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 how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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
#
########################################################################


__lowerCamelCase      :			List[str]							       =						16
__lowerCamelCase      :			int							       =						32



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	List[str]			,					__UpperCamelCase   :	Union[str, Any] = 16     )							-> Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""bert-base-cased"""     )
  SCREAMING_SNAKE_CASE__      							=				load_dataset("""glue"""			,					"""mrpc"""     )

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

  def collate_fn(__UpperCamelCase   :	Dict     ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    SCREAMING_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":
      SCREAMING_SNAKE_CASE__      							=				16
    elif accelerator.mixed_precision != "no":
      SCREAMING_SNAKE_CASE__      							=				8
    else:
      SCREAMING_SNAKE_CASE__      							=				None

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

  # Instantiate dataloaders.
  SCREAMING_SNAKE_CASE__      							=				DataLoader(
      tokenized_datasets["""train"""]			,					shuffle=__UpperCamelCase			,					collate_fn=__UpperCamelCase			,					batch_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				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

   __lowerCamelCase      :			str							       =						mocked_dataloaders  # noqa: F811



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str     )							-> List[Any]:







  """simple docstring"""





  if os.environ.get("""TESTING_MOCKED_DATALOADERS"""			,					__UpperCamelCase     ) == "1":
    SCREAMING_SNAKE_CASE__      							=				2
  # Initialize accelerator
  SCREAMING_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
  SCREAMING_SNAKE_CASE__      							=				config["""lr"""]
  SCREAMING_SNAKE_CASE__      							=				int(config["""num_epochs"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(config["""seed"""]     )
  SCREAMING_SNAKE_CASE__      							=				int(config["""batch_size"""]     )

  SCREAMING_SNAKE_CASE__      							=				evaluate.load("""glue"""			,					"""mrpc"""     )

  # If the batch size is too big we use gradient accumulation
  SCREAMING_SNAKE_CASE__      							=				1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    SCREAMING_SNAKE_CASE__      							=				batch_size // MAX_GPU_BATCH_SIZE
    SCREAMING_SNAKE_CASE__      							=				MAX_GPU_BATCH_SIZE

  set_seed(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				get_dataloaders(__UpperCamelCase			,					__UpperCamelCase     )
  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  SCREAMING_SNAKE_CASE__      							=				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).
  SCREAMING_SNAKE_CASE__      							=				model.to(accelerator.device     )

  # Instantiate optimizer
  SCREAMING_SNAKE_CASE__      							=				AdamW(params=model.parameters()			,					lr=__UpperCamelCase     )

  # Instantiate scheduler
  SCREAMING_SNAKE_CASE__      							=				get_linear_schedule_with_warmup(
      optimizer=__UpperCamelCase			,					num_warmup_steps=1_00			,					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.
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				accelerator.prepare(
      __UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase     )

  # Now we train the model
  for epoch in range(__UpperCamelCase     ):
    model.train()
    for step, batch in enumerate(__UpperCamelCase     ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device     )
      SCREAMING_SNAKE_CASE__      							=				model(**__UpperCamelCase     )
      SCREAMING_SNAKE_CASE__      							=				outputs.loss
      SCREAMING_SNAKE_CASE__      							=				loss / gradient_accumulation_steps
      accelerator.backward(__UpperCamelCase     )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    SCREAMING_SNAKE_CASE__      							=				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     )
      with torch.no_grad():
        SCREAMING_SNAKE_CASE__      							=				model(**__UpperCamelCase     )
      SCREAMING_SNAKE_CASE__      							=				outputs.logits.argmax(dim=-1     )
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				accelerator.gather((predictions, batch["""labels"""])     )
      # New Code #
      # First we check if it's a distributed system
      if accelerator.use_distributed:
        # Then see if we're on the last batch of our eval dataloader
        if step == len(__UpperCamelCase     ) - 1:
          # Last batch needs to be truncated on distributed systems as it contains additional samples
          SCREAMING_SNAKE_CASE__      							=				predictions[: len(eval_dataloader.dataset     ) - samples_seen]
          SCREAMING_SNAKE_CASE__      							=				references[: len(eval_dataloader.dataset     ) - samples_seen]
        else:
          # Otherwise we add the number of samples seen
          samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
      metric.add_batch(
          predictions=__UpperCamelCase			,					references=__UpperCamelCase			,					)

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



def      __SCREAMING_SNAKE_CASE  ( )							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				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."""     )
  SCREAMING_SNAKE_CASE__      							=				parser.parse_args()
  SCREAMING_SNAKE_CASE__      							=				{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(__UpperCamelCase			,					__UpperCamelCase     )


if __name__ == "__main__":
   main()

import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Optional[int]			,					__UpperCamelCase   :	str     )							-> Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				checkpoint

  SCREAMING_SNAKE_CASE__      							=				{}

  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""encoder.conv_in.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""encoder.conv_in.bias"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""encoder.conv_out.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""encoder.conv_out.bias"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""encoder.norm_out.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""encoder.norm_out.bias"""]

  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""decoder.conv_in.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""decoder.conv_in.bias"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""decoder.conv_out.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""decoder.conv_out.bias"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""decoder.norm_out.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""decoder.norm_out.bias"""]

  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""quant_conv.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""quant_conv.bias"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""post_quant_conv.weight"""]
  SCREAMING_SNAKE_CASE__      							=				vae_state_dict["""post_quant_conv.bias"""]

