






"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__SCREAMING_SNAKE_CASE      =      {
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						for attribute in key.split('.'   ):
												lowerCAmelCase      :List[Any]				  = getattr(a__    ,			a__   )

						if weight_type is not None:
												lowerCAmelCase      :Any				  = getattr(a__    ,			a__   ).shape
						else:
												lowerCAmelCase      :Optional[int]				  = hf_pointer.shape

						assert hf_shape == value.shape, (
						    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":
												lowerCAmelCase      :Dict				  = value
						elif weight_type == "weight_g":
												lowerCAmelCase      :Optional[Any]				  = value
						elif weight_type == "weight_v":
												lowerCAmelCase      :List[Any]				  = value
						elif weight_type == "bias":
												lowerCAmelCase      :Optional[int]				  = value
						else:
												lowerCAmelCase      :str				  = value

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


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = []
						lowerCAmelCase      :Optional[Any]				  = fairseq_model.state_dict()

						lowerCAmelCase      :Tuple				  = hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												lowerCAmelCase      :Optional[int]				  = False
												if "conv_layers" in name:
																		load_conv_layer(
																		    a__    ,			a__    ,			a__    ,			a__    ,			hf_model.config.feat_extract_norm == 'group'    ,			)
																		lowerCAmelCase      :List[Any]				  = True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowerCAmelCase      :List[Any]				  = 'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																								if key in name or key.split('w2v_model.'   )[-1] == name.split('.'   )[0]:
																														lowerCAmelCase      :Optional[int]				  = True
																														if "*" in mapped_key:
																																				lowerCAmelCase      :List[Any]				  = name.split(a__   )[0].split('.'   )[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				lowerCAmelCase      :Optional[Any]				  = mapped_key.replace('*'    ,			a__   )
																														if "weight_g" in name:
																																				lowerCAmelCase      :Any				  = 'weight_g'
																														elif "weight_v" in name:
																																				lowerCAmelCase      :Tuple				  = 'weight_v'
																														elif "weight" in name:
																																				lowerCAmelCase      :int				  = 'weight'
																														elif "bias" in name:
																																				lowerCAmelCase      :Optional[Any]				  = 'bias'
																														else:
																																				lowerCAmelCase      :Tuple				  = None
																														set_recursively(a__    ,			a__    ,			a__    ,			a__    ,			a__   )
																								continue
												if not is_used:
																		unused_weights.append(a__   )

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


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = full_name.split('conv_layers.'   )[-1]
						lowerCAmelCase      :Union[str, Any]				  = name.split('.'   )
						lowerCAmelCase      :Union[str, Any]				  = int(items[0]   )
						lowerCAmelCase      :str				  = int(items[1]   )

						if type_id == 0:
												if "bias" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
																		)
																		lowerCAmelCase      :Dict				  = value
																		logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
																		)
																		lowerCAmelCase      :Optional[int]				  = 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:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																		    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
																		    " found."
																		)
																		lowerCAmelCase      :Union[str, Any]				  = value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
												elif "weight" in name:
																		assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																		    F"""{full_name} has size {value.shape}, but"""
																		    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
																		)
																		lowerCAmelCase      :Any				  = value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
						else:
												unused_weights.append(a__   )


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = SEWDConfig()
						if is_finetuned:
												lowerCAmelCase      :List[Any]				  = model.wav_encoder.wav_model.cfg
						else:
												lowerCAmelCase      :int				  = model.cfg

