



'''simple docstring'''





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil






class __A			:



 '''simple docstring'''







 def __init__(self ,   A = "cpu" ,   A = "openai/clip-vit-large-patch14"       )    ->     None:


      """simple docstring"""



      _a      =  device
      _a      =  CLIPTokenizerFast.from_pretrained(A       )
      _a      =  [0.48145466, 0.4578275, 0.40821073]
      _a      =  [0.26862954, 0.26130258, 0.27577711]
      _a      =  torchvision.transforms.Normalize(self.image_mean ,   self.image_std       )
      _a      =  torchvision.transforms.Resize(224       )
      _a      =  torchvision.transforms.CenterCrop(224       )



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  self.resize(A       )
      _a      =  self.center_crop(A       )
      _a      =  self.normalize(A       )
      return images



 def __call__(self ,   A=None ,   A=None ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  self.tokenizer(text=A ,   **A       )
      _a      =  self.preprocess_img(A       )
      _a      =  {key: value.to(self.device       ) for (key, value) in encoding.items()}
      return encoding






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A=10 ,   A=0.01 ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=None ,   A=False ,   A=True ,   A="image" ,   A=True ,   A=False ,   A=False ,   A=False ,   )    ->     None:


      """simple docstring"""



      super().__init__()
      _a      =  None
      _a      =  device if device else get_device()
      if vqgan:
           _a      =  vqgan
      else:
           _a      =  load_vqgan(self.device ,   conf_path=A ,   ckpt_path=A       )
      self.vqgan.eval()
      if clip:
           _a      =  clip
      else:
           _a      =  CLIPModel.from_pretrained('''openai/clip-vit-base-patch32'''       )
      self.clip.to(self.device       )
      _a      =  ProcessorGradientFlow(device=self.device       )

      _a      =  iterations
      _a      =  lr
      _a      =  log
      _a      =  make_grid
      _a      =  return_val
      _a      =  quantize
      _a      =  self.vqgan.decoder.z_shape



 def 					a__  (self ,   A=None ,   A=None ,   A=5 ,   A=True       )    ->     List[Any]:


      """simple docstring"""



      _a      =  []
      if output_path is None:
           _a      =  '''./animation.gif'''
      if input_path is None:
           _a      =  self.save_path
      _a      =  sorted(glob(input_path + '''/*'''       )       )
      if not len(A       ):
           raise ValueError(
               '''No images found in save path, aborting (did you pass save_intermediate=True to the generate'''
               ''' function?)'''       )
      if len(A       ) == 1:
           print('''Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'''       )
      _a      =  total_duration / len(A       )
      _a      =  [frame_duration] * len(A       )
      if extend_frames:
           _a      =  1.5
           _a      =  3
      for file_name in paths:
           if file_name.endswith('''.png'''       ):
                images.append(imageio.imread(A       )       )
      imageio.mimsave(A ,   A ,   duration=A       )
      print(f'''gif saved to {output_path}'''       )



 def 					a__  (self ,   A=None ,   A=None       )    ->     List[str]:


      """simple docstring"""



      if not (path or img):
           raise ValueError('''Input either path or tensor'''       )
      if img is not None:
           raise NotImplementedError
      _a      =  preprocess(Image.open(A       ) ,   target_image_size=256       ).to(self.device       )
      _a      =  preprocess_vqgan(A       )
      _a  ,    *_a      =  self.vqgan.encode(A       )
      return z



 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  self.latent.detach().requires_grad_()
      _a      =  base_latent + transform_vector
      if self.quantize:
           _a  ,    *_a      =  self.vqgan.quantize(A       )
      else:
           _a      =  trans_latent
      return self.vqgan.decode(A       )



 def 					a__  (self ,   A ,   A ,   A=None       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  self.clip_preprocessor(text=A ,   images=A ,   return_tensors='''pt''' ,   padding=A       )
      _a      =  self.clip(**A       )
      _a      =  clip_outputs.logits_per_image
      if weights is not None:
           _a      =  similarity_logits * weights
      return similarity_logits.sum()



