



'''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 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      						=	16
_lowerCAmelCase      						=	32
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase = 16			):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[Any]							      =					AutoTokenizer.from_pretrained("""bert-base-cased"""			)
    lowerCAmelCase__ :  Dict							      =					load_dataset("""glue"""							,    """mrpc"""			)

    def tokenize_function(UpperCamelCase			):
        # max_length=None => use the model max length (it's actually the default)
        lowerCAmelCase__ :  Union[str, 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():
        lowerCAmelCase__ :  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
    lowerCAmelCase__ :  List[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.
        lowerCAmelCase__ :  Dict							      =					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":
            lowerCAmelCase__ :  Dict							      =					16
        elif accelerator.mixed_precision != "no":
            lowerCAmelCase__ :  Optional[Any]							      =					8
        else:
            lowerCAmelCase__ :  Optional[Any]							      =					None

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

    # Instantiate dataloaders.
    lowerCAmelCase__ :  List[str]							      =					DataLoader(
        tokenized_datasets["""train"""]							,    shuffle=UpperCamelCase							,    collate_fn=UpperCamelCase							,    batch_size=UpperCamelCase			)
    lowerCAmelCase__ :  Union[str, Any]							      =					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      						=	mocked_dataloaders  # noqa: F811
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





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

    lowerCAmelCase__ :  List[Any]							      =					evaluate.load("""glue"""							,    """mrpc"""			)

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

    set_seed(UpperCamelCase			)
    lowerCAmelCase__      ,   lowerCAmelCase__ :  List[Any]							      =					get_dataloaders(UpperCamelCase							,    UpperCamelCase			)
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    lowerCAmelCase__ :  Tuple							      =					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).
    lowerCAmelCase__ :  Optional[int]							      =					model.to(accelerator.device			)

    # Instantiate optimizer
    lowerCAmelCase__ :  List[str]							      =					AdamW(params=model.parameters()							,    lr=UpperCamelCase			)

    # Instantiate scheduler
    lowerCAmelCase__ :  Tuple							      =					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.
    lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					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			)
            lowerCAmelCase__ :  List[str]							      =					model(**UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					outputs.loss
            lowerCAmelCase__ :  Union[str, Any]							      =					loss / gradient_accumulation_steps
            accelerator.backward(UpperCamelCase			)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        lowerCAmelCase__ :  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			)
            with torch.no_grad():
                lowerCAmelCase__ :  List[str]							      =					model(**UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					outputs.logits.argmax(dim=-1			)
            lowerCAmelCase__      ,   lowerCAmelCase__ :  Union[str, Any]							      =					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
                    lowerCAmelCase__ :  int							      =					predictions[: len(eval_dataloader.dataset			) - samples_seen]
                    lowerCAmelCase__ :  int							      =					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							,    )

        lowerCAmelCase__ :  Any							      =					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f"""epoch {epoch}:"""							,    UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Optional[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."""			)
    lowerCAmelCase__ :  Optional[int]							      =					parser.parse_args()
    lowerCAmelCase__ :  Tuple							      =					{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
    training_function(UpperCamelCase							,    UpperCamelCase			)


if __name__ == "__main__":
 main()







'''simple docstring'''





import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=1			):



    """simple docstring"""





    if n_shave_prefix_segments >= 0:
        return ".".join(path.split("""."""			)[n_shave_prefix_segments:]			)
    else:
        return ".".join(path.split("""."""			)[:n_shave_prefix_segments]			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  Optional[Any]							      =					old_item.replace("""in_layers.0"""							,    """norm1"""			)
        lowerCAmelCase__ :  Optional[int]							      =					new_item.replace("""in_layers.2"""							,    """conv1"""			)

        lowerCAmelCase__ :  Dict							      =					new_item.replace("""out_layers.0"""							,    """norm2"""			)
        lowerCAmelCase__ :  str							      =					new_item.replace("""out_layers.3"""							,    """conv2"""			)

        lowerCAmelCase__ :  str							      =					new_item.replace("""emb_layers.1"""							,    """time_emb_proj"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""skip_connection"""							,    """conv_shortcut"""			)

        lowerCAmelCase__ :  Union[str, Any]							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

        mapping.append({"""old""": old_item, """new""": new_item}			)

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0			):



    """simple docstring"""





    lowerCAmelCase__ :  int							      =					[]
    for old_item in old_list:
        lowerCAmelCase__ :  List[str]							      =					old_item

        lowerCAmelCase__ :  int							      =					new_item.replace("""norm.weight"""							,    """group_norm.weight"""			)
        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""norm.bias"""							,    """group_norm.bias"""			)

        lowerCAmelCase__ :  Optional[Any]							      =					new_item.replace("""proj_out.weight"""							,    """proj_attn.weight"""			)
        lowerCAmelCase__ :  int							      =					new_item.replace("""proj_out.bias"""							,    """proj_attn.bias"""			)

        lowerCAmelCase__ :  str							      =					shave_segments(UpperCamelCase							,    n_shave_prefix_segments=UpperCamelCase			)

        mapping.append({"""old""": old_item, """new""": new_item}			)

    return mapping
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=None							,    UpperCamelCase=None							,    UpperCamelCase=None			):



