






import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							List[Any]=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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Dict=0       ):
	snake_case__			:				str       =  []
	for old_item in old_list:
		snake_case__			:				Any       =  old_item.replace("in_layers.0"					,  "norm1"       )
		snake_case__			:				Dict       =  new_item.replace("in_layers.2"					,  "conv1"       )

		snake_case__			:				Union[str, Any]       =  new_item.replace("out_layers.0"					,  "norm2"       )
		snake_case__			:				List[str]       =  new_item.replace("out_layers.3"					,  "conv2"       )

		snake_case__			:				List[str]       =  new_item.replace("emb_layers.1"					,  "time_emb_proj"       )
		snake_case__			:				str       =  new_item.replace("skip_connection"					,  "conv_shortcut"       )

		snake_case__			:				int       =  shave_segments(snake_case_					,  n_shave_prefix_segments=snake_case_       )

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

	return mapping







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							str=0       ):
	snake_case__			:				Optional[int]       =  []
	for old_item in old_list:
		snake_case__			:				Optional[int]       =  old_item

		snake_case__			:				Any       =  new_item.replace("norm.weight"					,  "group_norm.weight"       )
		snake_case__			:				Any       =  new_item.replace("norm.bias"					,  "group_norm.bias"       )

		snake_case__			:				Union[str, Any]       =  new_item.replace("proj_out.weight"					,  "proj_attn.weight"       )
		snake_case__			:				int       =  new_item.replace("proj_out.bias"					,  "proj_attn.bias"       )

		snake_case__			:				Optional[int]       =  shave_segments(snake_case_					,  n_shave_prefix_segments=snake_case_       )

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

	return mapping







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Any					,  snake_case_ :							Dict					,  snake_case_ :							Any=None					,  snake_case_ :							List[str]=None					,  snake_case_ :							Any=None       ):
	assert isinstance(snake_case_					,  snake_case_       ), "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():
			snake_case__			:				Optional[int]       =  old_checkpoint[path]
			snake_case__			:				Optional[Any]       =  old_tensor.shape[0] // 3

			snake_case__			:				Optional[int]       =  (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

			snake_case__			:				Union[str, Any]       =  old_tensor.shape[0] // config["num_head_channels"] // 3

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

			snake_case__			:				int       =  query.reshape(snake_case_       )
			snake_case__			:				Any       =  key.reshape(snake_case_       )
			snake_case__			:				Optional[Any]       =  value.reshape(snake_case_       )

	for path in paths:
		snake_case__			:				Union[str, 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
		snake_case__			:				Union[str, Any]       =  new_path.replace("middle_block.0"					,  "mid_block.resnets.0"       )
		snake_case__			:				Optional[Any]       =  new_path.replace("middle_block.1"					,  "mid_block.attentions.0"       )
		snake_case__			:				Tuple       =  new_path.replace("middle_block.2"					,  "mid_block.resnets.1"       )

		if additional_replacements is not None:
			for replacement in additional_replacements:
				snake_case__			:				List[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:
			snake_case__			:				Any       =  old_checkpoint[path["old"]][:, :, 0]
		else:
			snake_case__			:				Optional[Any]       =  old_checkpoint[path["old"]]







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Any					,  snake_case_ :							Optional[int]       ):
	snake_case__			:				List[str]       =  {}

	snake_case__			:				Dict       =  checkpoint["time_embed.0.weight"]
	snake_case__			:				Dict       =  checkpoint["time_embed.0.bias"]
	snake_case__			:				List[Any]       =  checkpoint["time_embed.2.weight"]
	snake_case__			:				Optional[int]       =  checkpoint["time_embed.2.bias"]

	snake_case__			:				Tuple       =  checkpoint["input_blocks.0.0.weight"]
	snake_case__			:				Any       =  checkpoint["input_blocks.0.0.bias"]

	snake_case__			:				int       =  checkpoint["out.0.weight"]
	snake_case__			:				List[Any]       =  checkpoint["out.0.bias"]
	snake_case__			:				Optional[Any]       =  checkpoint["out.2.weight"]
	snake_case__			:				Dict       =  checkpoint["out.2.bias"]

	# Retrieves the keys for the input blocks only
	snake_case__			:				Tuple       =  len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "input_blocks" in layer}       )
	snake_case__			:				Dict       =  {
	    layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
	    for layer_id in range(snake_case_       )
	}

	# Retrieves the keys for the middle blocks only
	snake_case__			:				Dict       =  len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "middle_block" in layer}       )
	snake_case__			:				Optional[int]       =  {
	    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
	    for layer_id in range(snake_case_       )
	}

	# Retrieves the keys for the output blocks only
	snake_case__			:				Optional[int]       =  len({".".join(layer.split("."       )[:2]       ) for layer in checkpoint if "output_blocks" in layer}       )
	snake_case__			:				List[Any]       =  {
	    layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
	    for layer_id in range(snake_case_       )
	}

	for i in range(1					,  snake_case_       ):
		snake_case__			:				Union[str, Any]       =  (i - 1) // (config["num_res_blocks"] + 1)
		snake_case__			:				int       =  (i - 1) % (config["num_res_blocks"] + 1)

		snake_case__			:				Optional[int]       =  [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
		snake_case__			:				Union[str, 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:
			snake_case__			:				Dict       =  checkpoint[
			    F'''input_blocks.{i}.0.op.weight'''
			]
			snake_case__			:				Dict       =  checkpoint[
			    F'''input_blocks.{i}.0.op.bias'''
			]
			continue

