


import argparse

import torch

from diffusers import UNetaDConditionModel




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







	'''simple docstring'''



	assert isinstance(__lowerCamelCase  ,			__lowerCamelCase  ), "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():
			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["num_head_channels"] // 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  )
			lowercase_			 = key.reshape(__lowerCamelCase  )
			lowercase_			 = value.reshape(__lowerCamelCase  )

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

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
		lowercase_			 = old_checkpoint[path["old"]]
		lowercase_			 = ["proj_attn.weight"]
		lowercase_			 = ["proj_out.weight", "proj_in.weight"]
		if any(k in new_path for k in names  ):
			lowercase_			 = weight[:, :, 0]
		elif any(k in new_path for k in names_a  ) and len(weight.shape  ) > 2 and ".attentions." not in new_path:
			lowercase_			 = weight[:, :, 0]
		else:
			lowercase_			 = weight




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Tuple  ,			__lowerCamelCase:		Tuple=0  ):







	'''simple docstring'''



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

		#         new_item = new_item.replace('norm.weight', 'group_norm.weight')
		#         new_item = new_item.replace('norm.bias', 'group_norm.bias')

		#         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
		#         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

		#         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

	return mapping




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Dict=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:		int  ,			__lowerCamelCase:		Any=0  ):







	'''simple docstring'''



	lowercase_			 = []
	for old_item in old_list:
		mapping.append({"old": old_item, "new": old_item}  )

	return mapping




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







	'''simple docstring'''



	lowercase_			 = []
	for old_item in old_list:
		lowercase_			 = old_item.replace("in_layers.0"  ,			"norm1"  )
		lowercase_			 = new_item.replace("in_layers.2"  ,			"conv1"  )

		lowercase_			 = new_item.replace("out_layers.0"  ,			"norm2"  )
		lowercase_			 = new_item.replace("out_layers.3"  ,			"conv2"  )

		lowercase_			 = new_item.replace("emb_layers.1"  ,			"time_emb_proj"  )
		lowercase_			 = new_item.replace("skip_connection"  ,			"conv_shortcut"  )

		lowercase_			 = shave_segments(__lowerCamelCase  ,			n_shave_prefix_segments=__lowerCamelCase  )

		if "temopral_conv" not in old_item:
			mapping.append({"old": old_item, "new": new_item}  )

	return mapping




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Tuple  ,			__lowerCamelCase:		Tuple  ,			__lowerCamelCase:		List[str]=None  ,			__lowerCamelCase:		List[Any]=False  ):







	'''simple docstring'''



	lowercase_			 = {}
	lowercase_			 = list(checkpoint.keys()  )

	lowercase_			 = "model.diffusion_model."

	# at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
	if sum(k.startswith("model_ema"  ) for k in keys  ) > 100 and extract_ema:
		print(F'Checkpoint {path} has both EMA and non-EMA weights.'  )
		print(
		    "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
		    " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."  )
		for key in keys:
			if key.startswith("model.diffusion_model"  ):
				lowercase_			 = "model_ema." + "".join(key.split("."  )[1:]  )
				lowercase_			 = checkpoint.pop(__lowerCamelCase  )
	else:
		if sum(k.startswith("model_ema"  ) for k in keys  ) > 100:
			print(
			    "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
			    " weights (usually better for inference), please make sure to add the `--extract_ema` flag."  )

		for key in keys:
			lowercase_			 = checkpoint.pop(__lowerCamelCase  )

	lowercase_			 = {}

	lowercase_			 = unet_state_dict["time_embed.0.weight"]
	lowercase_			 = unet_state_dict["time_embed.0.bias"]
	lowercase_			 = unet_state_dict["time_embed.2.weight"]
	lowercase_			 = unet_state_dict["time_embed.2.bias"]

	if config["class_embed_type"] is None:
		# No parameters to port
		...
	elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
		lowercase_			 = unet_state_dict["label_emb.0.0.weight"]
		lowercase_			 = unet_state_dict["label_emb.0.0.bias"]
		lowercase_			 = unet_state_dict["label_emb.0.2.weight"]
		lowercase_			 = unet_state_dict["label_emb.0.2.bias"]
	else:
		raise NotImplementedError(F'Not implemented `class_embed_type`: {config["class_embed_type"]}'  )

	lowercase_			 = unet_state_dict["input_blocks.0.0.weight"]
	lowercase_			 = unet_state_dict["input_blocks.0.0.bias"]

	lowercase_			 = [v for v in unet_state_dict if v.startswith("input_blocks.0.1"  )]
	lowercase_			 = renew_attention_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "input_blocks.0.1", "new": "transformer_in"}
	assign_to_checkpoint(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

	lowercase_			 = unet_state_dict["out.0.weight"]
	lowercase_			 = unet_state_dict["out.0.bias"]
	lowercase_			 = unet_state_dict["out.2.weight"]
	lowercase_			 = unet_state_dict["out.2.bias"]

	# Retrieves the keys for the input blocks only
	lowercase_			 = len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "input_blocks" in layer}  )
	lowercase_			 = {
	    layer_id: [key for key in unet_state_dict if F'input_blocks.{layer_id}' in key]
	    for layer_id in range(__lowerCamelCase  )
	}

