

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



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

		UpperCAmelCase__:	int									= new_item.replace("out_layers.0"	,"norm2"      )
		UpperCAmelCase__:	Optional[int]									= new_item.replace("out_layers.3"	,"conv2"      )

		UpperCAmelCase__:	int									= new_item.replace("emb_layers.1"	,"time_emb_proj"      )
		UpperCAmelCase__:	Tuple									= new_item.replace("skip_connection"	,"conv_shortcut"      )

		UpperCAmelCase__:	List[str]									= shave_segments(SCREAMING_SNAKE_CASE	,n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

	return mapping



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=0      ):
	UpperCAmelCase__:	str									= []
	for old_item in old_list:
		UpperCAmelCase__:	Optional[Any]									= old_item

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

		UpperCAmelCase__:	int									= new_item.replace("proj_out.weight"	,"proj_attn.weight"      )
		UpperCAmelCase__:	Tuple									= new_item.replace("proj_out.bias"	,"proj_attn.bias"      )

		UpperCAmelCase__:	Union[str, Any]									= shave_segments(SCREAMING_SNAKE_CASE	,n_shave_prefix_segments=SCREAMING_SNAKE_CASE      )

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

	return mapping



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None	,SCREAMING_SNAKE_CASE=None      ):
	assert isinstance(SCREAMING_SNAKE_CASE	,SCREAMING_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():
			UpperCAmelCase__:	Union[str, Any]									= old_checkpoint[path]
			UpperCAmelCase__:	int									= old_tensor.shape[0] // 3

			UpperCAmelCase__:	Dict									= (-1, channels) if len(old_tensor.shape      ) == 3 else (-1)

			UpperCAmelCase__:	Dict									= old_tensor.shape[0] // config["num_head_channels"] // 3

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

			UpperCAmelCase__:	Optional[int]									= query.reshape(SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	str									= key.reshape(SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	str									= value.reshape(SCREAMING_SNAKE_CASE      )

	for path in paths:
		UpperCAmelCase__:	int									= 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
		UpperCAmelCase__:	Tuple									= new_path.replace("middle_block.0"	,"mid_block.resnets.0"      )
		UpperCAmelCase__:	Any									= new_path.replace("middle_block.1"	,"mid_block.attentions.0"      )
		UpperCAmelCase__:	Dict									= new_path.replace("middle_block.2"	,"mid_block.resnets.1"      )

		if additional_replacements is not None:
			for replacement in additional_replacements:
				UpperCAmelCase__:	Dict									= 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:
			UpperCAmelCase__:	Union[str, Any]									= old_checkpoint[path["old"]][:, :, 0]
		else:
			UpperCAmelCase__:	Any									= old_checkpoint[path["old"]]



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Optional[int]									= {}

	UpperCAmelCase__:	Union[str, Any]									= checkpoint["time_embed.0.weight"]
	UpperCAmelCase__:	Any									= checkpoint["time_embed.0.bias"]
	UpperCAmelCase__:	Dict									= checkpoint["time_embed.2.weight"]
	UpperCAmelCase__:	Union[str, Any]									= checkpoint["time_embed.2.bias"]

	UpperCAmelCase__:	str									= checkpoint["input_blocks.0.0.weight"]
	UpperCAmelCase__:	Tuple									= checkpoint["input_blocks.0.0.bias"]

	UpperCAmelCase__:	str									= checkpoint["out.0.weight"]
	UpperCAmelCase__:	str									= checkpoint["out.0.bias"]
	UpperCAmelCase__:	Dict									= checkpoint["out.2.weight"]
	UpperCAmelCase__:	int									= checkpoint["out.2.bias"]

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

	# Retrieves the keys for the middle blocks only
	UpperCAmelCase__:	Optional[Any]									= len({".".join(layer.split("."      )[:2]      ) for layer in checkpoint if "middle_block" in layer}      )
	UpperCAmelCase__:	Tuple									= {
	    layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
	    for layer_id in range(SCREAMING_SNAKE_CASE      )
	}

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

	for i in range(1	,SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	List[str]									= (i - 1) // (config["num_res_blocks"] + 1)
		UpperCAmelCase__:	str									= (i - 1) % (config["num_res_blocks"] + 1)