    """simple docstring"""





    assert isinstance(UpperCamelCase							,    UpperCamelCase			), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            lowerCAmelCase__ :  Any							      =					old_checkpoint[path]
            lowerCAmelCase__ :  int							      =					old_tensor.shape[0] // 3

            lowerCAmelCase__ :  int							      =					(-1, channels) if len(old_tensor.shape			) == 3 else (-1)

            lowerCAmelCase__ :  Tuple							      =					old_tensor.shape[0] // config["""num_head_channels"""] // 3

            lowerCAmelCase__ :  List[Any]							      =					old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]			)
            lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					old_tensor.split(channels // num_heads							,    dim=1			)

            lowerCAmelCase__ :  int							      =					query.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Union[str, Any]							      =					key.reshape(UpperCamelCase			)
            lowerCAmelCase__ :  Optional[int]							      =					value.reshape(UpperCamelCase			)

    for path in paths:
        lowerCAmelCase__ :  Any							      =					path["""new"""]

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.0"""							,    """mid_block.resnets.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.1"""							,    """mid_block.attentions.0"""			)
        lowerCAmelCase__ :  Any							      =					new_path.replace("""middle_block.2"""							,    """mid_block.resnets.1"""			)

        if additional_replacements is not None:
            for replacement in additional_replacements:
                lowerCAmelCase__ :  Any							      =					new_path.replace(replacement["""old"""]							,    replacement["""new"""]			)

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            lowerCAmelCase__ :  List[Any]							      =					old_checkpoint[path["""old"""]][:, :, 0]
        else:
            lowerCAmelCase__ :  Dict							      =					old_checkpoint[path["""old"""]]
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					{}

    lowerCAmelCase__ :  str							      =					checkpoint["""time_embed.0.weight"""]
    lowerCAmelCase__ :  List[Any]							      =					checkpoint["""time_embed.0.bias"""]
    lowerCAmelCase__ :  int							      =					checkpoint["""time_embed.2.weight"""]
    lowerCAmelCase__ :  List[str]							      =					checkpoint["""time_embed.2.bias"""]

    lowerCAmelCase__ :  str							      =					checkpoint["""input_blocks.0.0.weight"""]
    lowerCAmelCase__ :  Any							      =					checkpoint["""input_blocks.0.0.bias"""]

    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.weight"""]
    lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint["""out.0.bias"""]
    lowerCAmelCase__ :  str							      =					checkpoint["""out.2.weight"""]
    lowerCAmelCase__ :  Tuple							      =					checkpoint["""out.2.bias"""]

    # Retrieves the keys for the input blocks only
    lowerCAmelCase__ :  Optional[Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """input_blocks""" in layer}			)
    lowerCAmelCase__ :  Optional[Any]							      =					{
        layer_id: [key for key in checkpoint if f"""input_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    # Retrieves the keys for the middle blocks only
    lowerCAmelCase__ :  Union[str, Any]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """middle_block""" in layer}			)
    lowerCAmelCase__ :  Union[str, Any]							      =					{
        layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    # Retrieves the keys for the output blocks only
    lowerCAmelCase__ :  List[str]							      =					len({""".""".join(layer.split("""."""			)[:2]			) for layer in checkpoint if """output_blocks""" in layer}			)
    lowerCAmelCase__ :  List[Any]							      =					{
        layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
        for layer_id in range(UpperCamelCase			)
    }

    for i in range(1							,    UpperCamelCase			):
        lowerCAmelCase__ :  Dict							      =					(i - 1) // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  Tuple							      =					(i - 1) % (config["""num_res_blocks"""] + 1)

        lowerCAmelCase__ :  Optional[int]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key]
        lowerCAmelCase__ :  Optional[Any]							      =					[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]

        if f"""input_blocks.{i}.0.op.weight""" in checkpoint:
            lowerCAmelCase__ :  Optional[int]							      =					checkpoint[
                f"""input_blocks.{i}.0.op.weight"""
            ]
            lowerCAmelCase__ :  Tuple							      =					checkpoint[
                f"""input_blocks.{i}.0.op.bias"""
            ]
            continue

        lowerCAmelCase__ :  Optional[Any]							      =					renew_resnet_paths(UpperCamelCase			)
        lowerCAmelCase__ :  Dict							      =					{"""old""": f"""input_blocks.{i}.0""", """new""": f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
        lowerCAmelCase__ :  Optional[Any]							      =					{"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
        assign_to_checkpoint(
            UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path, resnet_op]							,    config=UpperCamelCase			)