	# Retrieves the keys for the middle blocks only
	lowercase_			 = len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "middle_block" in layer}  )
	lowercase_			 = {
	    layer_id: [key for key in unet_state_dict if F'middle_block.{layer_id}' in key]
	    for layer_id in range(__lowerCamelCase  )
	}

	# Retrieves the keys for the output blocks only
	lowercase_			 = len({".".join(layer.split("."  )[:2]  ) for layer in unet_state_dict if "output_blocks" in layer}  )
	lowercase_			 = {
	    layer_id: [key for key in unet_state_dict if F'output_blocks.{layer_id}' in key]
	    for layer_id in range(__lowerCamelCase  )
	}

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

		lowercase_			 = [
		    key for key in input_blocks[i] if F'input_blocks.{i}.0' in key and F'input_blocks.{i}.0.op' not in key
		]
		lowercase_			 = [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]
		lowercase_			 = [key for key in input_blocks[i] if F'input_blocks.{i}.2' in key]

		if F'input_blocks.{i}.op.weight' in unet_state_dict:
			lowercase_			 = unet_state_dict.pop(
			    F'input_blocks.{i}.op.weight'  )
			lowercase_			 = unet_state_dict.pop(
			    F'input_blocks.{i}.op.bias'  )

		lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
		lowercase_			 = {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
		assign_to_checkpoint(
		    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

		lowercase_			 = [key for key in resnets if "temopral_conv" in key]
		lowercase_			 = renew_temp_conv_paths(__lowerCamelCase  )
		lowercase_			 = {
		    "old": F'input_blocks.{i}.0.temopral_conv',
		    "new": F'down_blocks.{block_id}.temp_convs.{layer_in_block_id}',
		}
		assign_to_checkpoint(
		    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

		if len(__lowerCamelCase  ):
			lowercase_			 = renew_attention_paths(__lowerCamelCase  )
			lowercase_			 = {"old": F'input_blocks.{i}.1', "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
			assign_to_checkpoint(
			    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

		if len(__lowerCamelCase  ):
			lowercase_			 = renew_attention_paths(__lowerCamelCase  )
			lowercase_			 = {
			    "old": F'input_blocks.{i}.2',
			    "new": F'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
			}
			assign_to_checkpoint(
			    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

	lowercase_			 = middle_blocks[0]
	lowercase_			 = [key for key in resnet_a if "temopral_conv" in key]
	lowercase_			 = middle_blocks[1]
	lowercase_			 = middle_blocks[2]
	lowercase_			 = middle_blocks[3]
	lowercase_			 = [key for key in resnet_a if "temopral_conv" in key]

	lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "middle_block.0", "new": "mid_block.resnets.0"}
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			config=__lowerCamelCase  ,			additional_replacements=[meta_path]  )

	lowercase_			 = renew_temp_conv_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "middle_block.0.temopral_conv", "new": "mid_block.temp_convs.0"}
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			config=__lowerCamelCase  ,			additional_replacements=[meta_path]  )

	lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "middle_block.3", "new": "mid_block.resnets.1"}
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			config=__lowerCamelCase  ,			additional_replacements=[meta_path]  )

	lowercase_			 = renew_temp_conv_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "middle_block.3.temopral_conv", "new": "mid_block.temp_convs.1"}
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			config=__lowerCamelCase  ,			additional_replacements=[meta_path]  )

	lowercase_			 = renew_attention_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

	lowercase_			 = renew_attention_paths(__lowerCamelCase  )
	lowercase_			 = {"old": "middle_block.2", "new": "mid_block.temp_attentions.0"}
	assign_to_checkpoint(
	    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

	for i in range(__lowerCamelCase  ):
		lowercase_			 = i // (config["layers_per_block"] + 1)
		lowercase_			 = i % (config["layers_per_block"] + 1)
		lowercase_			 = [shave_segments(__lowerCamelCase  ,			2  ) for name in output_blocks[i]]
		lowercase_			 = {}

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

		if len(__lowerCamelCase  ) > 1:
			lowercase_			 = [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
			lowercase_			 = [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]
			lowercase_			 = [key for key in output_blocks[i] if F'output_blocks.{i}.2' in key]

			lowercase_			 = renew_resnet_paths(__lowerCamelCase  )
			lowercase_			 = renew_resnet_paths(__lowerCamelCase  )

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

			lowercase_			 = [key for key in resnets if "temopral_conv" in key]
			lowercase_			 = renew_temp_conv_paths(__lowerCamelCase  )
			lowercase_			 = {
			    "old": F'output_blocks.{i}.0.temopral_conv',
			    "new": F'up_blocks.{block_id}.temp_convs.{layer_in_block_id}',
			}
			assign_to_checkpoint(
			    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

			lowercase_			 = {k: sorted(__lowerCamelCase  ) for k, v in output_block_list.items()}
			if ["conv.bias", "conv.weight"] in output_block_list.values():
				lowercase_			 = list(output_block_list.values()  ).index(["conv.bias", "conv.weight"]  )
				lowercase_			 = unet_state_dict[
				    F'output_blocks.{i}.{index}.conv.weight'
				]
				lowercase_			 = unet_state_dict[
				    F'output_blocks.{i}.{index}.conv.bias'
				]

