






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a      	=  logging.get_logger(__name__)

_a      	=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_a      	=  [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



						for attribute in key.split('.'	):
												_UpperCAmelCase									=			getattr(lowerCAmelCase_ ,		lowerCAmelCase_	)

						if weight_type is not None:
												_UpperCAmelCase									=			getattr(lowerCAmelCase_ ,		lowerCAmelCase_	).shape
						else:
												_UpperCAmelCase									=			hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
												    F""" {value.shape} for {full_name}"""	)

						if weight_type == "weight":
												_UpperCAmelCase									=			value
						elif weight_type == "weight_g":
												_UpperCAmelCase									=			value
						elif weight_type == "weight_v":
												_UpperCAmelCase									=			value
						elif weight_type == "bias":
												_UpperCAmelCase									=			value
						elif weight_type == "running_mean":
												_UpperCAmelCase									=			value
						elif weight_type == "running_var":
												_UpperCAmelCase									=			value
						elif weight_type == "num_batches_tracked":
												_UpperCAmelCase									=			value
						elif weight_type == "inv_freq":
												_UpperCAmelCase									=			value
						else:
												_UpperCAmelCase									=			value

						logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Dict:





						"""simple docstring"""



						_UpperCAmelCase									=			[]
						_UpperCAmelCase									=			fairseq_model.state_dict()

						_UpperCAmelCase									=			hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase									=			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    lowerCAmelCase_ ,		lowerCAmelCase_ ,		lowerCAmelCase_ ,		lowerCAmelCase_ ,		hf_model.config.feat_extract_norm == 'group' ,		)
																		_UpperCAmelCase									=			True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase									=			'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
																														_UpperCAmelCase									=			True
																														if "*" in mapped_key:
																																				_UpperCAmelCase									=			name.split(lowerCAmelCase_	)[0].split('.'	)[-2]
																																				_UpperCAmelCase									=			mapped_key.replace('*' ,		lowerCAmelCase_	)
																														if "pos_bias_u" in name:
																																				_UpperCAmelCase									=			None
																														elif "pos_bias_v" in name:
																																				_UpperCAmelCase									=			None
																														elif "weight_g" in name:
																																				_UpperCAmelCase									=			'weight_g'
																														elif "weight_v" in name:
																																				_UpperCAmelCase									=			'weight_v'
																														elif "bias" in name:
																																				_UpperCAmelCase									=			'bias'
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				_UpperCAmelCase									=			'weight'
																														elif "running_mean" in name:
																																				_UpperCAmelCase									=			'running_mean'
																														elif "inv_freq" in name:
																																				_UpperCAmelCase									=			'inv_freq'
																														elif "running_var" in name:
																																				_UpperCAmelCase									=			'running_var'
																														elif "num_batches_tracked" in name:
																																				_UpperCAmelCase									=			'num_batches_tracked'
																														else:
																																				_UpperCAmelCase									=			None
																														set_recursively(lowerCAmelCase_ ,		lowerCAmelCase_ ,		lowerCAmelCase_ ,		lowerCAmelCase_ ,		lowerCAmelCase_	)
																								continue
												if not is_used:
																		unused_weights.append(lowerCAmelCase_	)

						logger.warning(F"""Unused weights: {unused_weights}"""	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Any:





						"""simple docstring"""



						_UpperCAmelCase									=			full_name.split('conv_layers.'	)[-1]
						_UpperCAmelCase									=			name.split('.'	)
						_UpperCAmelCase									=			int(items[0]	)
						_UpperCAmelCase									=			int(items[1]	)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    F"""{full_name} has size {value.shape}, but"""
																								    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""	)
																		_UpperCAmelCase									=			value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
						else:
												unused_weights.append(lowerCAmelCase_	)


@torch.no_grad()
def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		__lowerCAmelCase=True	)->  Optional[int]:





						"""simple docstring"""



						if config_path is not None:
												_UpperCAmelCase									=			WavaVecaConformerConfig.from_pretrained(lowerCAmelCase_ ,		hidden_act='swish'	)
						else:
												_UpperCAmelCase									=			WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												_UpperCAmelCase									=			'rotary'

