





"""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


a						: str     	=			{
    '''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		(	_lowercase						:    Union[str, Any]					) ->Any:






	'''simple docstring'''






	a      :		List[Any]	=				["layers", "blocks"]
	for k in ignore_keys:
		state_dict.pop(_lowercase    ,       _lowercase					)


a						: 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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Dict					) ->int:






	'''simple docstring'''






	a      :		str	=				list(s_dict.keys()					)
	for key in keys:
		a      :		Any	=				key
		for k, v in WHISPER_MAPPING.items():
			if k in key:
				a      :		List[Any]	=				new_key.replace(_lowercase    ,       _lowercase					)

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

		a      :		Union[str, Any]	=				s_dict.pop(_lowercase					)
	return s_dict






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->List[str]:






	'''simple docstring'''






	a,       a      :		int	=				emb.weight.shape
	a      :		Optional[int]	=				nn.Linear(_lowercase    ,       _lowercase    ,       bias=_lowercase					)
	a      :		Any	=				emb.weight.data
	return lin_layer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str					) ->bytes:






	'''simple docstring'''






	os.makedirs(_lowercase    ,       exist_ok=_lowercase					)
	a      :		Union[str, Any]	=				os.path.basename(_lowercase					)

	a      :		List[str]	=				url.split("/"					)[-2]
	a      :		int	=				os.path.join(_lowercase    ,       _lowercase					)

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

	if os.path.isfile(_lowercase					):
		a      :		Optional[int]	=				open(_lowercase    ,       "rb"					).read()
		if hashlib.shaaaa(_lowercase					).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(_lowercase					) as source, open(_lowercase    ,       "wb"					) as output:
		with tqdm(
		    total=int(source.info().get("Content-Length"					)					)    ,       ncols=80    ,       unit="iB"    ,       unit_scale=_lowercase    ,       unit_divisor=1024					) as loop:
			while True:
				a      :		int	=				source.read(8192					)
				if not buffer:
					break

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

	a      :		Optional[Any]	=				open(_lowercase    ,       "rb"					).read()
	if hashlib.shaaaa(_lowercase					).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		(	_lowercase						:    List[str]    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






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

	a      :		Optional[int]	=				WhisperConfig(
	    vocab_size=dimensions["n_vocab"]    ,       encoder_ffn_dim=_lowercase    ,       decoder_ffn_dim=_lowercase    ,       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"]    ,       )

	a      :		str	=				WhisperForConditionalGeneration(_lowercase					)
	a,       a      :		Tuple	=				model.model.load_state_dict(_lowercase    ,       strict=_lowercase					)
	if len(_lowercase					) > 0 and not set(_lowercase					) <= {
	    "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:
		a      :		List[str]	=				make_linear_from_emb(model.model.decoder.embed_tokens					)
	else:
		a      :		Dict	=				proj_out_weights

	model.save_pretrained(_lowercase					)


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

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)







def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  list:



    """simple docstring"""






    return [
        txt[:a] + txt[a].upper() + txt[a + 1 :]
        for a in range(len(_lowerCAmelCase		)		)
        if txt[a].isalpha()
    ]


if __name__ == "__main__":
     __import__("""doctest""").testmod()