						lowerCAmelCase      :Union[str, Any]				  = fs_config.conv_bias
						lowerCAmelCase      :List[Any]				  = eval(fs_config.conv_feature_layers   )
						lowerCAmelCase      :int				  = [x[0] for x in conv_layers]
						lowerCAmelCase      :Optional[int]				  = [x[1] for x in conv_layers]
						lowerCAmelCase      :Optional[Any]				  = [x[2] for x in conv_layers]
						lowerCAmelCase      :int				  = 'gelu'
						lowerCAmelCase      :List[Any]				  = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						lowerCAmelCase      :Tuple				  = 0.0
						lowerCAmelCase      :List[Any]				  = fs_config.activation_fn.name
						lowerCAmelCase      :Tuple				  = fs_config.encoder_embed_dim
						lowerCAmelCase      :int				  = 0.02
						lowerCAmelCase      :Tuple				  = fs_config.encoder_ffn_embed_dim
						lowerCAmelCase      :Tuple				  = 1e-5
						lowerCAmelCase      :Dict				  = fs_config.encoder_layerdrop
						lowerCAmelCase      :Dict				  = fs_config.encoder_attention_heads
						lowerCAmelCase      :List[Any]				  = fs_config.conv_pos_groups
						lowerCAmelCase      :Optional[Any]				  = fs_config.conv_pos
						lowerCAmelCase      :Dict				  = len(a__   )
						lowerCAmelCase      :List[Any]				  = fs_config.encoder_layers
						lowerCAmelCase      :Optional[int]				  = fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						lowerCAmelCase      :Tuple				  = fs_config.max_position_embeddings
						lowerCAmelCase      :int				  = fs_config.position_buckets
						lowerCAmelCase      :Any				  = fs_config.share_att_key
						lowerCAmelCase      :Tuple				  = fs_config.relative_attention
						lowerCAmelCase      :Optional[Any]				  = fs_config.position_biased_input
						lowerCAmelCase      :Optional[Any]				  = tuple(fs_config.pos_att_type.split('|'   )   )
						lowerCAmelCase      :Any				  = fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												lowerCAmelCase      :Optional[int]				  = model.cfg
												lowerCAmelCase      :int				  = fs_config.final_dropout
												lowerCAmelCase      :List[str]				  = fs_config.layerdrop
						lowerCAmelCase      :Dict				  = fs_config.activation_dropout
						lowerCAmelCase      :str				  = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						lowerCAmelCase      :Union[str, Any]				  = fs_config.attention_dropout
						lowerCAmelCase      :str				  = fs_config.dropout_input
						lowerCAmelCase      :Optional[Any]				  = fs_config.dropout
						lowerCAmelCase      :Any				  = fs_config.mask_channel_length
						lowerCAmelCase      :str				  = fs_config.mask_channel_prob
						lowerCAmelCase      :Optional[Any]				  = fs_config.mask_length
						lowerCAmelCase      :List[Any]				  = fs_config.mask_prob

						lowerCAmelCase      :str				  = 'Wav2Vec2FeatureExtractor'
						lowerCAmelCase      :Optional[Any]				  = 'Wav2Vec2CTCTokenizer'

						return config


@torch.no_grad()
def 					UpperCAmelCase			(				a__    ,			a__    ,			a__=None    ,			a__=None    ,			a__=True   ):





						'''simple docstring'''






						if is_finetuned:
												lowerCAmelCase  ,   lowerCAmelCase  ,   lowerCAmelCase      :int				  = fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]    ,			arg_overrides={'data': '/'.join(dict_path.split('/'   )[:-1]   )}   )
						else:
												lowerCAmelCase  ,   lowerCAmelCase  ,   lowerCAmelCase      :Dict				  = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

						if config_path is not None:
												lowerCAmelCase      :List[str]				  = SEWDConfig.from_pretrained(a__   )
						else:
												lowerCAmelCase      :Optional[Any]				  = convert_config(model[0]    ,			a__   )
						lowerCAmelCase      :str				  = model[0].eval()

						lowerCAmelCase      :Tuple				  = True if config.feat_extract_norm == 'layer' else False
						lowerCAmelCase      :Tuple				  = WavaVecaFeatureExtractor(
						    feature_size=1    ,			sampling_rate=1_60_00    ,			padding_value=0    ,			do_normalize=a__    ,			return_attention_mask=a__    ,			)

