


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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_ :Optional[int]  							=			logging.get_logger(__name__)

a_ :List[Any]  							=			{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    '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    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__							)	-> Optional[Any]:




      '''simple docstring'''

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

      if weight_type is not None:
            SCREAMING_SNAKE_CASE__       :  str						     =		getattr(A__      ,    A__							).shape
      else:
            SCREAMING_SNAKE_CASE__       :  Dict						     =		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":
            SCREAMING_SNAKE_CASE__       :  Dict						     =		value
      elif weight_type == "weight_g":
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		value
      elif weight_type == "weight_v":
            SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		value
      elif weight_type == "bias":
            SCREAMING_SNAKE_CASE__       :  Dict						     =		value
      else:
            SCREAMING_SNAKE_CASE__       :  Any						     =		value

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




def    a   (							A__      ,    A__      ,    A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Dict						     =		[]
      SCREAMING_SNAKE_CASE__       :  int						     =		fairseq_model.state_dict()

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		False
            if "conv_layers" in name:
                  load_conv_layer(
                      A__      ,    A__      ,    A__      ,    A__      ,    hf_model.config.feat_extract_norm == '''group'''      ,    )
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		True
            else:
                  for key, mapped_key in MAPPING.items():
                        SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		'''sew.''' + 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]:
                              SCREAMING_SNAKE_CASE__       :  int						     =		True
                              if "*" in mapped_key:
                                    SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		name.split(A__							)[0].split('''.'''							)[-2]
                                    SCREAMING_SNAKE_CASE__       :  Any						     =		mapped_key.replace('''*'''      ,    A__							)
                              if "weight_g" in name:
                                    SCREAMING_SNAKE_CASE__       :  List[str]						     =		'''weight_g'''
                              elif "weight_v" in name:
                                    SCREAMING_SNAKE_CASE__       :  int						     =		'''weight_v'''
                              elif "weight" in name:
                                    SCREAMING_SNAKE_CASE__       :  Any						     =		'''weight'''
                              elif "bias" in name:
                                    SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		'''bias'''
                              else:
                                    SCREAMING_SNAKE_CASE__       :  Dict						     =		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    a   (							A__      ,    A__      ,    A__      ,    A__      ,    A__							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		full_name.split('''conv_layers.'''							)[-1]
      SCREAMING_SNAKE_CASE__       :  int						     =		name.split('''.'''							)
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		int(items[0]							)
      SCREAMING_SNAKE_CASE__       :  Dict						     =		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."""
                  )
                  SCREAMING_SNAKE_CASE__       :  List[str]						     =		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."""
                  )
                  SCREAMING_SNAKE_CASE__       :  Any						     =		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."
                  )
                  SCREAMING_SNAKE_CASE__       :  int						     =		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."""
                  )
                  SCREAMING_SNAKE_CASE__       :  int						     =		value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
      else:
            unused_weights.append(A__							)




def    a   (							A__      ,    A__							)	-> Dict:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		SEWConfig()
      if is_finetuned:
            SCREAMING_SNAKE_CASE__       :  Any						     =		model.wav_encoder.wav_model.cfg
      else:
            SCREAMING_SNAKE_CASE__       :  Any						     =		model.cfg

      SCREAMING_SNAKE_CASE__       :  str						     =		fs_config.conv_bias
      SCREAMING_SNAKE_CASE__       :  str						     =		eval(fs_config.conv_feature_layers							)
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		[x[0] for x in conv_layers]
      SCREAMING_SNAKE_CASE__       :  int						     =		[x[1] for x in conv_layers]
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		[x[2] for x in conv_layers]
      SCREAMING_SNAKE_CASE__       :  int						     =		'''gelu'''
      SCREAMING_SNAKE_CASE__       :  Any						     =		'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		0.0
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		fs_config.activation_fn.name
      SCREAMING_SNAKE_CASE__       :  Any						     =		fs_config.encoder_embed_dim
      SCREAMING_SNAKE_CASE__       :  str						     =		0.0_2
      SCREAMING_SNAKE_CASE__       :  Any						     =		fs_config.encoder_ffn_embed_dim
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		1e-5
      SCREAMING_SNAKE_CASE__       :  str						     =		fs_config.encoder_layerdrop
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		fs_config.encoder_attention_heads
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		fs_config.conv_pos_groups
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		fs_config.conv_pos
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		len(A__							)
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		fs_config.encoder_layers
      SCREAMING_SNAKE_CASE__       :  str						     =		fs_config.squeeze_factor

