
'''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


UpperCAmelCase_       :   Dict   =       {
    '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   :    Optional[Any]							):



      """simple docstring"""




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


UpperCAmelCase_       :   Tuple   =       {
    '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   :    Dict							):



      """simple docstring"""




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

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

            _lowerCamelCase				:   str								=   s_dict.pop(_lowerCAmelCase							)
      return s_dict







def 		A_       (	_lowerCAmelCase   :    List[str]							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      os.makedirs(_lowerCAmelCase				,	exist_ok=_lowerCAmelCase							)
      _lowerCamelCase				:   str								=   os.path.basename(_lowerCAmelCase							)

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

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

      _lowerCamelCase				:   Union[str, 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   :    Union[str, Any]				,	_lowerCAmelCase   :    int							):



      """simple docstring"""




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

      _lowerCamelCase				:   List[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				:   List[str]								=   WhisperForConditionalGeneration(_lowerCAmelCase							)
      _lowerCamelCase				:   Any								=   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				:   Dict								=   make_linear_from_emb(model.model.decoder.embed_tokens							)
      else:
            _lowerCamelCase				:   int								=   proj_out_weights

      model.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
 UpperCAmelCase_       :   List[Any]   =       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_       :   Any   =       parser.parse_args()

 convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCAmelCase_       :   Tuple   =       {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
 from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
 import sys

 UpperCAmelCase_       :   Tuple   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)