						if is_finetuned:
												if dict_path:
																		lowerCAmelCase      :Dict				  = Dictionary.load(a__   )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowerCAmelCase      :Any				  = target_dict.pad_index
																		lowerCAmelCase      :List[Any]				  = target_dict.bos_index
																		lowerCAmelCase      :Tuple				  = target_dict.pad_index
																		lowerCAmelCase      :List[str]				  = target_dict.bos_index
																		lowerCAmelCase      :int				  = target_dict.eos_index
																		lowerCAmelCase      :Dict				  = len(target_dict.symbols   )
																		lowerCAmelCase      :List[Any]				  = os.path.join(a__    ,			'vocab.json'   )
																		if not os.path.isdir(a__   ):
																								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(a__   )   )
																								return
																		os.makedirs(a__    ,			exist_ok=a__   )
																		with open(a__    ,			'w'    ,			encoding='utf-8'   ) as vocab_handle:
																								json.dump(target_dict.indices    ,			a__   )
																		lowerCAmelCase      :Any				  = WavaVecaCTCTokenizer(
																		    a__    ,			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=a__    ,			)
																		lowerCAmelCase      :List[str]				  = WavaVecaProcessor(feature_extractor=a__    ,			tokenizer=a__   )
																		processor.save_pretrained(a__   )

												lowerCAmelCase      :str				  = SEWDForCTC(a__   )
						else:
												lowerCAmelCase      :List[str]				  = SEWDModel(a__   )
												feature_extractor.save_pretrained(a__   )

						recursively_load_weights(a__    ,			a__    ,			a__   )

						hf_model.save_pretrained(a__   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE      =      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(
		    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		__SCREAMING_SNAKE_CASE      =      parser.parse_args()
		convert_sew_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
		)







"""simple docstring"""


import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel


def 					UpperCAmelCase			(				a__    ,			a__=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 					UpperCAmelCase			(				a__    ,			a__=0   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = []
						for old_item in old_list:
												lowerCAmelCase      :Union[str, Any]				  = old_item
												lowerCAmelCase      :str				  = new_item.replace('block.'    ,			'resnets.'   )
												lowerCAmelCase      :int				  = new_item.replace('conv_shorcut'    ,			'conv1'   )
												lowerCAmelCase      :int				  = new_item.replace('in_shortcut'    ,			'conv_shortcut'   )
												lowerCAmelCase      :Union[str, Any]				  = new_item.replace('temb_proj'    ,			'time_emb_proj'   )

												lowerCAmelCase      :List[str]				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )

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

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__=0    ,			a__=False   ):





						'''simple docstring'''






						lowerCAmelCase      :Tuple				  = []
						for old_item in old_list:
												lowerCAmelCase      :int				  = old_item

												# In `model.mid`, the layer is called `attn`.
												if not in_mid:
																		lowerCAmelCase      :int				  = new_item.replace('attn'    ,			'attentions'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('.k.'    ,			'.key.'   )
												lowerCAmelCase      :Tuple				  = new_item.replace('.v.'    ,			'.value.'   )
												lowerCAmelCase      :int				  = new_item.replace('.q.'    ,			'.query.'   )

												lowerCAmelCase      :Optional[Any]				  = new_item.replace('proj_out'    ,			'proj_attn'   )
												lowerCAmelCase      :List[str]				  = new_item.replace('norm'    ,			'group_norm'   )

												lowerCAmelCase      :Any				  = shave_segments(a__    ,			n_shave_prefix_segments=a__   )
												mapping.append({'old': old_item, 'new': new_item}   )

						return mapping


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__    ,			a__=None    ,			a__=None    ,			a__=None   ):





						'''simple docstring'''






						assert isinstance(a__    ,			a__   ), "Paths should be a list of dicts containing 'old' and 'new' keys."

						if attention_paths_to_split is not None:
												if config is None:
																		raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'   )

												for path, path_map in attention_paths_to_split.items():
																		lowerCAmelCase      :Union[str, Any]				  = old_checkpoint[path]
																		lowerCAmelCase      :List[Any]				  = old_tensor.shape[0] // 3

																		lowerCAmelCase      :int				  = (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

																		lowerCAmelCase      :Optional[int]				  = old_tensor.shape[0] // config.get('num_head_channels'    ,			1   ) // 3

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

																		lowerCAmelCase      :str				  = query.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Dict				  = key.reshape(a__   ).squeeze()
																		lowerCAmelCase      :Union[str, Any]				  = value.reshape(a__   ).squeeze()

						for path in paths:
												lowerCAmelCase      :Union[str, Any]				  = path['new']

												if attention_paths_to_split is not None and new_path in attention_paths_to_split:
																		continue

												lowerCAmelCase      :Dict				  = new_path.replace('down.'    ,			'down_blocks.'   )
												lowerCAmelCase      :int				  = new_path.replace('up.'    ,			'up_blocks.'   )