		snake_case__			:				str       =  renew_resnet_paths(snake_case_       )
		snake_case__			:				Optional[Any]       =  {"old": F'''input_blocks.{i}.0''', "new": F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
		snake_case__			:				str       =  {"old": "resnets.2.op", "new": "downsamplers.0.op"}
		assign_to_checkpoint(
		    snake_case_					,  snake_case_					,  snake_case_					,  additional_replacements=[meta_path, resnet_op]					,  config=snake_case_       )

		if len(snake_case_       ):
			snake_case__			:				Tuple       =  renew_attention_paths(snake_case_       )
			snake_case__			:				Dict       =  {
			    "old": F'''input_blocks.{i}.1''',
			    "new": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
			}
			snake_case__			:				Optional[int]       =  {
			    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(
			    snake_case_					,  snake_case_					,  snake_case_					,  additional_replacements=[meta_path]					,  attention_paths_to_split=snake_case_					,  config=snake_case_					,  )

	snake_case__			:				str       =  middle_blocks[0]
	snake_case__			:				int       =  middle_blocks[1]
	snake_case__			:				Dict       =  middle_blocks[2]

	snake_case__			:				Any       =  renew_resnet_paths(snake_case_       )
	assign_to_checkpoint(snake_case_					,  snake_case_					,  snake_case_					,  config=snake_case_       )

	snake_case__			:				Optional[int]       =  renew_resnet_paths(snake_case_       )
	assign_to_checkpoint(snake_case_					,  snake_case_					,  snake_case_					,  config=snake_case_       )

	snake_case__			:				Optional[Any]       =  renew_attention_paths(snake_case_       )
	snake_case__			:				Any       =  {
	    "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(
	    snake_case_					,  snake_case_					,  snake_case_					,  attention_paths_to_split=snake_case_					,  config=snake_case_       )

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

		for layer in output_block_layers:
			snake_case__,						snake_case__			:				Optional[Any]       =  layer.split("."       )[0], shave_segments(snake_case_					,  1       )
			if layer_id in output_block_list:
				output_block_list[layer_id].append(snake_case_       )
			else:
				snake_case__			:				int       =  [layer_name]

		if len(snake_case_       ) > 1:
			snake_case__			:				Optional[int]       =  [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
			snake_case__			:				Dict       =  [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

			snake_case__			:				Dict       =  renew_resnet_paths(snake_case_       )
			snake_case__			:				Optional[Any]       =  renew_resnet_paths(snake_case_       )

			snake_case__			:				Dict       =  {"old": F'''output_blocks.{i}.0''', "new": F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
			assign_to_checkpoint(snake_case_					,  snake_case_					,  snake_case_					,  additional_replacements=[meta_path]					,  config=snake_case_       )

			if ["conv.weight", "conv.bias"] in output_block_list.values():
				snake_case__			:				Dict       =  list(output_block_list.values()       ).index(["conv.weight", "conv.bias"]       )
				snake_case__			:				Optional[int]       =  checkpoint[
				    F'''output_blocks.{i}.{index}.conv.weight'''
				]
				snake_case__			:				int       =  checkpoint[
				    F'''output_blocks.{i}.{index}.conv.bias'''
				]

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

			if len(snake_case_       ):
				snake_case__			:				int       =  renew_attention_paths(snake_case_       )
				snake_case__			:				str       =  {
				    "old": F'''output_blocks.{i}.1''',
				    "new": F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
				}
				snake_case__			:				Any       =  {
				    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(
				    snake_case_					,  snake_case_					,  snake_case_					,  additional_replacements=[meta_path]					,  attention_paths_to_split=to_split if any("qkv" in key for key in attentions       ) else None					,  config=snake_case_					,  )
		else:
			snake_case__			:				Dict       =  renew_resnet_paths(snake_case_					,  n_shave_prefix_segments=1       )
			for path in resnet_0_paths:
				snake_case__			:				Any       =  ".".join(["output_blocks", str(snake_case_       ), path["old"]]       )
				snake_case__			:				Union[str, Any]       =  ".".join(["up_blocks", str(snake_case_       ), "resnets", str(snake_case_       ), path["new"]]       )

				snake_case__			:				Optional[Any]       =  checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
	__lowerCamelCase :      List[str]										=				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 :      Optional[Any]										=				parser.parse_args()

	__lowerCamelCase :      Optional[int]										=				torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		__lowerCamelCase :      Optional[Any]										=				json.loads(f.read())

	__lowerCamelCase :      List[Any]										=				convert_ldm_checkpoint(checkpoint, config)

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

	__lowerCamelCase :      Optional[int]										=				UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		__lowerCamelCase :      Tuple										=				DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
		__lowerCamelCase :      Any										=				VQModel.from_pretrained("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Any										=				{
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      List[Any]										=				[
	    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """InstructBlipQFormerModel""",
	    """InstructBlipPreTrainedModel""",
	    """InstructBlipForConditionalGeneration""",
	    """InstructBlipVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_instructblip import (
	    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    InstructBlipConfig,
	    InstructBlipQFormerConfig,
	    InstructBlipVisionConfig,
	)
	from .processing_instructblip import InstructBlipProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_instructblip import (
		    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    InstructBlipForConditionalGeneration,
		    InstructBlipPreTrainedModel,
		    InstructBlipQFormerModel,
		    InstructBlipVisionModel,
		)

else:
	import sys

	__lowerCamelCase :      Any										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



