pipeline_class(**__lowerCAmelCase)
         lowercase_     			=   pipe.to(__lowerCAmelCase)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase))
         lowercase_     			=   output.images

         lowercase_     			=   pipe(
             **self.get_dummy_inputs(__lowerCAmelCase) ,		return_dict=__lowerCAmelCase ,		)[0]

         lowercase_     			=   image[0, -3:, -3:, -1]
         lowercase_     			=   image_from_tuple[0, -3:, -3:, -1]

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

         lowercase_     			=   np.array(
             [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953])

         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 				lowercase    (    unittest.TestCase			):


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


   def __UpperCAmelCase							( self					:						List[str])   ->       Tuple:
         lowercase_     			=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy")

         lowercase_     			=   KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior" ,		torch_dtype=torch.floataa)
         pipe_prior.to(__lowerCAmelCase)

         lowercase_     			=   KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1" ,		torch_dtype=torch.floataa)
         lowercase_     			=   pipeline.to(__lowerCAmelCase)
         pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   "red cat, 4k photo"

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_			,				lowercase_     			=   pipe_prior(
             __lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=5 ,		negative_prompt="" ,		).to_tuple()

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_     			=   pipeline(
             __lowerCAmelCase ,		image_embeds=__lowerCAmelCase ,		negative_image_embeds=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=100 ,		output_type="np" ,		)

         lowercase_     			=   output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(__lowerCAmelCase ,		__lowerCAmelCase)






