
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					   ={
    """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							):
 lowerCamelCase		:	str							=      ["""layers""", """blocks"""]
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase,	lowerCamelCase							)


_lowerCamelCase					   ={
    """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							):
 lowerCamelCase		:	Union[str, Any]							=      list(s_dict.keys()							)
 for key in keys:
  lowerCamelCase		:	List[Any]							=      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		:	Tuple							=      s_dict.pop(lowerCamelCase							)
 return s_dict





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





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 os.makedirs(lowerCamelCase,	exist_ok=lowerCamelCase							)
 lowerCamelCase		:	List[Any]							=      os.path.basename(lowerCamelCase							)

 lowerCamelCase		:	int							=      url.split("""/"""							)[-2]
 lowerCamelCase		:	int							=      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		:	Optional[int]							=      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		:	List[Any]							=      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:
  lowerCamelCase		:	str							=      _download(_MODELS[checkpoint_path]							)
 else:
  lowerCamelCase		:	Dict							=      torch.load(lowerCamelCase,	map_location="""cpu"""							)
 lowerCamelCase		:	int							=      original_checkpoint["""dims"""]
 lowerCamelCase		:	Union[str, Any]							=      original_checkpoint["""model_state_dict"""]
 lowerCamelCase		:	Union[str, Any]							=      state_dict["""decoder.token_embedding.weight"""]
 remove_ignore_keys_(lowerCamelCase							)
 rename_keys(lowerCamelCase							)
 lowerCamelCase		:	str							=      True
 lowerCamelCase		:	str							=      state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

 lowerCamelCase		:	Optional[Any]							=      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		:	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		:	List[Any]							=      make_linear_from_emb(model.model.decoder.embed_tokens							)
 else:
  lowerCamelCase		:	Optional[int]							=      proj_out_weights

 model.save_pretrained(lowerCamelCase							)


if __name__ == "__main__":
      _lowerCamelCase					   =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					   =parser.parse_args()

      convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)






_lowerCamelCase					   ={
    "joule": 1.0,
    "kilojoule": 1_0_0_0,
    "megajoule": 1_0_0_0_0_0_0,
    "gigajoule": 1_0_0_0_0_0_0_0_0_0,
    "wattsecond": 1.0,
    "watthour": 3_6_0_0,
    "kilowatthour": 3_6_0_0_0_0_0,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_1_8_6.8,
    "kilocalorie_nutr": 4_1_8_6_8_0_0.0_0,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4E-1_9,
    "britishthermalunit_it": 1_0_5_5.0_5_5_8_5,
    "footpound": 1.355818,
}





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
  lowerCamelCase		:	Dict							=      (
      F'''Incorrect \'from_type\' or \'to_type\' value: {from_type!r}, {to_type!r}\n'''
      F'''Valid values are: {", ".join(lowerCamelCase							)}'''
  )
  raise ValueError(lowerCamelCase							)
 return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
      import doctest

      doctest.testmod()




