


import argparse
import json

import torch

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Optional[int]=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_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		str=0  ):







	'''simple docstring'''



	lowercase_			 = []
	for old_item in old_list:
		lowercase_			 = old_item
		lowercase_			 = new_item.replace("block."  ,			"resnets."  )
		lowercase_			 = new_item.replace("conv_shorcut"  ,			"conv1"  )
		lowercase_			 = new_item.replace("in_shortcut"  ,			"conv_shortcut"  )
		lowercase_			 = new_item.replace("temb_proj"  ,			"time_emb_proj"  )

		lowercase_			 = shave_segments(__lowerCamelCase  ,			n_shave_prefix_segments=__lowerCamelCase  )

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

	return mapping




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		List[Any]=0  ,			__lowerCamelCase:		Any=False  ):







	'''simple docstring'''



	lowercase_			 = []
	for old_item in old_list:
		lowercase_			 = old_item

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

		lowercase_			 = new_item.replace("proj_out"  ,			"proj_attn"  )
		lowercase_			 = new_item.replace("norm"  ,			"group_norm"  )

		lowercase_			 = shave_segments(__lowerCamelCase  ,			n_shave_prefix_segments=__lowerCamelCase  )
		mapping.append({"old": old_item, "new": new_item}  )

	return mapping




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		str  ,			__lowerCamelCase:		Any  ,			__lowerCamelCase:		Tuple=None  ,			__lowerCamelCase:		List[Any]=None  ,			__lowerCamelCase:		Union[str, Any]=None  ):







	'''simple docstring'''



	assert isinstance(__lowerCamelCase  ,			__lowerCamelCase  ), "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():
			lowercase_			 = old_checkpoint[path]
			lowercase_			 = old_tensor.shape[0] // 3

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

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

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

			lowercase_			 = query.reshape(__lowerCamelCase  ).squeeze()
			lowercase_			 = key.reshape(__lowerCamelCase  ).squeeze()
			lowercase_			 = value.reshape(__lowerCamelCase  ).squeeze()

	for path in paths:
		lowercase_			 = path["new"]

		if attention_paths_to_split is not None and new_path in attention_paths_to_split:
			continue

		lowercase_			 = new_path.replace("down."  ,			"down_blocks."  )
		lowercase_			 = new_path.replace("up."  ,			"up_blocks."  )

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

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Dict  ):







	'''simple docstring'''



	lowercase_			 = {}

	lowercase_			 = checkpoint["temb.dense.0.weight"]
	lowercase_			 = checkpoint["temb.dense.0.bias"]
	lowercase_			 = checkpoint["temb.dense.1.weight"]
	lowercase_			 = checkpoint["temb.dense.1.bias"]

	lowercase_			 = checkpoint["norm_out.weight"]
	lowercase_			 = checkpoint["norm_out.bias"]

	lowercase_			 = checkpoint["conv_in.weight"]
	lowercase_			 = checkpoint["conv_in.bias"]
	lowercase_			 = checkpoint["conv_out.weight"]
	lowercase_			 = checkpoint["conv_out.bias"]

	lowercase_			 = len({".".join(layer.split("."  )[:2]  ) for layer in checkpoint if "down" in layer}  )
	lowercase_			 = {
	    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )
	}

	lowercase_			 = len({".".join(layer.split("."  )[:2]  ) for layer in checkpoint if "up" in layer}  )
	lowercase_			 = {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )}

	for i in range(__lowerCamelCase  ):
		lowercase_			 = (i - 1) // (config["layers_per_block"] + 1)

		if any("downsample" in layer for layer in down_blocks[i]  ):
			lowercase_			 = checkpoint[
			    F'down.{i}.downsample.op.weight'
			]
			lowercase_			 = 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]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			2  ).split("."  )[:2]  ) for layer in down_blocks[i] if "block" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
			    for layer_id in range(__lowerCamelCase  )
			}

			if num_blocks > 0:
				for j in range(config["layers_per_block"]  ):
					lowercase_			 = renew_resnet_paths(blocks[j]  )
					assign_to_checkpoint(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  )

		if any("attn" in layer for layer in down_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			2  ).split("."  )[:2]  ) for layer in down_blocks[i] if "attn" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
			    for layer_id in range(__lowerCamelCase  )
			}

			if num_attn > 0:
				for j in range(config["layers_per_block"]  ):
					lowercase_			 = renew_attention_paths(attns[j]  )
					assign_to_checkpoint(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			config=__lowerCamelCase  )

	lowercase_			 = [key for key in checkpoint if "mid.block_1" in key]
	lowercase_			 = [key for key in checkpoint if "mid.block_2" in key]
	lowercase_			 = [key for key in checkpoint if "mid.attn_1" in key]