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

      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		'''Wav2Vec2FeatureExtractor'''
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		'''Wav2Vec2CTCTokenizer'''

      return config




@torch.no_grad()
def    a   (							A__      ,    A__      ,    A__=None      ,    A__=None      ,    A__=True							)	-> Dict:




      '''simple docstring'''

      if is_finetuned:
            SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]      ,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
      else:
            SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Tuple						     =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

      if config_path is not None:
            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		SEWConfig.from_pretrained(A__							)
      else:
            SCREAMING_SNAKE_CASE__       :  List[str]						     =		convert_config(model[0]      ,    A__							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		model[0].eval()

      SCREAMING_SNAKE_CASE__       :  Dict						     =		True if config.feat_extract_norm == '''layer''' else False
      SCREAMING_SNAKE_CASE__       :  Tuple						     =		WavaVecaFeatureExtractor(
          feature_size=1      ,    sampling_rate=1_6_0_0_0      ,    padding_value=0      ,    do_normalize=A__      ,    return_attention_mask=A__      ,    )

      if is_finetuned:
            if dict_path:
                  SCREAMING_SNAKE_CASE__       :  List[Any]						     =		Dictionary.load(A__							)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  SCREAMING_SNAKE_CASE__       :  Any						     =		target_dict.pad_index
                  SCREAMING_SNAKE_CASE__       :  int						     =		target_dict.bos_index
                  SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		target_dict.pad_index
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		target_dict.bos_index
                  SCREAMING_SNAKE_CASE__       :  str						     =		target_dict.eos_index
                  SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		len(target_dict.symbols							)
                  SCREAMING_SNAKE_CASE__       :  str						     =		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__							)
                  SCREAMING_SNAKE_CASE__       :  List[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__      ,    )
                  SCREAMING_SNAKE_CASE__       :  int						     =		WavaVecaProcessor(feature_extractor=A__      ,    tokenizer=A__							)
                  processor.save_pretrained(A__							)

            SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		SEWForCTC(A__							)
      else:
            SCREAMING_SNAKE_CASE__       :  List[Any]						     =		SEWModel(A__							)
            feature_extractor.save_pretrained(A__							)

      recursively_load_weights(A__      ,    A__      ,    A__							)

      hf_model.save_pretrained(A__							)


if __name__ == "__main__":
    a_ :Tuple  							=			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'
    )
    a_ :Union[str, Any]  							=			parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )










import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
    ("input_blocks.0.0.weight", "conv_in.weight"),
    ("input_blocks.0.0.bias", "conv_in.bias"),
    ("out.0.weight", "conv_norm_out.weight"),
    ("out.0.bias", "conv_norm_out.bias"),
    ("out.2.weight", "conv_out.weight"),
    ("out.2.bias", "conv_out.bias"),
]

UpperCAmelCase   :    Any     =		[
    # (stable-diffusion, HF Diffusers)
    ("in_layers.0", "norm1"),
    ("in_layers.2", "conv1"),
    ("out_layers.0", "norm2"),
    ("out_layers.3", "conv2"),
    ("emb_layers.1", "time_emb_proj"),
    ("skip_connection", "conv_shortcut"),
]

UpperCAmelCase   :    int     =		[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
		# loop over downblocks/upblocks

		for j in range(2):
				# loop over resnets/attentions for downblocks
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    Dict     =		f"""input_blocks.{3*i + j + 1}.0."""
				unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

				if i < 3:
						# no attention layers in down_blocks.3
						UpperCAmelCase   :    List[Any]     =		f"""down_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Optional[int]     =		f"""input_blocks.{3*i + j + 1}.1."""
						unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

		for j in range(3):
				# loop over resnets/attentions for upblocks
				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""output_blocks.{3*i + j}.0."""
				unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

				if i > 0:
						# no attention layers in up_blocks.0
						UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.attentions.{j}."""
						UpperCAmelCase   :    Tuple     =		f"""output_blocks.{3*i + j}.1."""
						unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

		if i < 3:
				# no downsample in down_blocks.3
				UpperCAmelCase   :    int     =		f"""down_blocks.{i}.downsamplers.0.conv."""
				UpperCAmelCase   :    Union[str, Any]     =		f"""input_blocks.{3*(i+1)}.0.op."""
				unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