        if len(UpperCamelCase			):
            lowerCAmelCase__ :  Optional[Any]							      =					renew_attention_paths(UpperCamelCase			)
            lowerCAmelCase__ :  Tuple							      =					{
                """old""": f"""input_blocks.{i}.1""",
                """new""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
            }
            lowerCAmelCase__ :  List[str]							      =					{
                f"""input_blocks.{i}.1.qkv.bias""": {
                    """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                    """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                    """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                },
                f"""input_blocks.{i}.1.qkv.weight""": {
                    """key""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                    """query""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                    """value""": f"""down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                },
            }
            assign_to_checkpoint(
                UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase							,    )

    lowerCAmelCase__ :  Dict							      =					middle_blocks[0]
    lowerCAmelCase__ :  Union[str, Any]							      =					middle_blocks[1]
    lowerCAmelCase__ :  Dict							      =					middle_blocks[2]

    lowerCAmelCase__ :  Any							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Dict							      =					renew_resnet_paths(UpperCamelCase			)
    assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    config=UpperCamelCase			)

    lowerCAmelCase__ :  Optional[int]							      =					renew_attention_paths(UpperCamelCase			)
    lowerCAmelCase__ :  Optional[int]							      =					{
        """middle_block.1.qkv.bias""": {
            """key""": """mid_block.attentions.0.key.bias""",
            """query""": """mid_block.attentions.0.query.bias""",
            """value""": """mid_block.attentions.0.value.bias""",
        },
        """middle_block.1.qkv.weight""": {
            """key""": """mid_block.attentions.0.key.weight""",
            """query""": """mid_block.attentions.0.query.weight""",
            """value""": """mid_block.attentions.0.value.weight""",
        },
    }
    assign_to_checkpoint(
        UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    attention_paths_to_split=UpperCamelCase							,    config=UpperCamelCase			)

    for i in range(UpperCamelCase			):
        lowerCAmelCase__ :  Tuple							      =					i // (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  List[str]							      =					i % (config["""num_res_blocks"""] + 1)
        lowerCAmelCase__ :  int							      =					[shave_segments(UpperCamelCase							,    2			) for name in output_blocks[i]]
        lowerCAmelCase__ :  Union[str, Any]							      =					{}

        for layer in output_block_layers:
            lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					layer.split("""."""			)[0], shave_segments(UpperCamelCase							,    1			)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase			)
            else:
                lowerCAmelCase__ :  str							      =					[layer_name]

        if len(UpperCamelCase			) > 1:
            lowerCAmelCase__ :  str							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
            lowerCAmelCase__ :  Dict							      =					[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

            lowerCAmelCase__ :  Optional[int]							      =					renew_resnet_paths(UpperCamelCase			)
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase			)

            lowerCAmelCase__ :  Optional[int]							      =					{"""old""": f"""output_blocks.{i}.0""", """new""": f"""up_blocks.{block_id}.resnets.{layer_in_block_id}"""}
            assign_to_checkpoint(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    config=UpperCamelCase			)

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                lowerCAmelCase__ :  List[Any]							      =					list(output_block_list.values()			).index(["""conv.weight""", """conv.bias"""]			)
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.weight"""
                ]
                lowerCAmelCase__ :  int							      =					checkpoint[
                    f"""output_blocks.{i}.{index}.conv.bias"""
                ]

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase			) == 2:
                    lowerCAmelCase__ :  Tuple							      =					[]

            if len(UpperCamelCase			):
                lowerCAmelCase__ :  Dict							      =					renew_attention_paths(UpperCamelCase			)
                lowerCAmelCase__ :  Tuple							      =					{
                    """old""": f"""output_blocks.{i}.1""",
                    """new""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                }
                lowerCAmelCase__ :  Tuple							      =					{
                    f"""output_blocks.{i}.1.qkv.bias""": {
                        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias""",
                        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias""",
                        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias""",
                    },
                    f"""output_blocks.{i}.1.qkv.weight""": {
                        """key""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight""",
                        """query""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight""",
                        """value""": f"""up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight""",
                    },
                }
                assign_to_checkpoint(
                    UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase							,    additional_replacements=[meta_path]							,    attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions			) else None							,    config=UpperCamelCase							,    )
        else:
            lowerCAmelCase__ :  int							      =					renew_resnet_paths(UpperCamelCase							,    n_shave_prefix_segments=1			)
            for path in resnet_0_paths:
                lowerCAmelCase__ :  Tuple							      =					""".""".join(["""output_blocks""", str(UpperCamelCase			), path["""old"""]]			)
                lowerCAmelCase__ :  List[Any]							      =					""".""".join(["""up_blocks""", str(UpperCamelCase			), """resnets""", str(UpperCamelCase			), path["""new"""]]			)

                lowerCAmelCase__ :  Union[str, Any]							      =					checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )

 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The config json file corresponding to the architecture.''',
 )

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 _lowerCAmelCase      						=	parser.parse_args()

 _lowerCAmelCase      						=	torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  _lowerCAmelCase      						=	json.loads(f.read())

 _lowerCAmelCase      						=	convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 _lowerCAmelCase      						=	UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _lowerCAmelCase      						=	DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
  _lowerCAmelCase      						=	VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _lowerCAmelCase      						=	LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)