	# Mid new 2
	lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]  ,			)

	lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]  ,			)

	lowercase_			 = renew_attention_paths(__lowerCamelCase  ,			in_mid=__lowerCamelCase  )
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]  ,			)

	for i in range(__lowerCamelCase  ):
		lowercase_			 = num_up_blocks - 1 - i

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

		if any("block" in layer for layer in up_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			2  ).split("."  )[:2]  ) for layer in up_blocks[i] if "block" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )
			}

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

		if any("attn" in layer for layer in up_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			2  ).split("."  )[:2]  ) for layer in up_blocks[i] if "attn" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )
			}

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

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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Dict  ,			__lowerCamelCase:		Any  ):







	'''simple docstring'''



	lowercase_			 = {}

	lowercase_			 = checkpoint["encoder.norm_out.weight"]
	lowercase_			 = checkpoint["encoder.norm_out.bias"]

	lowercase_			 = checkpoint["encoder.conv_in.weight"]
	lowercase_			 = checkpoint["encoder.conv_in.bias"]
	lowercase_			 = checkpoint["encoder.conv_out.weight"]
	lowercase_			 = checkpoint["encoder.conv_out.bias"]

	lowercase_			 = checkpoint["decoder.norm_out.weight"]
	lowercase_			 = checkpoint["decoder.norm_out.bias"]

	lowercase_			 = checkpoint["decoder.conv_in.weight"]
	lowercase_			 = checkpoint["decoder.conv_in.bias"]
	lowercase_			 = checkpoint["decoder.conv_out.weight"]
	lowercase_			 = checkpoint["decoder.conv_out.bias"]

	lowercase_			 = len({".".join(layer.split("."  )[:3]  ) for layer in checkpoint if "down" in layer}  )
	lowercase_			 = {
	    layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )
	}

	lowercase_			 = len({".".join(layer.split("."  )[:3]  ) for layer in checkpoint if "up" in layer}  )
	lowercase_			 = {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )}

	for i in range(__lowerCamelCase  ):
		lowercase_			 = (i - 1) // (config["layers_per_block"] + 1)

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

		if any("block" in layer for layer in down_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			3  ).split("."  )[:3]  ) for layer in down_blocks[i] if "block" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
			    for layer_id in range(__lowerCamelCase  )
			}

			if num_blocks > 0:
				for j in range(config["layers_per_block"]  ):
					lowercase_			 = renew_resnet_paths(blocks[j]  )
					assign_to_checkpoint(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  )

		if any("attn" in layer for layer in down_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			3  ).split("."  )[:3]  ) for layer in down_blocks[i] if "attn" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
			    for layer_id in range(__lowerCamelCase  )
			}

			if num_attn > 0:
				for j in range(config["layers_per_block"]  ):
					lowercase_			 = renew_attention_paths(attns[j]  )
					assign_to_checkpoint(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			config=__lowerCamelCase  )

	lowercase_			 = [key for key in checkpoint if "mid.block_1" in key]
	lowercase_			 = [key for key in checkpoint if "mid.block_2" in key]
	lowercase_			 = [key for key in checkpoint if "mid.attn_1" in key]

	# Mid new 2
	lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]  ,			)

	lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]  ,			)

	lowercase_			 = renew_attention_paths(__lowerCamelCase  ,			in_mid=__lowerCamelCase  )
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]  ,			)

	for i in range(__lowerCamelCase  ):
		lowercase_			 = num_up_blocks - 1 - i

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

		if any("block" in layer for layer in up_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			3  ).split("."  )[:3]  ) for layer in up_blocks[i] if "block" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )
			}

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

		if any("attn" in layer for layer in up_blocks[i]  ):
			lowercase_			 = len(
			    {".".join(shave_segments(__lowerCamelCase  ,			3  ).split("."  )[:3]  ) for layer in up_blocks[i] if "attn" in layer}  )
			lowercase_			 = {
			    layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(__lowerCamelCase  )
			}

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

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

	return new_checkpoint


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

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

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

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

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

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

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

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       			=				{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       			=				[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__       			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)