				# Clear attentions as they have been attributed above.
				if len(__lowerCamelCase  ) == 2:
					lowercase_			 = []

			if len(__lowerCamelCase  ):
				lowercase_			 = renew_attention_paths(__lowerCamelCase  )
				lowercase_			 = {
				    "old": F'output_blocks.{i}.1',
				    "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
				}
				assign_to_checkpoint(
				    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )

			if len(__lowerCamelCase  ):
				lowercase_			 = renew_attention_paths(__lowerCamelCase  )
				lowercase_			 = {
				    "old": F'output_blocks.{i}.2',
				    "new": F'up_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
				}
				assign_to_checkpoint(
				    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			additional_replacements=[meta_path]  ,			config=__lowerCamelCase  )
		else:
			lowercase_			 = renew_resnet_paths(__lowerCamelCase  ,			n_shave_prefix_segments=1  )
			for path in resnet_0_paths:
				lowercase_			 = ".".join(["output_blocks", str(__lowerCamelCase  ), path["old"]]  )
				lowercase_			 = ".".join(["up_blocks", str(__lowerCamelCase  ), "resnets", str(__lowerCamelCase  ), path["new"]]  )
				lowercase_			 = unet_state_dict[old_path]

			lowercase_			 = [l for l in output_block_layers if "temopral_conv" in l]
			for path in temopral_conv_paths:
				lowercase_			 = path.split("temopral_conv."  )[-1]
				lowercase_			 = ".".join(["output_blocks", str(__lowerCamelCase  ), str(__lowerCamelCase  ), "temopral_conv", pruned_path]  )
				lowercase_			 = ".".join(["up_blocks", str(__lowerCamelCase  ), "temp_convs", str(__lowerCamelCase  ), pruned_path]  )
				lowercase_			 = unet_state_dict[old_path]

	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("""--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, map_location="""cpu""")
							SCREAMING_SNAKE_CASE__       			=				UNetaDConditionModel()

							SCREAMING_SNAKE_CASE__       			=				convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

							SCREAMING_SNAKE_CASE__       			=				set(unet.state_dict().keys()) - set(converted_ckpt.keys())
							SCREAMING_SNAKE_CASE__       			=				set(converted_ckpt.keys()) - set(unet.state_dict().keys())

							assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

							# load state_dict
							unet.load_state_dict(converted_ckpt)

							unet.save_pretrained(args.dump_path)

							# -- finish converting the unet --





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				"prophetnet"
							lowerCAmelCase__          =				["past_key_values"]
							lowerCAmelCase__          =				{
							    "num_attention_heads": "num_encoder_attention_heads",
							}




							def __init__(  self						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = "gelu"						,       UpperCAmelCase = 30522						,       UpperCAmelCase = 1024						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 4096						,       UpperCAmelCase = 12						,       UpperCAmelCase = 16						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 0.1						,       UpperCAmelCase = 512						,       UpperCAmelCase = 0.02						,       UpperCAmelCase = True						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 2						,       UpperCAmelCase = 32						,       UpperCAmelCase = 128						,       UpperCAmelCase = False						,       UpperCAmelCase = 0.0						,       UpperCAmelCase = True						,       UpperCAmelCase = 0						,       UpperCAmelCase = 1						,       UpperCAmelCase = 2						,       **UpperCAmelCase						,       )     ->       Dict:






								'''simple docstring'''






								lowercase_			 = vocab_size
								lowercase_			 = hidden_size
								lowercase_			 = encoder_ffn_dim
								lowercase_			 = num_encoder_layers
								lowercase_			 = num_encoder_attention_heads
								lowercase_			 = decoder_ffn_dim
								lowercase_			 = num_decoder_layers
								lowercase_			 = num_decoder_attention_heads
								lowercase_			 = max_position_embeddings
								lowercase_			 = init_std  # Normal(0, this parameter)
								lowercase_			 = activation_function

								# parameters for prophetnet
								lowercase_			 = ngram
								lowercase_			 = num_buckets
								lowercase_			 = relative_max_distance
								lowercase_			 = disable_ngram_loss
								lowercase_			 = eps

								# 3 Types of Dropout
								lowercase_			 = attention_dropout
								lowercase_			 = activation_dropout
								lowercase_			 = dropout

								lowercase_			 = use_cache

								super().__init__(
								    pad_token_id=UpperCAmelCase						,       bos_token_id=UpperCAmelCase						,       eos_token_id=UpperCAmelCase						,       is_encoder_decoder=UpperCAmelCase						,       add_cross_attention=UpperCAmelCase						,       decoder_start_token_id=UpperCAmelCase						,       **UpperCAmelCase						,       )




							@property
							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								return self.num_encoder_layers + self.num_decoder_layers




							@num_hidden_layers.setter
							def 				A__      (  self						,       UpperCAmelCase  )     ->       Any:






								'''simple docstring'''






								raise NotImplementedError(
								    "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
								    " `num_decoder_layers`."  )

