import math



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (
        not isinstance(_a  ,      (int, float)     )
        or power_factor < -1
        or power_factor > 1
    ):
        raise ValueError('''power_factor must be a valid float value between -1 and 1.'''     )
    return apparent_power * power_factor



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if (
        not isinstance(_a  ,      (int, float)     )
        or power_factor < -1
        or power_factor > 1
    ):
        raise ValueError('''power_factor must be a valid float value between -1 and 1.'''     )
    return apparent_power * math.sqrt(1 - power_factor**2     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




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


__lowercase										= {
    '''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  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:int        =		['''layers''', '''blocks''']
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )


__lowercase										= {
    '''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  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:List[str]        =		list(s_dict.keys()     )
    for key in keys:
        __UpperCamelCase					:Dict        =		key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                __UpperCamelCase					:str        =		new_key.replace(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

        __UpperCamelCase					:Any        =		s_dict.pop(SCREAMING_SNAKE_CASE     )
    return s_dict



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    os.makedirs(SCREAMING_SNAKE_CASE  ,      exist_ok=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:int        =		os.path.basename(SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Optional[Any]        =		url.split('''/'''     )[-2]
    __UpperCamelCase					:Tuple        =		os.path.join(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

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

    if os.path.isfile(SCREAMING_SNAKE_CASE     ):
        __UpperCamelCase					:List[str]        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
        if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).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(SCREAMING_SNAKE_CASE     ) as source, open(SCREAMING_SNAKE_CASE  ,      '''wb'''     ) as output:
        with tqdm(
            total=int(source.info().get('''Content-Length'''     )     )  ,      ncols=80  ,      unit='''iB'''  ,      unit_scale=SCREAMING_SNAKE_CASE  ,      unit_divisor=1_024     ) as loop:
            while True:
                __UpperCamelCase					:Optional[Any]        =		source.read(8_192     )
                if not buffer:
                    break

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

    __UpperCamelCase					:str        =		open(SCREAMING_SNAKE_CASE  ,      '''rb'''     ).read()
    if hashlib.shaaaa(SCREAMING_SNAKE_CASE     ).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  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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

    __UpperCamelCase					:Dict        =		WhisperConfig(
        vocab_size=dimensions['''n_vocab''']  ,      encoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      decoder_ffn_dim=SCREAMING_SNAKE_CASE  ,      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''']  ,      )

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

    model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= 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.''')
    __lowercase										= parser.parse_args()

    convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





