






import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase  							= get_logger(__name__)

# Datasets
lowercase  							= "https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
lowercase  							= "https://cdn-datasets.huggingface.co/datasets/datasets"
lowercase  							= "https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
lowercase  							= "https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
lowercase  							= "https://cdn-datasets.huggingface.co/datasets/metric"
lowercase  							= "https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
lowercase  							= os.environ.get("HF_ENDPOINT", "https://huggingface.co")
lowercase  							= HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
lowercase  							= "main"

lowercase  							= version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
lowercase  							= {"1", "ON", "YES", "TRUE"}
lowercase  							= ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
lowercase  							= version.parse(importlib_metadata.version("dill"))
lowercase  							= version.parse(importlib_metadata.version("pandas"))
lowercase  							= version.parse(importlib_metadata.version("pyarrow"))

lowercase  							= os.environ.get("USE_TF", "AUTO").upper()
lowercase  							= os.environ.get("USE_TORCH", "AUTO").upper()
lowercase  							= os.environ.get("USE_JAX", "AUTO").upper()

lowercase  							= "N/A"
lowercase  							= False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
	lowercase  							= importlib.util.find_spec("torch") is not None
	if TORCH_AVAILABLE:
		try:
			lowercase  							= version.parse(importlib_metadata.version("torch"))
			logger.info(f'PyTorch version {TORCH_VERSION} available.')
		except importlib_metadata.PackageNotFoundError:
			pass
else:
	logger.info("Disabling PyTorch because USE_TF is set")

lowercase  							= "N/A"
lowercase  							= False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
	lowercase  							= importlib.util.find_spec("tensorflow") is not None
	if TF_AVAILABLE:
		# For the metadata, we have to look for both tensorflow and tensorflow-cpu
		for package in [
		    "tensorflow",
		    "tensorflow-cpu",
		    "tensorflow-gpu",
		    "tf-nightly",
		    "tf-nightly-cpu",
		    "tf-nightly-gpu",
		    "intel-tensorflow",
		    "tensorflow-rocm",
		    "tensorflow-macos",
		]:
			try:
				lowercase  							= version.parse(importlib_metadata.version(package))
			except importlib_metadata.PackageNotFoundError:
				continue
			else:
				break
		else:
			lowercase  							= False
	if TF_AVAILABLE:
		if TF_VERSION.major < 2:
			logger.info(f'TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.')
			lowercase  							= False
		else:
			logger.info(f'TensorFlow version {TF_VERSION} available.')
else:
	logger.info("Disabling Tensorflow because USE_TORCH is set")


lowercase  							= "N/A"
lowercase  							= False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
	lowercase  							= importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
	if JAX_AVAILABLE:
		try:
			lowercase  							= version.parse(importlib_metadata.version("jax"))
			logger.info(f'JAX version {JAX_VERSION} available.')
		except importlib_metadata.PackageNotFoundError:
			pass
else:
	logger.info("Disabling JAX because USE_JAX is set to False")


lowercase  							= os.environ.get("USE_BEAM", "AUTO").upper()
lowercase  							= "N/A"
lowercase  							= False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
	try:
		lowercase  							= version.parse(importlib_metadata.version("apache_beam"))
		lowercase  							= True
		logger.info(f'Apache Beam version {BEAM_VERSION} available.')
	except importlib_metadata.PackageNotFoundError:
		pass
else:
	logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
lowercase  							= importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
lowercase  							= importlib.util.find_spec("PIL") is not None
lowercase  							= importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
lowercase  							= importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
lowercase  							= importlib.util.find_spec("rarfile") is not None
lowercase  							= importlib.util.find_spec("zstandard") is not None
lowercase  							= importlib.util.find_spec("lz4") is not None
lowercase  							= importlib.util.find_spec("py7zr") is not None


# Cache location
lowercase  							= "~/.cache"
lowercase  							= os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
lowercase  							= os.path.join(XDG_CACHE_HOME, "huggingface")
lowercase  							= os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

lowercase  							= os.path.join(HF_CACHE_HOME, "datasets")
lowercase  							= Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

lowercase  							= os.path.join(HF_CACHE_HOME, "metrics")
lowercase  							= Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

lowercase  							= os.path.join(HF_CACHE_HOME, "modules")
lowercase  							= Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

lowercase  							= "downloads"
lowercase  							= os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowercase  							= Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

lowercase  							= "extracted"
lowercase  							= os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowercase  							= Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowercase  							= (
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
lowercase  							= 1000