				# no upsample in up_blocks.3
				UpperCAmelCase   :    Optional[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    str     =		f"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
				unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

UpperCAmelCase   :    str     =		"mid_block.attentions.0."
UpperCAmelCase   :    int     =		"middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
		UpperCAmelCase   :    int     =		f"""mid_block.resnets.{j}."""
		UpperCAmelCase   :    List[str]     =		f"""middle_block.{2*j}."""
		unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in unet_state_dict.keys()}
			for sd_name, hf_name in unet_conversion_map:
						lowerCamelCase    		=  sd_name
			for k, v in mapping.items():
						if "resnets" in k:
									for sd_part, hf_part in unet_conversion_map_resnet:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			for k, v in mapping.items():
						for sd_part, hf_part in unet_conversion_map_layer:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: unet_state_dict[k] for k, v in mapping.items()}
			return new_state_dict


# ================#
# VAE Conversion #
# ================#

UpperCAmelCase   :    int     =		[
    # (stable-diffusion, HF Diffusers)
    ("nin_shortcut", "conv_shortcut"),
    ("norm_out", "conv_norm_out"),
    ("mid.attn_1.", "mid_block.attentions.0."),
]

for i in range(4):
		# down_blocks have two resnets
		for j in range(2):
				UpperCAmelCase   :    List[Any]     =		f"""encoder.down_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    int     =		f"""encoder.down.{i}.block.{j}."""
				vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

		if i < 3:
				UpperCAmelCase   :    Any     =		f"""down_blocks.{i}.downsamplers.0."""
				UpperCAmelCase   :    Tuple     =		f"""down.{i}.downsample."""
				vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

				UpperCAmelCase   :    List[Any]     =		f"""up_blocks.{i}.upsamplers.0."""
				UpperCAmelCase   :    List[str]     =		f"""up.{3-i}.upsample."""
				vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

		# up_blocks have three resnets
		# also, up blocks in hf are numbered in reverse from sd
		for j in range(3):
				UpperCAmelCase   :    Any     =		f"""decoder.up_blocks.{i}.resnets.{j}."""
				UpperCAmelCase   :    str     =		f"""decoder.up.{3-i}.block.{j}."""
				vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
		UpperCAmelCase   :    Dict     =		f"""mid_block.resnets.{i}."""
		UpperCAmelCase   :    Union[str, Any]     =		f"""mid.block_{i+1}."""
		vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


UpperCAmelCase   :    Tuple     =		[
    # (stable-diffusion, HF Diffusers)
    ("norm.", "group_norm."),
    ("q.", "query."),
    ("k.", "key."),
    ("v.", "value."),
    ("proj_out.", "proj_attn."),
]






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			return w.reshape(*w.shape  ,							1  ,							1      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {k: k for k in vae_state_dict.keys()}
			for k, v in mapping.items():
						for sd_part, hf_part in vae_conversion_map:
									lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v
			for k, v in mapping.items():
						if "attentions" in k:
									for sd_part, hf_part in vae_conversion_map_attn:
												lowerCamelCase    		=  v.replace(lowerCamelCase__  ,							lowerCamelCase__      )
									lowerCamelCase    		=  v
			lowerCamelCase    		=  {v: vae_state_dict[k] for k, v in mapping.items()}
			lowerCamelCase    		=  ["""q""", """k""", """v""", """proj_out"""]
			for k, v in new_state_dict.items():
						for weight_name in weights_to_convert:
									if f'mid.attn_1.{weight_name}.weight' in k:
												print(f'Reshaping {k} for SD format'      )
												lowerCamelCase    		=  reshape_weight_for_sd(lowerCamelCase__      )
			return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


UpperCAmelCase   :    Union[str, Any]     =		[
    # (stable-diffusion, HF Diffusers)
    ("resblocks.", "text_model.encoder.layers."),
    ("ln_1", "layer_norm1"),
    ("ln_2", "layer_norm2"),
    (".c_fc.", ".fc1."),
    (".c_proj.", ".fc2."),
    (".attn", ".self_attn"),
    ("ln_final.", "transformer.text_model.final_layer_norm."),
    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
UpperCAmelCase   :    str     =		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCAmelCase   :    List[Any]     =		re.compile("|".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCAmelCase   :    int     =		{"q": 0, "k": 1, "v": 2}