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

												if "attentions" in new_path:
																		lowerCAmelCase      :Tuple				  = old_checkpoint[path['old']].squeeze()
												else:
																		lowerCAmelCase      :Optional[int]				  = old_checkpoint[path['old']]


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['temb.dense.0.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['temb.dense.0.bias']
						lowerCAmelCase      :str				  = checkpoint['temb.dense.1.weight']
						lowerCAmelCase      :int				  = checkpoint['temb.dense.1.bias']

						lowerCAmelCase      :Dict				  = checkpoint['norm_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['norm_out.bias']

						lowerCAmelCase      :Optional[int]				  = checkpoint['conv_in.weight']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_in.bias']
						lowerCAmelCase      :List[Any]				  = checkpoint['conv_out.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['conv_out.bias']

						lowerCAmelCase      :Tuple				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :Dict				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

						lowerCAmelCase      :Dict				  = len({'.'.join(layer.split('.'   )[:2]   ) for layer in checkpoint if 'up' in layer}   )
						lowerCAmelCase      :List[Any]				  = {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a__   )}

						for i in range(a__   ):
												lowerCAmelCase      :Union[str, Any]				  = (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :Tuple				  = checkpoint[
																		    F"""down.{i}.downsample.op.weight"""
																		]
																		lowerCAmelCase      :Optional[Any]				  = checkpoint[F"""down.{i}.downsample.op.bias"""]
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
												#            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

												if any('block' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :List[Any]				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in down_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Any				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__   )

												if any('attn' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :str				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in down_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :List[Any]				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Optional[Any]				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Optional[int]				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :str				  = [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,			)

						lowerCAmelCase      :Union[str, Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[Any]				  = num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :int				  = checkpoint[
																		    F"""up.{i}.upsample.conv.weight"""
																		]
																		lowerCAmelCase      :Any				  = checkpoint[F"""up.{i}.upsample.conv.bias"""]

												if any('block' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Optional[Any]				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in up_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Dict				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :Tuple				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :str				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

												if any('attn' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			2   ).split('.'   )[:2]   ) for layer in up_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :Optional[int]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :List[str]				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :Tuple				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

						lowerCAmelCase      :Tuple				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						return new_checkpoint


def 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = {}

						lowerCAmelCase      :int				  = checkpoint['encoder.norm_out.weight']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['encoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['encoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['encoder.conv_in.bias']
						lowerCAmelCase      :Dict				  = checkpoint['encoder.conv_out.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['encoder.conv_out.bias']

						lowerCAmelCase      :Dict				  = checkpoint['decoder.norm_out.weight']
						lowerCAmelCase      :List[str]				  = checkpoint['decoder.norm_out.bias']

						lowerCAmelCase      :str				  = checkpoint['decoder.conv_in.weight']
						lowerCAmelCase      :Optional[Any]				  = checkpoint['decoder.conv_in.bias']
						lowerCAmelCase      :Union[str, Any]				  = checkpoint['decoder.conv_out.weight']
						lowerCAmelCase      :int				  = checkpoint['decoder.conv_out.bias']

						lowerCAmelCase      :List[str]				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'down' in layer}   )
						lowerCAmelCase      :int				  = {
						    layer_id: [key for key in checkpoint if F"""down.{layer_id}""" in key] for layer_id in range(a__   )
						}

						lowerCAmelCase      :Any				  = len({'.'.join(layer.split('.'   )[:3]   ) for layer in checkpoint if 'up' in layer}   )
						lowerCAmelCase      :int				  = {layer_id: [key for key in checkpoint if F"""up.{layer_id}""" in key] for layer_id in range(a__   )}

						for i in range(a__   ):
												lowerCAmelCase      :Tuple				  = (i - 1) // (config['layers_per_block'] + 1)

												if any('downsample' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :List[Any]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.weight"""
																		]
																		lowerCAmelCase      :Optional[int]				  = checkpoint[
																		    F"""encoder.down.{i}.downsample.conv.bias"""
																		]

												if any('block' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :int				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in down_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :str				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""block.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :List[Any]				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__   )

												if any('attn' in layer for layer in down_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in down_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :Any				  = {
																		    layer_id: [key for key in down_blocks[i] if F"""attn.{layer_id}""" in key]
																		    for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block']   ):
																														lowerCAmelCase      :Any				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			config=a__   )