		UpperCAmelCase__:	int									= [key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
		UpperCAmelCase__:	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:
			UpperCAmelCase__:	Union[str, Any]									= checkpoint[
			    f"input_blocks.{i}.0.op.weight"
			]
			UpperCAmelCase__:	List[str]									= checkpoint[
			    f"input_blocks.{i}.0.op.bias"
			]
			continue

		UpperCAmelCase__:	List[str]									= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	int									= {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
		UpperCAmelCase__:	Dict									= {"old": "resnets.2.op", "new": "downsamplers.0.op"}
		assign_to_checkpoint(
		    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,additional_replacements=[meta_path, resnet_op]	,config=SCREAMING_SNAKE_CASE      )

		if len(SCREAMING_SNAKE_CASE      ):
			UpperCAmelCase__:	Any									= renew_attention_paths(SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	int									= {
			    "old": f"input_blocks.{i}.1",
			    "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
			}
			UpperCAmelCase__:	List[Any]									= {
			    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(
			    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,additional_replacements=[meta_path]	,attention_paths_to_split=SCREAMING_SNAKE_CASE	,config=SCREAMING_SNAKE_CASE	,)

	UpperCAmelCase__:	Any									= middle_blocks[0]
	UpperCAmelCase__:	str									= middle_blocks[1]
	UpperCAmelCase__:	str									= middle_blocks[2]

	UpperCAmelCase__:	Any									= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
	assign_to_checkpoint(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,config=SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	List[str]									= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
	assign_to_checkpoint(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,config=SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Any									= renew_attention_paths(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	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(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,attention_paths_to_split=SCREAMING_SNAKE_CASE	,config=SCREAMING_SNAKE_CASE      )

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

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

		if len(SCREAMING_SNAKE_CASE      ) > 1:
			UpperCAmelCase__:	List[str]									= [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
			UpperCAmelCase__:	List[Any]									= [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

			UpperCAmelCase__:	List[str]									= renew_resnet_paths(SCREAMING_SNAKE_CASE      )
			UpperCAmelCase__:	Union[str, Any]									= renew_resnet_paths(SCREAMING_SNAKE_CASE      )

			UpperCAmelCase__:	List[Any]									= {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
			assign_to_checkpoint(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,additional_replacements=[meta_path]	,config=SCREAMING_SNAKE_CASE      )

			if ["conv.weight", "conv.bias"] in output_block_list.values():
				UpperCAmelCase__:	Optional[Any]									= list(output_block_list.values()      ).index(["conv.weight", "conv.bias"]      )
				UpperCAmelCase__:	Union[str, Any]									= checkpoint[
				    f"output_blocks.{i}.{index}.conv.weight"
				]
				UpperCAmelCase__:	Any									= checkpoint[
				    f"output_blocks.{i}.{index}.conv.bias"
				]

				# Clear attentions as they have been attributed above.
				if len(SCREAMING_SNAKE_CASE      ) == 2:
					UpperCAmelCase__:	Union[str, Any]									= []

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

				UpperCAmelCase__:	Dict									= checkpoint[old_path]

	return new_checkpoint


if __name__ == "__main__":
					_lowerCAmelCase  :      Any			 =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  :      str			 =parser.parse_args()

					_lowerCAmelCase  :      Union[str, Any]			 =torch.load(args.checkpoint_path)

					with open(args.config_file) as f:
										_lowerCAmelCase  :      Union[str, Any]			 =json.loads(f.read())

					_lowerCAmelCase  :      Union[str, Any]			 =convert_ldm_checkpoint(checkpoint, config)

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

					_lowerCAmelCase  :      List[Any]			 =UNetaDModel(**config)
					model.load_state_dict(converted_checkpoint)

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

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


import unittest

from transformers import DonutProcessor


_lowerCAmelCase  :      str			 ="""naver-clova-ix/donut-base"""

class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= DonutProcessor.from_pretrained(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= {
						    "name": "John Doe",
						    "age": "99",
						    "city": "Atlanta",
						    "state": "GA",
						    "zip": "30301",
						    "phone": "123-4567",
						    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
						}

						UpperCAmelCase__:	Optional[Any]									= (
						    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
						    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
						    "<s_nicknames><s_nickname>Johnny</s_nickname>"
						    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
						)
						UpperCAmelCase__:	str									= self.processor.tokenajson(lowerCamelCase__						)

						self.assertDictEqual(lowerCamelCase__				,      lowerCamelCase__						)