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			lowerCamelCase    		=  {}
			for k, v in text_enc_dict.items():
						if (
						    k.endswith(""".self_attn.q_proj.weight"""      )
						    or k.endswith(""".self_attn.k_proj.weight"""      )
						    or k.endswith(""".self_attn.v_proj.weight"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.weight"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.weight"""      )]
									if k_pre not in capture_qkv_weight:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						if (
						    k.endswith(""".self_attn.q_proj.bias"""      )
						    or k.endswith(""".self_attn.k_proj.bias"""      )
						    or k.endswith(""".self_attn.v_proj.bias"""      )
						):
									lowerCamelCase    		=  k[: -len(""".q_proj.bias"""      )]
									lowerCamelCase    		=  k[-len("""q_proj.bias"""      )]
									if k_pre not in capture_qkv_bias:
												lowerCamelCase    		=  [None, None, None]
									lowerCamelCase    		=  v
									continue

						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  v

			for k_pre, tensors in capture_qkv_weight.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			for k_pre, tensors in capture_qkv_bias.items():
						if None in tensors:
									raise Exception("""CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"""      )
						lowerCamelCase    		=  textenc_pattern.sub(lambda lowerCamelCase__      : protected[re.escape(m.group(0      )      )]  ,							lowerCamelCase__      )
						lowerCamelCase    		=  torch.cat(lowerCamelCase__      )

			return new_state_dict






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			return text_enc_dict


if __name__ == "__main__":
		UpperCAmelCase   :    List[str]     =		argparse.ArgumentParser()

		parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
		parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
		parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
		parser.add_argument(
		    "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt."
		)

		UpperCAmelCase   :    List[str]     =		parser.parse_args()

		assert args.model_path is not None, "Must provide a model path!"

		assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

		# Path for safetensors
		UpperCAmelCase   :    int     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors")
		UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "text_encoder", "model.safetensors")

		# Load models from safetensors if it exists, if it doesn't pytorch
		if osp.exists(unet_path):
				UpperCAmelCase   :    Tuple     =		load_file(unet_path, device="cpu")
		else:
				UpperCAmelCase   :    Tuple     =		osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    List[Any]     =		torch.load(unet_path, map_location="cpu")

		if osp.exists(vae_path):
				UpperCAmelCase   :    Any     =		load_file(vae_path, device="cpu")
		else:
				UpperCAmelCase   :    Dict     =		osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin")
				UpperCAmelCase   :    Dict     =		torch.load(vae_path, map_location="cpu")

		if osp.exists(text_enc_path):
				UpperCAmelCase   :    str     =		load_file(text_enc_path, device="cpu")
		else:
				UpperCAmelCase   :    Optional[int]     =		osp.join(args.model_path, "text_encoder", "pytorch_model.bin")
				UpperCAmelCase   :    str     =		torch.load(text_enc_path, map_location="cpu")

		# Convert the UNet model
		UpperCAmelCase   :    List[str]     =		convert_unet_state_dict(unet_state_dict)
		UpperCAmelCase   :    List[str]     =		{"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}

		# Convert the VAE model
		UpperCAmelCase   :    Optional[Any]     =		convert_vae_state_dict(vae_state_dict)
		UpperCAmelCase   :    Union[str, Any]     =		{"first_stage_model." + k: v for k, v in vae_state_dict.items()}

		# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
		UpperCAmelCase   :    str     =		"text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict

		if is_vaa_model:
				# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
				UpperCAmelCase   :    Optional[Any]     =		{"transformer." + k: v for k, v in text_enc_dict.items()}
				UpperCAmelCase   :    List[str]     =		convert_text_enc_state_dict_vaa(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
		else:
				UpperCAmelCase   :    int     =		convert_text_enc_state_dict(text_enc_dict)
				UpperCAmelCase   :    Optional[Any]     =		{"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}

		# Put together new checkpoint
		UpperCAmelCase   :    Dict     =		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
		if args.half:
				UpperCAmelCase   :    List[Any]     =		{k: v.half() for k, v in state_dict.items()}

		if args.use_safetensors:
				save_file(state_dict, args.checkpoint_path)
		else:
				UpperCAmelCase   :    Union[str, Any]     =		{"state_dict": state_dict}
				torch.save(state_dict, args.checkpoint_path)