						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.block_1' in key]
						lowerCAmelCase      :Tuple				  = [key for key in checkpoint if 'mid.block_2' in key]
						lowerCAmelCase      :List[Any]				  = [key for key in checkpoint if 'mid.attn_1' in key]

						# Mid new 2
						lowerCAmelCase      :Optional[Any]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_resnet_paths(a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]    ,			)

						lowerCAmelCase      :List[str]				  = renew_attention_paths(a__    ,			in_mid=a__   )
						assign_to_checkpoint(
						    a__    ,			a__    ,			a__    ,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]    ,			)

						for i in range(a__   ):
												lowerCAmelCase      :Optional[int]				  = num_up_blocks - 1 - i

												if any('upsample' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :str				  = checkpoint[
																		    F"""decoder.up.{i}.upsample.conv.weight"""
																		]
																		lowerCAmelCase      :List[Any]				  = checkpoint[
																		    F"""decoder.up.{i}.upsample.conv.bias"""
																		]

												if any('block' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :Dict				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in up_blocks[i] if 'block' in layer}   )
																		lowerCAmelCase      :Optional[Any]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""block.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_blocks > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :List[Any]				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :List[Any]				  = renew_resnet_paths(blocks[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

												if any('attn' in layer for layer in up_blocks[i]   ):
																		lowerCAmelCase      :str				  = len(
																		    {'.'.join(shave_segments(a__    ,			3   ).split('.'   )[:3]   ) for layer in up_blocks[i] if 'attn' in layer}   )
																		lowerCAmelCase      :Optional[int]				  = {
																		    layer_id: [key for key in up_blocks[i] if F"""attn.{layer_id}""" in key] for layer_id in range(a__   )
																		}

																		if num_attn > 0:
																								for j in range(config['layers_per_block'] + 1   ):
																														lowerCAmelCase      :List[str]				  = {'old': F"""up_blocks.{i}""", 'new': F"""up_blocks.{block_id}"""}
																														lowerCAmelCase      :int				  = renew_attention_paths(attns[j]   )
																														assign_to_checkpoint(a__    ,			a__    ,			a__    ,			additional_replacements=[replace_indices]   )

						lowerCAmelCase      :Optional[Any]				  = {k.replace('mid_new_2'    ,			'mid_block'   ): v for k, v in new_checkpoint.items()}
						lowerCAmelCase      :Optional[int]				  = checkpoint['quant_conv.weight']
						lowerCAmelCase      :Tuple				  = checkpoint['quant_conv.bias']
						if "quantize.embedding.weight" in checkpoint:
												lowerCAmelCase      :Optional[Any]				  = checkpoint['quantize.embedding.weight']
						lowerCAmelCase      :Any				  = checkpoint['post_quant_conv.weight']
						lowerCAmelCase      :str				  = checkpoint['post_quant_conv.bias']

						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(
		    '--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.')

		__SCREAMING_SNAKE_CASE      =      parser.parse_args()
		__SCREAMING_SNAKE_CASE      =      torch.load(args.checkpoint_path)

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

		# unet case
		__SCREAMING_SNAKE_CASE      =      {key.split('.')[0] for key in checkpoint.keys()}
		if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
				__SCREAMING_SNAKE_CASE      =      convert_vq_autoenc_checkpoint(checkpoint, config)
		else:
				__SCREAMING_SNAKE_CASE      =      convert_ddpm_checkpoint(checkpoint, config)

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

		if config["_class_name"] == "VQModel":
				__SCREAMING_SNAKE_CASE      =      VQModel(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		elif config["_class_name"] == "AutoencoderKL":
				__SCREAMING_SNAKE_CASE      =      AutoencoderKL(**config)
				model.load_state_dict(converted_checkpoint)
				model.save_pretrained(args.dump_path)
		else:
				__SCREAMING_SNAKE_CASE      =      UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				__SCREAMING_SNAKE_CASE      =      DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))

				__SCREAMING_SNAKE_CASE      =      DDPMPipeline(unet=model, scheduler=scheduler)
				pipe.save_pretrained(args.dump_path)