  # Retrieves the keys for the encoder down blocks only
  SCREAMING_SNAKE_CASE__      							=				len({""".""".join(layer.split("""."""     )[:3]     ) for layer in vae_state_dict if """encoder.down""" in layer}     )
  SCREAMING_SNAKE_CASE__      							=				{
      layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__UpperCamelCase     )
  }

  # Retrieves the keys for the decoder up blocks only
  SCREAMING_SNAKE_CASE__      							=				len({""".""".join(layer.split("""."""     )[:3]     ) for layer in vae_state_dict if """decoder.up""" in layer}     )
  SCREAMING_SNAKE_CASE__      							=				{
      layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(__UpperCamelCase     )
  }

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				[key for key in down_blocks[i] if f"""down.{i}""" in key and f"""down.{i}.downsample""" not in key]

    if f"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.weight"""     )
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict.pop(
          f"""encoder.down.{i}.downsample.conv.bias"""     )

    SCREAMING_SNAKE_CASE__      							=				renew_vae_resnet_paths(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				{"""old""": f"""down.{i}.block""", """new""": f"""down_blocks.{i}.resnets"""}
    assign_to_checkpoint(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					additional_replacements=[meta_path]			,					config=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				[key for key in vae_state_dict if """encoder.mid.block""" in key]
  SCREAMING_SNAKE_CASE__      							=				2
  for i in range(1			,					num_mid_res_blocks + 1     ):
    SCREAMING_SNAKE_CASE__      							=				[key for key in mid_resnets if f"""encoder.mid.block_{i}""" in key]

    SCREAMING_SNAKE_CASE__      							=				renew_vae_resnet_paths(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				{"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
    assign_to_checkpoint(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					additional_replacements=[meta_path]			,					config=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				[key for key in vae_state_dict if """encoder.mid.attn""" in key]
  SCREAMING_SNAKE_CASE__      							=				renew_vae_attention_paths(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					additional_replacements=[meta_path]			,					config=__UpperCamelCase     )
  conv_attn_to_linear(__UpperCamelCase     )

  for i in range(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__      							=				num_up_blocks - 1 - i
    SCREAMING_SNAKE_CASE__      							=				[
        key for key in up_blocks[block_id] if f"""up.{block_id}""" in key and f"""up.{block_id}.upsample""" not in key
    ]

    if f"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.weight"""
      ]
      SCREAMING_SNAKE_CASE__      							=				vae_state_dict[
          f"""decoder.up.{block_id}.upsample.conv.bias"""
      ]

    SCREAMING_SNAKE_CASE__      							=				renew_vae_resnet_paths(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				{"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
    assign_to_checkpoint(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					additional_replacements=[meta_path]			,					config=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				[key for key in vae_state_dict if """decoder.mid.block""" in key]
  SCREAMING_SNAKE_CASE__      							=				2
  for i in range(1			,					num_mid_res_blocks + 1     ):
    SCREAMING_SNAKE_CASE__      							=				[key for key in mid_resnets if f"""decoder.mid.block_{i}""" in key]

    SCREAMING_SNAKE_CASE__      							=				renew_vae_resnet_paths(__UpperCamelCase     )
    SCREAMING_SNAKE_CASE__      							=				{"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
    assign_to_checkpoint(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					additional_replacements=[meta_path]			,					config=__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				[key for key in vae_state_dict if """decoder.mid.attn""" in key]
  SCREAMING_SNAKE_CASE__      							=				renew_vae_attention_paths(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				{"""old""": """mid.attn_1""", """new""": """mid_block.attentions.0"""}
  assign_to_checkpoint(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase			,					additional_replacements=[meta_path]			,					config=__UpperCamelCase     )
  conv_attn_to_linear(__UpperCamelCase     )
  return new_checkpoint



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	str			,					__UpperCamelCase   :	str			,					)							-> Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				requests.get(
      """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""     )
  SCREAMING_SNAKE_CASE__      							=				io.BytesIO(r.content     )

  SCREAMING_SNAKE_CASE__      							=				OmegaConf.load(__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				5_12
  SCREAMING_SNAKE_CASE__      							=				"""cuda""" if torch.cuda.is_available() else """cpu"""
  if checkpoint_path.endswith("""safetensors"""     ):
    from safetensors import safe_open

    SCREAMING_SNAKE_CASE__      							=				{}
    with safe_open(__UpperCamelCase			,					framework="""pt"""			,					device="""cpu"""     ) as f:
      for key in f.keys():
        SCREAMING_SNAKE_CASE__      							=				f.get_tensor(__UpperCamelCase     )
  else:
    SCREAMING_SNAKE_CASE__      							=				torch.load(__UpperCamelCase			,					map_location=__UpperCamelCase     )["""state_dict"""]

  # Convert the VAE model.
  SCREAMING_SNAKE_CASE__      							=				create_vae_diffusers_config(__UpperCamelCase			,					image_size=__UpperCamelCase     )
  SCREAMING_SNAKE_CASE__      							=				custom_convert_ldm_vae_checkpoint(__UpperCamelCase			,					__UpperCamelCase     )

  SCREAMING_SNAKE_CASE__      							=				AutoencoderKL(**__UpperCamelCase     )
  vae.load_state_dict(__UpperCamelCase     )
  vae.save_pretrained(__UpperCamelCase     )


if __name__ == "__main__":
   __lowerCamelCase      :			List[Any]							       =		argparse.ArgumentParser()

   parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
   parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

   __lowerCamelCase      :			Optional[int]							       =		parser.parse_args()

   vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
