"""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     :		int											=					{
    "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						:				Union[str, Any]  ):





       '''simple docstring'''

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


_UpperCamelCase     :		Union[str, Any]											=					{
    "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						:				int  ):





       '''simple docstring'''

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

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

              lowercase__    :	int     					=			s_dict.pop(_lowerCAmelCase  )
       return s_dict





def        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

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





def        a_  (   _lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				str  ):





       '''simple docstring'''

       os.makedirs(_lowerCAmelCase     ,				exist_ok=_lowerCAmelCase  )
       lowercase__    :	Any     					=			os.path.basename(_lowerCAmelCase  )

       lowercase__    :	Optional[int]     					=			url.split('/'  )[-2]
       lowercase__    :	Union[str, Any]     					=			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  ):
              lowercase__    :	Optional[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:
                            lowercase__    :	List[str]     					=			source.read(8192  )
                            if not buffer:
                                   break

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

       lowercase__    :	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						:				Optional[int]  ):





       '''simple docstring'''

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

       lowercase__    :	str     					=			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']     ,				)

       lowercase__    :	Optional[Any]     					=			WhisperForConditionalGeneration(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	List[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:
              lowercase__    :	Union[str, Any]     					=			make_linear_from_emb(model.model.decoder.embed_tokens  )
       else:
              lowercase__    :	List[str]     					=			proj_out_weights

       model.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
       _UpperCamelCase     :		int											=					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     :		List[Any]											=					parser.parse_args()

       convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)


"""simple docstring"""





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			[0] * len(_lowerCAmelCase  )

       for i in range(1     ,				len(_lowerCAmelCase  )  ):
              # use last results for better performance - dynamic programming
              lowercase__    :	List[str]     					=			prefix_result[i - 1]
              while j > 0 and input_string[i] != input_string[j]:
                     lowercase__    :	Dict     					=			prefix_result[j - 1]

              if input_string[i] == input_string[j]:
                     j += 1
              lowercase__    :	Union[str, Any]     					=			j

       return prefix_result





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       return max(prefix_function(_lowerCAmelCase  )  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