 def 					a__  (self ,   A ,   A ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  self._get_clip_similarity(pos_prompts['''prompts'''] ,   A ,   weights=(1 / pos_prompts['''weights'''])       )
      if neg_prompts:
           _a      =  self._get_clip_similarity(neg_prompts['''prompts'''] ,   A ,   weights=neg_prompts['''weights''']       )
      else:
           _a      =  torch.tensor([1] ,   device=self.device       )
      _a      =  -torch.log(A       ) + torch.log(A       )
      return loss



 def 					a__  (self ,   A ,   A ,   A       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  torch.randn_like(self.latent ,   requires_grad=A ,   device=self.device       )
      _a      =  torch.optim.Adam([vector] ,   lr=self.lr       )

      for i in range(self.iterations       ):
           optim.zero_grad()
           _a      =  self._add_vector(A       )
           _a      =  loop_post_process(A       )
           _a      =  self._get_CLIP_loss(A ,   A ,   A       )
           print('''CLIP loss''' ,   A       )
           if self.log:
                wandb.log({'''CLIP Loss''': clip_loss}       )
           clip_loss.backward(retain_graph=A       )
           optim.step()
           if self.return_val == "image":
                yield custom_to_pil(transformed_img[0]       )
           else:
                yield vector



 def 					a__  (self ,   A ,   A ,   A       )    ->     List[str]:


      """simple docstring"""



      wandb.init(reinit=A ,   project='''face-editor'''       )
      wandb.config.update({'''Positive Prompts''': positive_prompts}       )
      wandb.config.update({'''Negative Prompts''': negative_prompts}       )
      wandb.config.update({'''lr''': self.lr, '''iterations''': self.iterations}       )
      if image_path:
           _a      =  Image.open(A       )
           _a      =  image.resize((256, 256)       )
           wandb.log('''Original Image''' ,   wandb.Image(A       )       )



 def 					a__  (self ,   A       )    ->     Any:


      """simple docstring"""



      if not prompts:
           return []
      _a      =  []
      _a      =  []
      if isinstance(A ,   A       ):
           _a      =  [prompt.strip() for prompt in prompts.split('''|'''       )]
      for prompt in prompts:
           if isinstance(A ,   (tuple, list)       ):
                _a      =  prompt[0]
                _a      =  float(prompt[1]       )
           elif ":" in prompt:
                _a  ,    _a      =  prompt.split(''':'''       )
                _a      =  float(A       )
           else:
                _a      =  prompt
                _a      =  1.0
           processed_prompts.append(A       )
           weights.append(A       )
      return {
          "prompts": processed_prompts,
          "weights": torch.tensor(A ,   device=self.device       ),
      }



 def 					a__  (self ,   A ,   A=None ,   A=None ,   A=True ,   A=False ,   A=True ,   A=True ,   A=None ,   )    ->     List[Any]:


      """simple docstring"""



      if image_path:
           _a      =  self._get_latent(A       )
      else:
           _a      =  torch.randn(self.latent_dim ,   device=self.device       )
      if self.log:
           self._init_logging(A ,   A ,   A       )

      assert pos_prompts, "You must provide at least one positive prompt."
      _a      =  self.process_prompts(A       )
      _a      =  self.process_prompts(A       )
      if save_final and save_path is None:
           _a      =  os.path.join('''./outputs/''' ,   '''_'''.join(pos_prompts['''prompts''']       )       )
      if not os.path.exists(A       ):
           os.makedirs(A       )
      else:
           _a      =  save_path + '''_''' + get_timestamp()
           os.makedirs(A       )
      _a      =  save_path

      _a      =  self.vqgan.decode(self.latent       )[0]
      if show_intermediate:
           print('''Original Image'''       )
           show_pil(custom_to_pil(A       )       )