						if is_finetuned:
												if dict_path:
																		_UpperCAmelCase									=			Dictionary.load(lowerCAmelCase_	)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCAmelCase									=			target_dict.pad_index
																		_UpperCAmelCase									=			target_dict.bos_index
																		_UpperCAmelCase									=			target_dict.eos_index
																		_UpperCAmelCase									=			len(target_dict.symbols	)
																		_UpperCAmelCase									=			os.path.join(lowerCAmelCase_ ,		'vocab.json'	)
																		if not os.path.isdir(lowerCAmelCase_	):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase_	)	)
																								return
																		os.makedirs(lowerCAmelCase_ ,		exist_ok=lowerCAmelCase_	)
																		_UpperCAmelCase									=			target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		_UpperCAmelCase									=			0
																		_UpperCAmelCase									=			1
																		with open(lowerCAmelCase_ ,		'w' ,		encoding='utf-8'	) as vocab_handle:
																								json.dump(lowerCAmelCase_ ,		lowerCAmelCase_	)
																		_UpperCAmelCase									=			WavaVecaCTCTokenizer(
																		    lowerCAmelCase_ ,		unk_token=target_dict.unk_word ,		pad_token=target_dict.pad_word ,		bos_token=target_dict.bos_word ,		eos_token=target_dict.eos_word ,		word_delimiter_token='|' ,		do_lower_case=lowerCAmelCase_ ,		)
																		_UpperCAmelCase									=			True if config.feat_extract_norm == 'layer' else False
																		_UpperCAmelCase									=			WavaVecaFeatureExtractor(
																		    feature_size=1 ,		sampling_rate=16_000 ,		padding_value=0 ,		do_normalize=lowerCAmelCase_ ,		return_attention_mask=lowerCAmelCase_ ,		)
																		_UpperCAmelCase									=			WavaVecaProcessor(feature_extractor=lowerCAmelCase_ ,		tokenizer=lowerCAmelCase_	)
																		processor.save_pretrained(lowerCAmelCase_	)

												_UpperCAmelCase									=			WavaVecaConformerForCTC(lowerCAmelCase_	)
						else:
												_UpperCAmelCase									=			WavaVecaConformerForPreTraining(lowerCAmelCase_	)

						if is_finetuned:
												_UpperCAmelCase									=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path] ,		arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
						else:
												_UpperCAmelCase									=			argparse.Namespace(task='audio_pretraining'	)
												_UpperCAmelCase									=			fairseq.tasks.setup_task(lowerCAmelCase_	)

												_UpperCAmelCase									=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,		task=lowerCAmelCase_	)

						_UpperCAmelCase									=			model[0].eval()

						recursively_load_weights(lowerCAmelCase_ ,		lowerCAmelCase_ ,		not is_finetuned	)

						hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		_a      	=  parser.parse_args()
		convert_wavaveca_conformer_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)





'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=1  )			->      Dict:
		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     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Tuple:
		_a     :      Any        =							[]
		for old_item in old_list:
				_a     :      Union[str, Any]        =							old_item.replace('in_layers.0'						,			'norm1'  )
				_a     :      Optional[int]        =							new_item.replace('in_layers.2'						,			'conv1'  )

				_a     :      str        =							new_item.replace('out_layers.0'						,			'norm2'  )
				_a     :      List[str]        =							new_item.replace('out_layers.3'						,			'conv2'  )

				_a     :      str        =							new_item.replace('emb_layers.1'						,			'time_emb_proj'  )
				_a     :      Tuple        =							new_item.replace('skip_connection'						,			'conv_shortcut'  )

				_a     :      Any        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_=0  )			->      Any:
		_a     :      List[str]        =							[]
		for old_item in old_list:
				_a     :      List[Any]        =							old_item

				_a     :      Optional[int]        =							new_item.replace('norm.weight'						,			'group_norm.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('norm.bias'						,			'group_norm.bias'  )

				_a     :      Any        =							new_item.replace('proj_out.weight'						,			'proj_attn.weight'  )
				_a     :      Optional[Any]        =							new_item.replace('proj_out.bias'						,			'proj_attn.bias'  )

				_a     :      Optional[int]        =							shave_segments(lowerCAmelCase_						,			n_shave_prefix_segments=lowerCAmelCase_  )

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

		return mapping



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Any:
		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():
						_a     :      Optional[Any]        =							old_checkpoint[path]
						_a     :      Optional[Any]        =							old_tensor.shape[0] // 3

						_a     :      Any        =							(-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

						_a     :      int        =							old_tensor.shape[0] // config['num_head_channels'] // 3

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

						_a     :      Dict        =							query.reshape(lowerCAmelCase_  )
						_a     :      str        =							key.reshape(lowerCAmelCase_  )
						_a     :      Optional[int]        =							value.reshape(lowerCAmelCase_  )

		for path in paths:
				_a     :      Dict        =							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
				_a     :      Any        =							new_path.replace('middle_block.0'						,			'mid_block.resnets.0'  )
				_a     :      str        =							new_path.replace('middle_block.1'						,			'mid_block.attentions.0'  )
				_a     :      Union[str, Any]        =							new_path.replace('middle_block.2'						,			'mid_block.resnets.1'  )

				if additional_replacements is not None:
						for replacement in additional_replacements:
								_a     :      int        =							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:
						_a     :      List[str]        =							old_checkpoint[path['old']][:, :, 0]
				else:
						_a     :      Dict        =							old_checkpoint[path['old']]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		_a     :      Optional[int]        =							{}

