



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def    __lowerCamelCase						(      _lowercase			,       _lowercase=1	)   ->    Optional[int]:
							if n_shave_prefix_segments >= 0:
														return ".".join(path.split("""."""	)[n_shave_prefix_segments:]	)
							else:
														return ".".join(path.split("""."""	)[:n_shave_prefix_segments]	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase=0	)   ->    Tuple:
							UpperCAmelCase  :	Tuple							  =	[]
							for old_item in old_list:
														UpperCAmelCase  :	List[Any]							  =	old_item.replace("""in_layers.0"""			,       """norm1"""	)
														UpperCAmelCase  :	Dict							  =	new_item.replace("""in_layers.2"""			,       """conv1"""	)

														UpperCAmelCase  :	Any							  =	new_item.replace("""out_layers.0"""			,       """norm2"""	)
														UpperCAmelCase  :	int							  =	new_item.replace("""out_layers.3"""			,       """conv2"""	)

														UpperCAmelCase  :	Optional[Any]							  =	new_item.replace("""emb_layers.1"""			,       """time_emb_proj"""	)
														UpperCAmelCase  :	Dict							  =	new_item.replace("""skip_connection"""			,       """conv_shortcut"""	)

														UpperCAmelCase  :	str							  =	shave_segments(_lowercase			,       n_shave_prefix_segments=_lowercase	)

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

							return mapping



def    __lowerCamelCase						(      _lowercase			,       _lowercase=0	)   ->    str:
							UpperCAmelCase  :	Dict							  =	[]
							for old_item in old_list:
														UpperCAmelCase  :	Dict							  =	old_item

														UpperCAmelCase  :	str							  =	new_item.replace("""norm.weight"""			,       """group_norm.weight"""	)
														UpperCAmelCase  :	Optional[Any]							  =	new_item.replace("""norm.bias"""			,       """group_norm.bias"""	)

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

														UpperCAmelCase  :	Union[str, Any]							  =	shave_segments(_lowercase			,       n_shave_prefix_segments=_lowercase	)

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

							return mapping



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=None			,       _lowercase=None	)   ->    Dict:
							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():
																					UpperCAmelCase  :	Optional[Any]							  =	old_checkpoint[path]
																					UpperCAmelCase  :	str							  =	old_tensor.shape[0] // 3

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

																					UpperCAmelCase  :	str							  =	old_tensor.shape[0] // config["""num_head_channels"""] // 3

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

																					UpperCAmelCase  :	Optional[Any]							  =	query.reshape(_lowercase	)
																					UpperCAmelCase  :	Optional[Any]							  =	key.reshape(_lowercase	)
																					UpperCAmelCase  :	Any							  =	value.reshape(_lowercase	)

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

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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[str]:
							UpperCAmelCase  :	Optional[int]							  =	{}

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

							UpperCAmelCase  :	Any							  =	checkpoint["""input_blocks.0.0.weight"""]
							UpperCAmelCase  :	str							  =	checkpoint["""input_blocks.0.0.bias"""]

							UpperCAmelCase  :	Optional[Any]							  =	checkpoint["""out.0.weight"""]
							UpperCAmelCase  :	List[Any]							  =	checkpoint["""out.0.bias"""]
							UpperCAmelCase  :	Optional[Any]							  =	checkpoint["""out.2.weight"""]
							UpperCAmelCase  :	List[str]							  =	checkpoint["""out.2.bias"""]

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

							# Retrieves the keys for the middle blocks only
							UpperCAmelCase  :	str							  =	len({""".""".join(layer.split("""."""	)[:2]	) for layer in checkpoint if """middle_block""" in layer}	)
							UpperCAmelCase  :	Any							  =	{
							    layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
							    for layer_id in range(_lowercase	)
							}

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

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

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

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

														if len(_lowercase	):
																					UpperCAmelCase  :	Any							  =	renew_attention_paths(_lowercase	)
																					UpperCAmelCase  :	Optional[Any]							  =	{
																					    """old""": F'''input_blocks.{i}.1''',
																					    """new""": F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
																					}
																					UpperCAmelCase  :	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(
																					    _lowercase			,       _lowercase			,       _lowercase			,       additional_replacements=[meta_path]			,       attention_paths_to_split=_lowercase			,       config=_lowercase			,       )

							UpperCAmelCase  :	Optional[Any]							  =	middle_blocks[0]
							UpperCAmelCase  :	Tuple							  =	middle_blocks[1]
							UpperCAmelCase  :	Any							  =	middle_blocks[2]

							UpperCAmelCase  :	int							  =	renew_resnet_paths(_lowercase	)
							assign_to_checkpoint(_lowercase			,       _lowercase			,       _lowercase			,       config=_lowercase	)

							UpperCAmelCase  :	Dict							  =	renew_resnet_paths(_lowercase	)
							assign_to_checkpoint(_lowercase			,       _lowercase			,       _lowercase			,       config=_lowercase	)

							UpperCAmelCase  :	int							  =	renew_attention_paths(_lowercase	)
							UpperCAmelCase  :	Union[str, 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(
							    _lowercase			,       _lowercase			,       _lowercase			,       attention_paths_to_split=_lowercase			,       config=_lowercase	)

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

														for layer in output_block_layers:
																					UpperCAmelCase  :	Any							  =	layer.split("""."""	)[0], shave_segments(_lowercase			,       1	)
																					if layer_id in output_block_list:
																												output_block_list[layer_id].append(_lowercase	)
																					else:
																												UpperCAmelCase  :	Tuple							  =	[layer_name]

														if len(_lowercase	) > 1:
																					UpperCAmelCase  :	List[Any]							  =	[key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
																					UpperCAmelCase  :	Union[str, Any]							  =	[key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

																					UpperCAmelCase  :	int							  =	renew_resnet_paths(_lowercase	)
																					UpperCAmelCase  :	Dict							  =	renew_resnet_paths(_lowercase	)

																					UpperCAmelCase  :	List[str]							  =	{"""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	)

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

																												# Clear attentions as they have been attributed above.
																												if len(_lowercase	) == 2:
																																			UpperCAmelCase  :	Any							  =	[]

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

																												UpperCAmelCase  :	List[Any]							  =	checkpoint[old_path]

							return new_checkpoint


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

							a     :    Union[str, Any]      						=					parser.parse_args()

							a     :    Tuple      						=					torch.load(args.checkpoint_path)

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

							a     :    str      						=					convert_ldm_checkpoint(checkpoint, config)

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

							a     :    Optional[Any]      						=					UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

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






'''simple docstring'''




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


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

