


import argparse

import torch

from diffusers import UNetaDConditionModel

def 		snake_case_   (				lowercase__     :    Dict       ,     lowercase__     :    Tuple       ,     lowercase__     :    List[str]       ,     lowercase__     :    Dict=None       ,     lowercase__     :    str=None       ,     lowercase__     :    List[str]=None     ):






				'''simple docstring'''






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

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

												_lowerCAmelCase  						=old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

												_lowerCAmelCase  						=query.reshape(lowercase__     )
												_lowerCAmelCase  						=key.reshape(lowercase__     )
												_lowerCAmelCase  						=value.reshape(lowercase__     )

				for path in paths:
								_lowerCAmelCase  						=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:
																_lowerCAmelCase  						=new_path.replace(replacement["""old"""]       ,     replacement["""new"""]     )

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

def 		snake_case_   (				lowercase__     :    Dict       ,     lowercase__     :    Tuple=0     ):






				'''simple docstring'''






				_lowerCAmelCase  						=[]
				for old_item in old_list:
								_lowerCAmelCase  						=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 		snake_case_   (				lowercase__     :    Dict       ,     lowercase__     :    List[Any]=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 		snake_case_   (				lowercase__     :    List[str]       ,     lowercase__     :    List[str]=0     ):






				'''simple docstring'''






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

				return mapping

def 		snake_case_   (				lowercase__     :    Tuple       ,     lowercase__     :    int=0     ):






				'''simple docstring'''






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

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

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

								_lowerCAmelCase  						=shave_segments(lowercase__       ,     n_shave_prefix_segments=lowercase__     )

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

				return mapping

def 		snake_case_   (				lowercase__     :    int       ,     lowercase__     :    Union[str, Any]       ,     lowercase__     :    Union[str, Any]=None       ,     lowercase__     :    int=False     ):






				'''simple docstring'''






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

				_lowerCAmelCase  						="""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     ) > 1_00 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"""     ):
																_lowerCAmelCase  						="""model_ema.""" + """""".join(key.split("""."""     )[1:]     )
																_lowerCAmelCase  						=checkpoint.pop(lowercase__     )
				else:
								if sum(k.startswith("""model_ema"""     ) for k in keys     ) > 1_00:
												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:
												_lowerCAmelCase  						=checkpoint.pop(lowercase__     )

				_lowerCAmelCase  						={}

				_lowerCAmelCase  						=unet_state_dict["""time_embed.0.weight"""]
				_lowerCAmelCase  						=unet_state_dict["""time_embed.0.bias"""]
				_lowerCAmelCase  						=unet_state_dict["""time_embed.2.weight"""]
				_lowerCAmelCase  						=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":
								_lowerCAmelCase  						=unet_state_dict["""label_emb.0.0.weight"""]
								_lowerCAmelCase  						=unet_state_dict["""label_emb.0.0.bias"""]
								_lowerCAmelCase  						=unet_state_dict["""label_emb.0.2.weight"""]
								_lowerCAmelCase  						=unet_state_dict["""label_emb.0.2.bias"""]
				else:
								raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"     )

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

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

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

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

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

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

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

								_lowerCAmelCase  						=[
								    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
								]
								_lowerCAmelCase  						=[key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
								_lowerCAmelCase  						=[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:
												_lowerCAmelCase  						=unet_state_dict.pop(
												    f"input_blocks.{i}.op.weight"     )
												_lowerCAmelCase  						=unet_state_dict.pop(
												    f"input_blocks.{i}.op.bias"     )

								_lowerCAmelCase  						=renew_resnet_paths(lowercase__     )
								_lowerCAmelCase  						={"""old""": f"input_blocks.{i}.0", """new""": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
								assign_to_checkpoint(
								    lowercase__       ,     lowercase__       ,     lowercase__       ,     additional_replacements=[meta_path]       ,     config=lowercase__     )

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

								if len(lowercase__     ):
												_lowerCAmelCase  						=renew_attention_paths(lowercase__     )
												_lowerCAmelCase  						={"""old""": f"input_blocks.{i}.1", """new""": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
												assign_to_checkpoint(
												    lowercase__       ,     lowercase__       ,     lowercase__       ,     additional_replacements=[meta_path]       ,     config=lowercase__     )

								if len(lowercase__     ):
												_lowerCAmelCase  						=renew_attention_paths(lowercase__     )
												_lowerCAmelCase  						={
												    """old""": f"input_blocks.{i}.2",
												    """new""": f"down_blocks.{block_id}.temp_attentions.{layer_in_block_id}",
												}
												assign_to_checkpoint(
												    lowercase__       ,     lowercase__       ,     lowercase__       ,     additional_replacements=[meta_path]       ,     config=lowercase__     )

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

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

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

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

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

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

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

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

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

								if len(lowercase__     ) > 1:
												_lowerCAmelCase  						=[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
												_lowerCAmelCase  						=[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
												_lowerCAmelCase  						=[key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

												_lowerCAmelCase  						=renew_resnet_paths(lowercase__     )
												_lowerCAmelCase  						=renew_resnet_paths(lowercase__     )

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

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

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

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

												if len(lowercase__     ):
																_lowerCAmelCase  						=renew_attention_paths(lowercase__     )
																_lowerCAmelCase  						={
																    """old""": f"output_blocks.{i}.1",
																    """new""": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
																}
																assign_to_checkpoint(
																    lowercase__       ,     lowercase__       ,     lowercase__       ,     additional_replacements=[meta_path]       ,     config=lowercase__     )

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

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

				return new_checkpoint


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE	:							Optional[int]           =  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	:							Tuple           =  parser.parse_args()

		__SCREAMING_SNAKE_CASE	:							str           =  torch.load(args.checkpoint_path, map_location='''cpu''')
		__SCREAMING_SNAKE_CASE	:							str           =  UNetaDConditionModel()

		__SCREAMING_SNAKE_CASE	:							Optional[Any]           =  convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

		__SCREAMING_SNAKE_CASE	:							Dict           =  set(unet.state_dict().keys()) - set(converted_ckpt.keys())
		__SCREAMING_SNAKE_CASE	:							Optional[int]           =  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 TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__SCREAMING_SNAKE_CASE	:							str           =  {
    '''configuration_ernie_m''': ['''ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieMConfig'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Any           =  ['''ErnieMTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  [
		    '''ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ErnieMForMultipleChoice''',
		    '''ErnieMForQuestionAnswering''',
		    '''ErnieMForSequenceClassification''',
		    '''ErnieMForTokenClassification''',
		    '''ErnieMModel''',
		    '''ErnieMPreTrainedModel''',
		    '''ErnieMForInformationExtraction''',
		]


if TYPE_CHECKING:
		from .configuration_ernie_m import ERNIE_M_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ernie_m import ErnieMTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ernie_m import (
				    ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ErnieMForInformationExtraction,
				    ErnieMForMultipleChoice,
				    ErnieMForQuestionAnswering,
				    ErnieMForSequenceClassification,
				    ErnieMForTokenClassification,
				    ErnieMModel,
				    ErnieMPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE	:							str           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

