






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


snake_case    				=		{
    """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				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    ["layers", "blocks"]
  for k in ignore_keys:
    state_dict.pop(__a						,     __a				)


snake_case    				=		{
    """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				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    list(s_dict.keys()				)
  for key in keys:
    SCREAMING_SNAKE_CASE       :      str							=    key
    for k, v in WHISPER_MAPPING.items():
      if k in key:
        SCREAMING_SNAKE_CASE       :      int							=    new_key.replace(__a						,     __a				)

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

    SCREAMING_SNAKE_CASE       :      Tuple							=    s_dict.pop(__a				)
  return s_dict


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Any							=    emb.weight.shape
  SCREAMING_SNAKE_CASE       :      Dict							=    nn.Linear(__a						,     __a						,     bias=__a				)
  SCREAMING_SNAKE_CASE       :      int							=    emb.weight.data
  return lin_layer


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  os.makedirs(__a						,     exist_ok=__a				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    os.path.basename(__a				)

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

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

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

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

  SCREAMING_SNAKE_CASE       :      Tuple							=    open(__a						,     "rb"				).read()
  if hashlib.shaaaa(__a				).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						,     lowercase				):

  """simple docstring"""






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

  SCREAMING_SNAKE_CASE       :      List[Any]							=    WhisperConfig(
      vocab_size=dimensions["n_vocab"]						,     encoder_ffn_dim=__a						,     decoder_ffn_dim=__a						,     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"]						,     )

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

  model.save_pretrained(__a				)


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

   convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)














from ..utils import DummyObject, requires_backends


class  SCREAMING_SNAKE_CASE			(			metaclass=lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Union[str, Any]			=	['''keras_nlp''']


     def __init__(				self       :	Dict					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	Dict      ):
       requires_backends(self					, ["keras_nlp"]      )