		_a     :      Dict        =							checkpoint['time_embed.0.weight']
		_a     :      Tuple        =							checkpoint['time_embed.0.bias']
		_a     :      Union[str, Any]        =							checkpoint['time_embed.2.weight']
		_a     :      List[str]        =							checkpoint['time_embed.2.bias']

		_a     :      List[str]        =							checkpoint['input_blocks.0.0.weight']
		_a     :      Union[str, Any]        =							checkpoint['input_blocks.0.0.bias']

		_a     :      Optional[int]        =							checkpoint['out.0.weight']
		_a     :      int        =							checkpoint['out.0.bias']
		_a     :      List[str]        =							checkpoint['out.2.weight']
		_a     :      Optional[int]        =							checkpoint['out.2.bias']

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

		# Retrieves the keys for the middle blocks only
		_a     :      List[Any]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'middle_block' in layer}  )
		_a     :      Union[str, Any]        =							{
		    layer_id: [key for key in checkpoint if f"""middle_block.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		# Retrieves the keys for the output blocks only
		_a     :      Optional[int]        =							len({'.'.join(layer.split('.'  )[:2]  ) for layer in checkpoint if 'output_blocks' in layer}  )
		_a     :      str        =							{
		    layer_id: [key for key in checkpoint if f"""output_blocks.{layer_id}""" in key]
		    for layer_id in range(lowerCAmelCase_  )
		}

		for i in range(1						,			lowerCAmelCase_  ):
				_a     :      List[Any]        =							(i - 1) // (config['num_res_blocks'] + 1)
				_a     :      Optional[int]        =							(i - 1) % (config['num_res_blocks'] + 1)

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

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

				if len(lowerCAmelCase_  ):
						_a     :      List[str]        =							renew_attention_paths(lowerCAmelCase_  )
						_a     :      List[Any]        =							{
						    'old': f"""input_blocks.{i}.1""",
						    'new': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}""",
						}
						_a     :      Optional[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(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			additional_replacements=[meta_path]						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_						,			)

		_a     :      str        =							middle_blocks[0]
		_a     :      Tuple        =							middle_blocks[1]
		_a     :      Any        =							middle_blocks[2]

		_a     :      List[Any]        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      Any        =							renew_resnet_paths(lowerCAmelCase_  )
		assign_to_checkpoint(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			config=lowerCAmelCase_  )

		_a     :      int        =							renew_attention_paths(lowerCAmelCase_  )
		_a     :      int        =							{
		    '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(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			attention_paths_to_split=lowerCAmelCase_						,			config=lowerCAmelCase_  )

		for i in range(lowerCAmelCase_  ):
				_a     :      List[str]        =							i // (config['num_res_blocks'] + 1)
				_a     :      Any        =							i % (config['num_res_blocks'] + 1)
				_a     :      Union[str, Any]        =							[shave_segments(lowerCAmelCase_						,			2  ) for name in output_blocks[i]]
				_a     :      Optional[Any]        =							{}

				for layer in output_block_layers:
						_a       ,					_a     :      str        =							layer.split('.'  )[0], shave_segments(lowerCAmelCase_						,			1  )
						if layer_id in output_block_list:
								output_block_list[layer_id].append(lowerCAmelCase_  )
						else:
								_a     :      str        =							[layer_name]

				if len(lowerCAmelCase_  ) > 1:
						_a     :      str        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
						_a     :      Optional[Any]        =							[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]

						_a     :      Dict        =							renew_resnet_paths(lowerCAmelCase_  )
						_a     :      str        =							renew_resnet_paths(lowerCAmelCase_  )

						_a     :      Optional[int]        =							{'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_  )

						if ["conv.weight", "conv.bias"] in output_block_list.values():
								_a     :      List[Any]        =							list(output_block_list.values()  ).index(['conv.weight', 'conv.bias']  )
								_a     :      Tuple        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.weight"""
								]
								_a     :      List[str]        =							checkpoint[
								    f"""output_blocks.{i}.{index}.conv.bias"""
								]

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

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

								_a     :      Union[str, Any]        =							checkpoint[old_path]

		return new_checkpoint


if __name__ == "__main__":
							__lowerCAmelCase									=   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									=   parser.parse_args()

							__lowerCAmelCase									=   torch.load(args.checkpoint_path)

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

							__lowerCAmelCase									=   convert_ldm_checkpoint(checkpoint, config)

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

							__lowerCAmelCase									=   UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

							try:
														__lowerCAmelCase									=   DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
														__lowerCAmelCase									=   VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

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




