import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


UpperCAmelCase_			    =							{
    '''tiny.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt''',
    '''tiny''': '''https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt''',
    '''base.en''': '''https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt''',
    '''base''': '''https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt''',
    '''small.en''': '''https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt''',
    '''small''': '''https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt''',
    '''medium.en''': '''https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt''',
    '''medium''': '''https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt''',
    '''large''': '''https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt''',
    '''large-v2''': '''https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt''',
}




def       lowerCAmelCase_     (		lowercase:							int		)    ->				Optional[int]:







 '''simple docstring'''
 _UpperCamelCase:     str							= ['''layers''', '''blocks''']
 for k in ignore_keys:
  state_dict.pop(lowercase  ,					lowercase		)


UpperCAmelCase_			    =							{
    '''blocks''': '''layers''',
    '''mlp.0''': '''fc1''',
    '''mlp.2''': '''fc2''',
    '''mlp_ln''': '''final_layer_norm''',
    '''.attn.query''': '''.self_attn.q_proj''',
    '''.attn.key''': '''.self_attn.k_proj''',
    '''.attn.value''': '''.self_attn.v_proj''',
    '''.attn_ln''': '''.self_attn_layer_norm''',
    '''.attn.out''': '''.self_attn.out_proj''',
    '''.cross_attn.query''': '''.encoder_attn.q_proj''',
    '''.cross_attn.key''': '''.encoder_attn.k_proj''',
    '''.cross_attn.value''': '''.encoder_attn.v_proj''',
    '''.cross_attn_ln''': '''.encoder_attn_layer_norm''',
    '''.cross_attn.out''': '''.encoder_attn.out_proj''',
    '''decoder.ln.''': '''decoder.layer_norm.''',
    '''encoder.ln.''': '''encoder.layer_norm.''',
    '''token_embedding''': '''embed_tokens''',
    '''encoder.positional_embedding''': '''encoder.embed_positions.weight''',
    '''decoder.positional_embedding''': '''decoder.embed_positions.weight''',
    '''ln_post''': '''layer_norm''',
}




def       lowerCAmelCase_     (		lowercase:							Union[str, Any]		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase:     Union[str, Any]							= list(s_dict.keys()		)
 for key in keys:
  _UpperCamelCase:     List[str]							= key
  for k, v in WHISPER_MAPPING.items():
   if k in key:
    _UpperCamelCase:     Tuple							= new_key.replace(lowercase  ,					lowercase		)

  print(F"""{key} -> {new_key}"""		)

  _UpperCamelCase:     Tuple							= s_dict.pop(lowercase		)
 return s_dict




def       lowerCAmelCase_     (		lowercase:							Dict		)    ->				List[Any]:







 '''simple docstring'''
 _UpperCamelCase	,							_UpperCamelCase:     Optional[Any]							= emb.weight.shape
 _UpperCamelCase:     str							= nn.Linear(lowercase  ,					lowercase  ,					bias=lowercase		)
 _UpperCamelCase:     Optional[Any]							= emb.weight.data
 return lin_layer




def       lowerCAmelCase_     (		lowercase:							str  ,					lowercase:							str		)    ->				bytes:







 '''simple docstring'''
 os.makedirs(lowercase  ,					exist_ok=lowercase		)
 _UpperCamelCase:     List[str]							= os.path.basename(lowercase		)

 _UpperCamelCase:     Any							= url.split('''/'''		)[-2]
 _UpperCamelCase:     Dict							= os.path.join(lowercase  ,					lowercase		)

 if os.path.exists(lowercase		) and not os.path.isfile(lowercase		):
  raise RuntimeError(F"""{download_target} exists and is not a regular file"""		)

 if os.path.isfile(lowercase		):
  _UpperCamelCase:     List[str]							= open(lowercase  ,					'''rb'''		).read()
  if hashlib.shaaaa(lowercase		).hexdigest() == expected_shaaaa:
   return model_bytes
  else:
   warnings.warn(F"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"""		)

 with urllib.request.urlopen(lowercase		) as source, open(lowercase  ,					'''wb'''		) as output:
  with tqdm(
      total=int(source.info().get('''Content-Length'''		)		)  ,					ncols=80  ,					unit='''iB'''  ,					unit_scale=lowercase  ,					unit_divisor=1_024		) as loop:
   while True:
    _UpperCamelCase:     Optional[int]							= source.read(8_192		)
    if not buffer:
     break

    output.write(lowercase		)
    loop.update(len(lowercase		)		)

 _UpperCamelCase:     List[str]							= open(lowercase  ,					'''rb'''		).read()
 if hashlib.shaaaa(lowercase		).hexdigest() != expected_shaaaa:
  raise RuntimeError(
      '''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.'''		)

 return model_bytes




def       lowerCAmelCase_     (		lowercase:							List[Any]  ,					lowercase:							Optional[Any]		)    ->				int:







 '''simple docstring'''
 if ".pt" not in checkpoint_path:
  _UpperCamelCase:     Optional[int]							= _download(_MODELS[checkpoint_path]		)
 else:
  _UpperCamelCase:     Any							= torch.load(lowercase  ,					map_location='''cpu'''		)
 _UpperCamelCase:     str							= original_checkpoint['''dims''']
 _UpperCamelCase:     Union[str, Any]							= original_checkpoint['''model_state_dict''']
 _UpperCamelCase:     Tuple							= state_dict['''decoder.token_embedding.weight''']
 remove_ignore_keys_(lowercase		)
 rename_keys(lowercase		)
 _UpperCamelCase:     Optional[Any]							= True
 _UpperCamelCase:     int							= state_dict['''decoder.layers.0.fc1.weight'''].shape[0]

 _UpperCamelCase:     List[str]							= WhisperConfig(
     vocab_size=dimensions['''n_vocab''']  ,					encoder_ffn_dim=lowercase  ,					decoder_ffn_dim=lowercase  ,					num_mel_bins=dimensions['''n_mels''']  ,					d_model=dimensions['''n_audio_state''']  ,					max_target_positions=dimensions['''n_text_ctx''']  ,					encoder_layers=dimensions['''n_audio_layer''']  ,					encoder_attention_heads=dimensions['''n_audio_head''']  ,					decoder_layers=dimensions['''n_text_layer''']  ,					decoder_attention_heads=dimensions['''n_text_state''']  ,					max_source_positions=dimensions['''n_audio_ctx''']  ,					)

 _UpperCamelCase:     List[str]							= WhisperForConditionalGeneration(lowercase		)
 _UpperCamelCase	,							_UpperCamelCase:     Any							= model.model.load_state_dict(lowercase  ,					strict=lowercase		)
 if len(lowercase		) > 0 and not set(lowercase		) <= {
     "encoder.embed_positions.weights",
     "decoder.embed_positions.weights",
 }:
  raise ValueError(
      '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
      F""" but all the following weights are missing {missing}"""		)

 if tie_embeds:
  _UpperCamelCase:     Union[str, Any]							= make_linear_from_emb(model.model.decoder.embed_tokens		)
 else:
  _UpperCamelCase:     Union[str, Any]							= proj_out_weights

 model.save_pretrained(lowercase		)


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()
  # # Required parameters
  parser.add_argument('''--checkpoint_path''', type=str, help='''Patht to the downloaded checkpoints''')
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  UpperCAmelCase_			    =							parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

 # directly update weight in diffusers model
 for key in state_dict:
  # it is suggested to print out the key, it usually will be something like below
  # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

  # as we have set the alpha beforehand, so just skip
  if ".alpha" in key or key in visited:
   continue

  if "text" in key:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

  # find the target layer
  _UpperCamelCase:     Union[str, Any]							= layer_infos.pop(0		)
  while len(lowercase		) > -1:
   try:
    _UpperCamelCase:     Union[str, Any]							= curr_layer.__getattr__(lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:     Tuple							= layer_infos.pop(0		)
    elif len(lowercase		) == 0:
     break
   except Exception:
    if len(lowercase		) > 0:
     temp_name += "_" + layer_infos.pop(0		)
    else:
     _UpperCamelCase:     List[Any]							= layer_infos.pop(0		)

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

  # update weight
  if len(state_dict[pair_keys[0]].shape		) == 4:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= state_dict[pair_keys[1]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		).unsqueeze(2		).unsqueeze(3		)
  else:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= state_dict[pair_keys[1]].to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		)

  # update visited list
  for item in pair_keys:
   visited.append(lowercase		)

 return pipeline


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

  parser.add_argument(
      '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
  )
  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument(
      '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
  )
  parser.add_argument(
      '''--lora_prefix_text_encoder''',
      default='''lora_te''',
      type=str,
      help='''The prefix of text encoder weight in safetensors''',
  )
  parser.add_argument('''--alpha''', default=0.7_5, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
  parser.add_argument(
      '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
  )
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

  UpperCAmelCase_			    =							parser.parse_args()

  UpperCAmelCase_			    =							args.base_model_path
  UpperCAmelCase_			    =							args.checkpoint_path
  UpperCAmelCase_			    =							args.dump_path
  UpperCAmelCase_			    =							args.lora_prefix_unet
  UpperCAmelCase_			    =							args.lora_prefix_text_encoder
  UpperCAmelCase_			    =							args.alpha

  UpperCAmelCase_			    =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)