'''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					={
    "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					(				UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    ["layers", "blocks"]
    for k in ignore_keys:
        state_dict.pop(__lowerCamelCase							,    __lowerCamelCase							)


__UpperCAmelCase					={
    "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					(				UpperCamelCase__							)      ->       List[str]:
    __lowerCamelCase            =    list(s_dict.keys()							)
    for key in keys:
        __lowerCamelCase            =    key
        for k, v in WHISPER_MAPPING.items():
            if k in key:
                __lowerCamelCase            =    new_key.replace(__lowerCamelCase							,    __lowerCamelCase							)

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

        __lowerCamelCase            =    s_dict.pop(__lowerCamelCase							)
    return s_dict






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Tuple:
    __lowerCamelCase            =    emb.weight.shape
    __lowerCamelCase            =    nn.Linear(__lowerCamelCase							,    __lowerCamelCase							,    bias=__lowerCamelCase							)
    __lowerCamelCase            =    emb.weight.data
    return lin_layer






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       List[str]:
    os.makedirs(__lowerCamelCase							,    exist_ok=__lowerCamelCase							)
    __lowerCamelCase            =    os.path.basename(__lowerCamelCase							)

    __lowerCamelCase            =    url.split('''/'''							)[-2]
    __lowerCamelCase            =    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            =    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=10_24							) as loop:
            while True:
                __lowerCamelCase            =    source.read(81_92							)
                if not buffer:
                    break

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

    __lowerCamelCase            =    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 __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    if ".pt" not in checkpoint_path:
        __lowerCamelCase            =    _download(_MODELS[checkpoint_path]							)
    else:
        __lowerCamelCase            =    torch.load(__lowerCamelCase							,    map_location='''cpu'''							)
    __lowerCamelCase            =    original_checkpoint["dims"]
    __lowerCamelCase            =    original_checkpoint["model_state_dict"]
    __lowerCamelCase            =    state_dict["decoder.token_embedding.weight"]
    remove_ignore_keys_(__lowerCamelCase							)
    rename_keys(__lowerCamelCase							)
    __lowerCamelCase            =    True
    __lowerCamelCase            =    state_dict["decoder.layers.0.fc1.weight"].shape[0]

    __lowerCamelCase            =    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            =    WhisperForConditionalGeneration(__lowerCamelCase							)
    __lowerCamelCase            =    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            =    make_linear_from_emb(model.model.decoder.embed_tokens							)
    else:
        __lowerCamelCase            =    proj_out_weights

    model.save_pretrained(__lowerCamelCase							)


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

  convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)


__lowerCamelCase				=			"""Tobias Carryer"""

from time import time






class     UpperCAmelCase						:



   def __init__(self						:   Dict       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   str       ,		snake_case__						:   Optional[int]=int(time()					)					)						->      List[Any]:  # noqa: B008


          '''simple docstring'''


          snake_case			:  Dict     							= multiplier
          snake_case			:  Dict     							= increment
          snake_case			:  Union[str, Any]     							= modulo
          snake_case			:  Tuple     							= seed



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Optional[int]     							= (self.multiplier * self.seed + self.increment) % self.modulo
          return self.seed


if __name__ == "__main__":
   # Show the LCG in action.
   __lowerCamelCase				=			LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
   while True:
      print(lcg.next_number())





