


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()
snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''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''',
}
snake_case__			 =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Optional[Any]	,  a	:   List[Any]	,  a	:   Optional[int]	,  a	:   Optional[int]				)    ->	str:





   """simple docstring"""
   for attribute in key.split("."				):
      a__ :Union[str, Any]						  =    getattr(a	,  a				)

   if weight_type is not None:
      a__ :List[Any]						  =    getattr(a	,  a				).shape
   else:
      a__ :Optional[Any]						  =    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":
      a__ :Dict						  =    value
   elif weight_type == "weight_g":
      a__ :str						  =    value
   elif weight_type == "weight_v":
      a__ :List[str]						  =    value
   elif weight_type == "bias":
      a__ :str						  =    value
   elif weight_type == "running_mean":
      a__ :Any						  =    value
   elif weight_type == "running_var":
      a__ :Tuple						  =    value
   elif weight_type == "num_batches_tracked":
      a__ :Dict						  =    value
   elif weight_type == "inv_freq":
      a__ :Union[str, Any]						  =    value
   else:
      a__ :Optional[int]						  =    value

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


def 				lowerCamelCase__	(      a	:   Optional[Any]	,  a	:   List[Any]	,  a	:   Any				)    ->	List[Any]:





   """simple docstring"""
   a__ :Tuple						  =    []
   a__ :Tuple						  =    fairseq_model.state_dict()

   a__ :Optional[int]						  =    hf_model.wavaveca_conformer.feature_extractor

   for name, value in fairseq_dict.items():
      a__ :Optional[int]						  =    False
      if "conv_layers" in name:
         load_conv_layer(
             a	,  a	,  a	,  a	,  hf_model.config.feat_extract_norm == "group"	,  )
         a__ :Union[str, Any]						  =    True
      else:
         for key, mapped_key in MAPPING.items():
            a__ :int						  =    "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]:
               a__ :Any						  =    True
               if "*" in mapped_key:
                  a__ :Union[str, Any]						  =    name.split(a				)[0].split("."				)[-2]
                  a__ :str						  =    mapped_key.replace("*"	,  a				)
               if "pos_bias_u" in name:
                  a__ :Dict						  =    None
               elif "pos_bias_v" in name:
                  a__ :str						  =    None
               elif "weight_g" in name:
                  a__ :List[str]						  =    "weight_g"
               elif "weight_v" in name:
                  a__ :Tuple						  =    "weight_v"
               elif "bias" in name:
                  a__ :Any						  =    "bias"
               elif "weight" in name:
                  # TODO: don't match quantizer.weight_proj
                  a__ :List[Any]						  =    "weight"
               elif "running_mean" in name:
                  a__ :List[Any]						  =    "running_mean"
               elif "inv_freq" in name:
                  a__ :Union[str, Any]						  =    "inv_freq"
               elif "running_var" in name:
                  a__ :str						  =    "running_var"
               elif "num_batches_tracked" in name:
                  a__ :Optional[Any]						  =    "num_batches_tracked"
               else:
                  a__ :List[Any]						  =    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 				lowerCamelCase__	(      a	:   Optional[int]	,  a	:   Union[str, Any]	,  a	:   str	,  a	:   Union[str, Any]	,  a	:   Optional[Any]				)    ->	Union[str, Any]:





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


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   str	,  a	:   Dict	,  a	:   int=None	,  a	:   Optional[int]=None	,  a	:   int=True				)    ->	Optional[Any]:





   """simple docstring"""
   if config_path is not None:
      a__ :Optional[int]						  =    WavaVecaConformerConfig.from_pretrained(a	,  hidden_act="swish"				)
   else:
      a__ :int						  =    WavaVecaConformerConfig()

   if "rope" in checkpoint_path:
      a__ :Any						  =    "rotary"

   if is_finetuned:
      if dict_path:
         a__ :Tuple						  =    Dictionary.load(a				)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         a__ :Tuple						  =    target_dict.pad_index
         a__ :int						  =    target_dict.bos_index
         a__ :int						  =    target_dict.eos_index
         a__ :Dict						  =    len(target_dict.symbols				)
         a__ :List[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				)
         a__ :Union[str, Any]						  =    target_dict.indices

         # fairseq has the <pad> and <s> switched
         a__ :List[Any]						  =    0
         a__ :Dict						  =    1
         with open(a	,  "w"	,  encoding="utf-8"				) as vocab_handle:
            json.dump(a	,  a				)
         a__ :List[str]						  =    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	,  )
         a__ :Dict						  =    True if config.feat_extract_norm == "layer" else False
         a__ :Union[str, Any]						  =    WavaVecaFeatureExtractor(
             feature_size=1	,  sampling_rate=16_000	,  padding_value=0	,  do_normalize=a	,  return_attention_mask=a	,  )
         a__ :List[Any]						  =    WavaVecaProcessor(feature_extractor=a	,  tokenizer=a				)
         processor.save_pretrained(a				)

      a__ :List[Any]						  =    WavaVecaConformerForCTC(a				)
   else:
      a__ :Any						  =    WavaVecaConformerForPreTraining(a				)

   if is_finetuned:
      a__ :Union[str, Any]						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]	,  arg_overrides={"data": "/".join(dict_path.split("/"				)[:-1]				)}				)
   else:
      a__ :Union[str, Any]						  =    argparse.Namespace(task="audio_pretraining"				)
      a__ :Any						  =    fairseq.tasks.setup_task(a				)

      a__ :Optional[Any]						  =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,  task=a				)

   a__ :List[Any]						  =    model[0].eval()

   recursively_load_weights(a	,  a	,  not is_finetuned				)

   hf_wavavec.save_pretrained(a				)


if __name__ == "__main__":
  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(
      '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
  )
  snake_case__			 =						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
  )






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '''--original_config_file''',
		    default=None,
		    type=str,
		    help='''The YAML config file corresponding to the original architecture.''',
		)
		parser.add_argument(
		    '''--num_in_channels''',
		    default=None,
		    type=int,
		    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
		)
		parser.add_argument(
		    '''--scheduler_type''',
		    default='''pndm''',
		    type=str,
		    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
		)
		parser.add_argument(
		    '''--pipeline_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
		        '''. If `None` pipeline will be automatically inferred.'''
		    ),
		)
		parser.add_argument(
		    '''--image_size''',
		    default=None,
		    type=int,
		    help=(
		        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
		        ''' Base. Use 768 for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--prediction_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
		        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--extract_ema''',
		    action='''store_true''',
		    help=(
		        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
		        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
		        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
		    ),
		)
		parser.add_argument(
		    '''--upcast_attention''',
		    action='''store_true''',
		    help=(
		        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
		        ''' diffusion 2.1.'''
		    ),
		)
		parser.add_argument(
		    '''--from_safetensors''',
		    action='''store_true''',
		    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
		)
		parser.add_argument(
		    '''--to_safetensors''',
		    action='''store_true''',
		    help='''Whether to store pipeline in safetensors format or not.''',
		)
		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
		parser.add_argument(
		    '''--stable_unclip''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
		)
		parser.add_argument(
		    '''--stable_unclip_prior''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
		)
		parser.add_argument(
		    '''--clip_stats_path''',
		    type=str,
		    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
		    required=False,
		)
		parser.add_argument(
		    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
		)
		parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
		parser.add_argument(
		    '''--vae_path''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
		)
		snake_case__			 =						parser.parse_args()

		snake_case__			 =						download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


