





from graphs.minimum_spanning_tree_kruskal import kruskal






def       _A  (      ):
   a__	:						Optional[Any]      		=  9
   a__	:						Dict      		=  [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   a__	:						List[str]      		=  kruskal(lowerCamelCase							, lowerCamelCase   )

   a__	:						str      		=  [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(lowerCamelCase   ) == sorted(lowerCamelCase   )












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


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{
    """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       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  ["layers", "blocks"]
   for k in ignore_keys:
      state_dict.pop(lowerCamelCase							, lowerCamelCase   )


SCREAMING_SNAKE_CASE__       :			List[str]			      =						{
    """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       _A  (      lowerCamelCase   ):
   a__	:						Tuple      		=  list(s_dict.keys()   )
   for key in keys:
      a__	:						Optional[Any]      		=  key
      for k, v in WHISPER_MAPPING.items():
         if k in key:
            a__	:						Optional[int]      		=  new_key.replace(lowerCamelCase							, lowerCamelCase   )

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

      a__	:						Dict      		=  s_dict.pop(lowerCamelCase   )
   return s_dict






def       _A  (      lowerCamelCase   ):
   a__		,     a__	:						Any      		=  emb.weight.shape
   a__	:						Optional[Any]      		=  nn.Linear(lowerCamelCase							, lowerCamelCase							, bias=lowerCamelCase   )
   a__	:						Optional[Any]      		=  emb.weight.data
   return lin_layer






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   os.makedirs(lowerCamelCase							, exist_ok=lowerCamelCase   )
   a__	:						Optional[Any]      		=  os.path.basename(lowerCamelCase   )

   a__	:						List[Any]      		=  url.split("/"   )[-2]
   a__	:						Tuple      		=  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   ):
      a__	:						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:
            a__	:						Optional[Any]      		=  source.read(8192   )
            if not buffer:
               break

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

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

   a__	:						Tuple      		=  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"]							, )

   a__	:						Optional[Any]      		=  WhisperForConditionalGeneration(lowerCamelCase   )
   a__		,     a__	:						Tuple      		=  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:
      a__	:						Any      		=  make_linear_from_emb(model.model.decoder.embed_tokens   )
   else:
      a__	:						str      		=  proj_out_weights

   model.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			Tuple			      =						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.""")
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						parser.parse_args()

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)

