


'''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


lowerCamelCase				:  Union[str, Any]    		=      {
    '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 _SCREAMING_SNAKE_CASE (A			)			->			Optional[Any]:






       """simple docstring"""







       lowercase__      =					['''layers''', '''blocks''']
       for k in ignore_keys:
              state_dict.pop(A    ,					A			)


lowerCamelCase				:  List[str]    		=      {
    '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 _SCREAMING_SNAKE_CASE (A			)			->			List[Any]:






       """simple docstring"""







       lowercase__      =					list(s_dict.keys()			)
       for key in keys:
              lowercase__      =					key
              for k, v in WHISPER_MAPPING.items():
                     if k in key:
                            lowercase__      =					new_key.replace(A    ,					A			)

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

              lowercase__      =					s_dict.pop(A			)
       return s_dict

def _SCREAMING_SNAKE_CASE (A			)			->			Optional[Any]:






       """simple docstring"""







       lowercase__     ,lowercase__      =					emb.weight.shape
       lowercase__      =					nn.Linear(A    ,					A    ,					bias=A			)
       lowercase__      =					emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			bytes:






       """simple docstring"""







       os.makedirs(A    ,					exist_ok=A			)
       lowercase__      =					os.path.basename(A			)

       lowercase__      =					url.split('''/'''			)[-2]
       lowercase__      =					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			):
              lowercase__      =					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=1_024			) as loop:
                     while True:
                            lowercase__      =					source.read(8_192			)
                            if not buffer:
                                   break

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

       lowercase__      =					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 _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Any:






       """simple docstring"""







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

       lowercase__      =					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''']    ,					)

       lowercase__      =					WhisperForConditionalGeneration(A			)
       lowercase__     ,lowercase__      =					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:
              lowercase__      =					make_linear_from_emb(model.model.decoder.embed_tokens			)
       else:
              lowercase__      =					proj_out_weights

       model.save_pretrained(A			)


if __name__ == "__main__":
 lowerCamelCase				:  Dict    		=      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				:  Tuple    		=      parser.parse_args()

 convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A			)			->			bool:






       """simple docstring"""







       lowercase__      =					[int(A			) for i in ip_va_address.split('''.'''			) if i.isdigit()]
       return len(A			) == 4 and all(0 <= int(A			) <= 254 for octet in octets			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[Any]    		=      input().strip()
 lowerCamelCase				:  Union[str, Any]    		=      'valid' if is_ip_va_address_valid(ip) else 'invalid'
 print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