# Size of the preloaded record batch in `Dataset.__iter__`
lowercase  							= 10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
lowercase  							= 4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
lowercase  							= "500MB"

# Parquet configuration
lowercase  							= 100
lowercase  							= 100
lowercase  							= 100

# Offline mode
lowercase  							= os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
lowercase  							= 0  # Disabled
lowercase  							= float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
lowercase  							= "dataset.arrow"
lowercase  							= "indices.arrow"
lowercase  							= "state.json"
lowercase  							= "dataset_info.json"
lowercase  							= "dataset_infos.json"
lowercase  							= "LICENSE"
lowercase  							= "metric_info.json"
lowercase  							= "dataset_dict.json"

lowercase  							= "datasets_modules"

lowercase  							= 255

# Streaming
lowercase  							= 20
lowercase  							= 5

# Datasets without script
lowercase  							= 200
lowercase  							= 10
lowercase  							= 200

# Progress bars
lowercase  							= 0.05  # 20 progress updates per sec













import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel





def 						__UpperCAmelCase      (					a_							,					a_=1):
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split('.')[n_shave_prefix_segments:])
							else:
														return ".".join(path.split('.')[:n_shave_prefix_segments])





def 						__UpperCAmelCase      (					a_							,					a_=0):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item
														snake_case_           =							new_item.replace('block.'							,					'resnets.')
														snake_case_           =							new_item.replace('conv_shorcut'							,					'conv1')
														snake_case_           =							new_item.replace('in_shortcut'							,					'conv_shortcut')
														snake_case_           =							new_item.replace('temb_proj'							,					'time_emb_proj')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)

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

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_=0							,					a_=False):
							snake_case_           =							[]
							for old_item in old_list:
														snake_case_           =							old_item

														# In `model.mid`, the layer is called `attn`.
														if not in_mid:
																					snake_case_           =							new_item.replace('attn'							,					'attentions')
														snake_case_           =							new_item.replace('.k.'							,					'.key.')
														snake_case_           =							new_item.replace('.v.'							,					'.value.')
														snake_case_           =							new_item.replace('.q.'							,					'.query.')

														snake_case_           =							new_item.replace('proj_out'							,					'proj_attn')
														snake_case_           =							new_item.replace('norm'							,					'group_norm')

														snake_case_           =							shave_segments(a_							,					n_shave_prefix_segments=a_)
														mapping.append({'old': old_item, 'new': new_item})

							return mapping





def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_=None							,					a_=None							,					a_=None):
							assert isinstance(a_							,					a_), "Paths should be a list of dicts containing 'old' and 'new' keys."

							if attention_paths_to_split is not None:
														if config is None:
																					raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.')

														for path, path_map in attention_paths_to_split.items():
																					snake_case_           =							old_checkpoint[path]
																					snake_case_           =							old_tensor.shape[0] // 3

																					snake_case_           =							(-1, channels) if len(old_tensor.shape) == 3 else (-1)

																					snake_case_           =							old_tensor.shape[0] // config.get('num_head_channels'							,					1) // 3

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

																					snake_case_           =							query.reshape(a_).squeeze()
																					snake_case_           =							key.reshape(a_).squeeze()
																					snake_case_           =							value.reshape(a_).squeeze()

							for path in paths:
														snake_case_           =							path['new']

														if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																					continue

														snake_case_           =							new_path.replace('down.'							,					'down_blocks.')
														snake_case_           =							new_path.replace('up.'							,					'up_blocks.')

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

														if "attentions" in new_path:
																					snake_case_           =							old_checkpoint[path['old']].squeeze()
														else:
																					snake_case_           =							old_checkpoint[path['old']]





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['temb.dense.0.weight']
							snake_case_           =							checkpoint['temb.dense.0.bias']
							snake_case_           =							checkpoint['temb.dense.1.weight']
							snake_case_           =							checkpoint['temb.dense.1.bias']

							snake_case_           =							checkpoint['norm_out.weight']
							snake_case_           =							checkpoint['norm_out.bias']

							snake_case_           =							checkpoint['conv_in.weight']
							snake_case_           =							checkpoint['conv_in.bias']
							snake_case_           =							checkpoint['conv_out.weight']
							snake_case_           =							checkpoint['conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:2]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''down.{i}.downsample.op.weight'''
																					]
																					snake_case_           =							checkpoint[f'''down.{i}.downsample.op.bias''']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
														#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[f'''up.{i}.upsample.conv.bias''']