      _a      =  loop_post_process(A       )
      for iter, transformed_img in enumerate(self._optimize_CLIP(A ,   A ,   A       )       ):
           if show_intermediate:
                show_pil(A       )
           if save_intermediate:
                transformed_img.save(os.path.join(self.save_path ,   f'''iter_{iter:03d}.png'''       )       )
           if self.log:
                wandb.log({'''Image''': wandb.Image(A       )}       )
      if show_final:
           show_pil(A       )
      if save_final:
           transformed_img.save(os.path.join(self.save_path ,   f'''iter_{iter:03d}_final.png'''       )       )








'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

lowercase_		       =  {
    "post_extract_proj": "feature_projection.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.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": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase_		       =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     for attribute in key.split('''.'''):
          if is_finetuned:
               if attribute in ["quantizer", "project_q", "project_hid"]:
                    # those layers are only relevant for pretraining and should be dropped
                    return

               if attribute == "ctc_proj":
                    # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                    _a      =  '''lm_head'''

          _a      =  getattr(__A  ,	__A)

     if weight_type is not None:
          _a      =  getattr(__A  ,	__A).shape
     else:
          _a      =  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":
          _a      =  value
     elif weight_type == "weight_g":
          _a      =  value
     elif weight_type == "weight_v":
          _a      =  value
     elif weight_type == "bias":
          _a      =  value
     else:
          _a      =  value

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




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  []
     _a      =  fairseq_model.state_dict()

     _a      =  hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =  False
          if "conv_layers" in name:
               load_conv_layer(
                   __A  ,	__A  ,	__A  ,	__A  ,	hf_model.config.feat_extract_norm == '''group'''  ,	)
               _a      =  True
          else:
               for key, mapped_key in MAPPING.items():
                    _a      =  '''unispeech.''' + 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      =  True
                         if "*" in mapped_key:
                              _a      =  name.split(__A)[0].split('''.''')[-2]
                              _a      =  mapped_key.replace('''*'''  ,	__A)
                         if "weight_g" in name:
                              _a      =  '''weight_g'''
                         elif "weight_v" in name:
                              _a      =  '''weight_v'''
                         elif "bias" in name:
                              _a      =  '''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a      =  '''weight'''
                         else:
                              _a      =  None
                         set_recursively(__A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)
                    continue
          if not is_used:
               unused_weights.append(__A)

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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  full_name.split('''conv_layers.''')[-1]
     _a      =  name.split('''.''')
     _a      =  int(items[0])
     _a      =  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.'''
               )
               _a      =  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.'''
               )
               _a      =  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."
               )
               _a      =  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.'''
               )
               _a      =  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  ,	__A  ,	__A=None  ,	__A=None  ,	__A=True):



     """simple docstring"""



     if config_path is not None:
          _a      =  UniSpeechConfig.from_pretrained(__A)
     else:
          _a      =  UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               _a      =  Dictionary.load_from_json(__A)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _a      =  target_dict.pad_index
               _a      =  target_dict.bos_index
               _a      =  target_dict.eos_index
               _a      =  len(target_dict.symbols)
               _a      =  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      =  target_dict.indices

               # fairseq has the <pad> and <s> switched
               _a      =  42
               _a      =  43
               with open(__A  ,	'''w'''  ,	encoding='''utf-8''') as vocab_handle:
                    json.dump(__A  ,	__A)
               _a      =  WavaVecaPhonemeCTCTokenizer(
                   __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      =  True if config.feat_extract_norm == '''layer''' else False
               _a      =  WavaVecaFeatureExtractor(
                   feature_size=1  ,	sampling_rate=16_000  ,	padding_value=0  ,	do_normalize=__A  ,	return_attention_mask=__A  ,	)
               _a      =  WavaVecaProcessor(feature_extractor=__A  ,	tokenizer=__A)
               processor.save_pretrained(__A)

          _a      =  UniSpeechForCTC(__A)
     else:
          _a      =  UniSpeechForPreTraining(__A)

     if is_finetuned:
          _a  ,    _a  ,    _a      =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]  ,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
     else:
          _a  ,    _a  ,    _a      =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

     _a      =  model[0].eval()

     recursively_load_weights(__A  ,	__A  ,	__A)

     hf_unispeech.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_		       =  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"
  )
  lowercase_		       =  parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )



