


"""simple docstring"""






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


_lowerCAmelCase							:    int					   =	{
    '''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_(		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     ["layers", "blocks"]
   for k in ignore_keys:
      state_dict.pop(_lowerCamelCase			,		_lowerCamelCase       )


_lowerCAmelCase							:    int					   =	{
    '''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_(		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase   :				Union[str, Any]							=     list(s_dict.keys()       )
   for key in keys:
      _lowerCamelCase   :				int							=     key
      for k, v in WHISPER_MAPPING.items():
         if k in key:
            _lowerCamelCase   :				List[Any]							=     new_key.replace(_lowerCamelCase			,		_lowerCamelCase       )

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

      _lowerCamelCase   :				Union[str, Any]							=     s_dict.pop(_lowerCamelCase       )
   return s_dict







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Any:
   '''simple docstring'''





   _lowerCamelCase,					_lowerCamelCase   :				Any							=     emb.weight.shape
   _lowerCamelCase   :				Union[str, Any]							=     nn.Linear(_lowerCamelCase			,		_lowerCamelCase			,		bias=_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     emb.weight.data
   return lin_layer







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     bytes:
   '''simple docstring'''





   os.makedirs(_lowerCamelCase			,		exist_ok=_lowerCamelCase       )
   _lowerCamelCase   :				Optional[Any]							=     os.path.basename(_lowerCamelCase       )

   _lowerCamelCase   :				Union[str, Any]							=     url.split("/"       )[-2]
   _lowerCamelCase   :				Union[str, Any]							=     os.path.join(_lowerCamelCase			,		_lowerCamelCase       )

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

   if os.path.isfile(_lowerCamelCase       ):
      _lowerCamelCase   :				List[Any]							=     open(_lowerCamelCase			,		"rb"       ).read()
      if hashlib.shaaaa(_lowerCamelCase       ).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(_lowerCamelCase       ) as source, open(_lowerCamelCase			,		"wb"       ) as output:
      with tqdm(
          total=int(source.info().get("Content-Length"       )       )			,		ncols=80			,		unit="iB"			,		unit_scale=_lowerCamelCase			,		unit_divisor=1024       ) as loop:
         while True:
            _lowerCamelCase   :				str							=     source.read(8192       )
            if not buffer:
               break

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

   _lowerCamelCase   :				Dict							=     open(_lowerCamelCase			,		"rb"       ).read()
   if hashlib.shaaaa(_lowerCamelCase       ).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_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     str:
   '''simple docstring'''





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

   _lowerCamelCase   :				Dict							=     WhisperConfig(
       vocab_size=dimensions["n_vocab"]			,		encoder_ffn_dim=_lowerCamelCase			,		decoder_ffn_dim=_lowerCamelCase			,		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"]			,		)

   _lowerCamelCase   :				List[str]							=     WhisperForConditionalGeneration(_lowerCamelCase       )
   _lowerCamelCase,					_lowerCamelCase   :				int							=     model.model.load_state_dict(_lowerCamelCase			,		strict=_lowerCamelCase       )
   if len(_lowerCamelCase       ) > 0 and not set(_lowerCamelCase       ) <= {
       "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:
      _lowerCamelCase   :				Optional[int]							=     make_linear_from_emb(model.model.decoder.embed_tokens       )
   else:
      _lowerCamelCase   :				Tuple							=     proj_out_weights

   model.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
  _lowerCAmelCase							:    Dict					   =	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.''')
  _lowerCAmelCase							:    int					   =	parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)




"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       SwinConfig(image_size=192							)

     if "base" in model_name:
          _UpperCAmelCase     :      Tuple														=       6
          _UpperCAmelCase     :      Optional[Any]														=       128
          _UpperCAmelCase     :      Dict														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      List[Any]														=       (4, 8, 16, 32)
     elif "large" in model_name:
          _UpperCAmelCase     :      int														=       12
          _UpperCAmelCase     :      Optional[int]														=       192
          _UpperCAmelCase     :      Optional[Any]														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      str														=       (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"							)

     _UpperCAmelCase     :      Optional[int]														=       window_size
     _UpperCAmelCase     :      Optional[int]														=       embed_dim
     _UpperCAmelCase     :      List[Any]														=       depths
     _UpperCAmelCase     :      Any														=       num_heads

     return config

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  str:





     '''simple docstring'''

     if "encoder.mask_token" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("encoder.mask_token"		,    "embeddings.mask_token"							)
     if "encoder.patch_embed.proj" in name:
          _UpperCAmelCase     :      Optional[int]														=       name.replace("encoder.patch_embed.proj"		,    "embeddings.patch_embeddings.projection"							)
     if "encoder.patch_embed.norm" in name:
          _UpperCAmelCase     :      Any														=       name.replace("encoder.patch_embed.norm"		,    "embeddings.norm"							)
     if "attn.proj" in name:
          _UpperCAmelCase     :      int														=       name.replace("attn.proj"		,    "attention.output.dense"							)
     if "attn" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("attn"		,    "attention.self"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "layernorm_before"							)
     if "norm2" in name:
          _UpperCAmelCase     :      int														=       name.replace("norm2"		,    "layernorm_after"							)
     if "mlp.fc1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("mlp.fc1"		,    "intermediate.dense"							)
     if "mlp.fc2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("mlp.fc2"		,    "output.dense"							)

     if name == "encoder.norm.weight":
          _UpperCAmelCase     :      Any														=       "layernorm.weight"
     if name == "encoder.norm.bias":
          _UpperCAmelCase     :      List[Any]														=       "layernorm.bias"

     if "decoder" in name:
          pass
     else:
          _UpperCAmelCase     :      Dict														=       "swin." + name

     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase     :      Dict														=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__							)

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               _UpperCAmelCase     :      int														=       key.split("."							)
               _UpperCAmelCase     :      List[str]														=       int(key_split[2]							)
               _UpperCAmelCase     :      Dict														=       int(key_split[4]							)
               _UpperCAmelCase     :      str														=       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    _UpperCAmelCase     :      Optional[Any]														=       val[:dim, :]
                    _UpperCAmelCase     :      List[Any]														=       val[
                        dim : dim * 2, :
                    ]
                    _UpperCAmelCase     :      Optional[Any]														=       val[-dim:, :]
               else:
                    _UpperCAmelCase     :      Optional[int]														=       val[
                        :dim
                    ]
                    _UpperCAmelCase     :      Dict														=       val[
                        dim : dim * 2
                    ]
                    _UpperCAmelCase     :      Union[str, Any]														=       val[
                        -dim:
                    ]
          else:
               _UpperCAmelCase     :      List[Any]														=       val

     return orig_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["model"]

     _UpperCAmelCase     :      Union[str, Any]														=       get_swin_config(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__							)
     model.eval()

     _UpperCAmelCase     :      Optional[Any]														=       convert_state_dict(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     model.load_state_dict(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase     :      List[Any]														=       ViTImageProcessor(size={"height": 192, "width": 192}							)
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							)
     _UpperCAmelCase     :      str														=       image_processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(**SCREAMING_SNAKE_CASE__							).logits

     print(outputs.keys()							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)

          print(f'Saving image processor to {pytorch_dump_folder_path}'							)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if push_to_hub:
          print(f'Pushing model and image processor for {model_name} to hub'							)
          model.push_to_hub(f'microsoft/{model_name}'							)
          image_processor.push_to_hub(f'microsoft/{model_name}'							)


if __name__ == "__main__":
 _lowerCAmelCase							:   str				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="swin-base-simmim-window6-192",
     type=str,
     choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
     help="Name of the Swin SimMIM model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _lowerCAmelCase							:   str				=				parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