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					2).split('.')[:2]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							return new_checkpoint





def 						__UpperCAmelCase      (					a_							,					a_):
							snake_case_           =							{}

							snake_case_           =							checkpoint['encoder.norm_out.weight']
							snake_case_           =							checkpoint['encoder.norm_out.bias']

							snake_case_           =							checkpoint['encoder.conv_in.weight']
							snake_case_           =							checkpoint['encoder.conv_in.bias']
							snake_case_           =							checkpoint['encoder.conv_out.weight']
							snake_case_           =							checkpoint['encoder.conv_out.bias']

							snake_case_           =							checkpoint['decoder.norm_out.weight']
							snake_case_           =							checkpoint['decoder.norm_out.bias']

							snake_case_           =							checkpoint['decoder.conv_in.weight']
							snake_case_           =							checkpoint['decoder.conv_in.bias']
							snake_case_           =							checkpoint['decoder.conv_out.weight']
							snake_case_           =							checkpoint['decoder.conv_out.bias']

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'down' in layer})
							snake_case_           =							{
							    layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(a_)
							}

							snake_case_           =							len({'.'.join(layer.split('.')[:3]) for layer in checkpoint if 'up' in layer})
							snake_case_           =							{layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(a_)}

							for i in range(a_):
														snake_case_           =							(i - 1) // (config['layers_per_block'] + 1)

														if any('downsample' in layer for layer in down_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''encoder.down.{i}.downsample.conv.bias'''
																					]

														if any('block' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_)

														if any('attn' in layer for layer in down_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in down_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
																					    for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block']):
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					config=a_)

							snake_case_           =							[key for key in checkpoint if 'mid.block_1' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.block_2' in key]
							snake_case_           =							[key for key in checkpoint if 'mid.attn_1' in key]

							# Mid new 2
							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,					)

							snake_case_           =							renew_resnet_paths(a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,					)

							snake_case_           =							renew_attention_paths(a_							,					in_mid=a_)
							assign_to_checkpoint(
							    a_							,					a_							,					a_							,					additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,					)

							for i in range(a_):
														snake_case_           =							num_up_blocks - 1 - i

														if any('upsample' in layer for layer in up_blocks[i]):
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.weight'''
																					]
																					snake_case_           =							checkpoint[
																					    f'''decoder.up.{i}.upsample.conv.bias'''
																					]

														if any('block' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'block' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_blocks > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_resnet_paths(blocks[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

														if any('attn' in layer for layer in up_blocks[i]):
																					snake_case_           =							len(
																					    {'.'.join(shave_segments(a_							,					3).split('.')[:3]) for layer in up_blocks[i] if 'attn' in layer})
																					snake_case_           =							{
																					    layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(a_)
																					}

																					if num_attn > 0:
																												for j in range(config['layers_per_block'] + 1):
																																			snake_case_           =							{'old': f'''up_blocks.{i}''', 'new': f'''up_blocks.{block_id}'''}
																																			snake_case_           =							renew_attention_paths(attns[j])
																																			assign_to_checkpoint(a_							,					a_							,					a_							,					additional_replacements=[replace_indices])

							snake_case_           =							{k.replace('mid_new_2'							,					'mid_block'): v for k, v in new_checkpoint.items()}
							snake_case_           =							checkpoint['quant_conv.weight']
							snake_case_           =							checkpoint['quant_conv.bias']
							if "quantize.embedding.weight" in checkpoint:
														snake_case_           =							checkpoint['quantize.embedding.weight']
							snake_case_           =							checkpoint['post_quant_conv.weight']
							snake_case_           =							checkpoint['post_quant_conv.bias']

							return new_checkpoint


if __name__ == "__main__":
	lowercase  							= 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.")

	lowercase  							= parser.parse_args()
	lowercase  							= torch.load(args.checkpoint_path)

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

	# unet case
	lowercase  							= {key.split(".")[0] for key in checkpoint.keys()}
	if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
		lowercase  							= convert_vq_autoenc_checkpoint(checkpoint, config)
	else:
		lowercase  							= convert_ddpm_checkpoint(checkpoint, config)

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

	if config["_class_name"] == "VQModel":
		lowercase  							= VQModel(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	elif config["_class_name"] == "AutoencoderKL":
		lowercase  							= AutoencoderKL(**config)
		model.load_state_dict(converted_checkpoint)
		model.save_pretrained(args.dump_path)
	else:
		lowercase  							= UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

		lowercase  							= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

		lowercase  							= DDPMPipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)





