



"""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_   						=   {
    '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      __UpperCAmelCase      (    __lowerCamelCase	)							->      List[Any]:
		lowercase__   :    Dict				=			['''layers''', '''blocks''']
		for k in ignore_keys:
				state_dict.pop(__lowerCamelCase						,     __lowerCamelCase	)


lowerCAmelCase_   						=   {
    '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      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    List[str]				=			list(s_dict.keys()	)
		for key in keys:
				lowercase__   :    Any				=			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__   :    Dict				=			s_dict.pop(__lowerCamelCase	)
		return s_dict



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Union[str, Any]:
		lowercase__		,			lowercase__   :    Tuple				=			emb.weight.shape
		lowercase__   :    str				=			nn.Linear(__lowerCamelCase						,     __lowerCamelCase						,     bias=__lowerCamelCase	)
		lowercase__   :    List[str]				=			emb.weight.data
		return lin_layer



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      bytes:
		os.makedirs(__lowerCamelCase						,     exist_ok=__lowerCamelCase	)
		lowercase__   :    Optional[int]				=			os.path.basename(__lowerCamelCase	)

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

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

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

		lowercase__   :    Optional[int]				=			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__   :    Any				=			WhisperForConditionalGeneration(__lowerCamelCase	)
		lowercase__		,			lowercase__   :    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__   :    int				=			make_linear_from_emb(model.model.decoder.embed_tokens	)
		else:
				lowercase__   :    Optional[Any]				=			proj_out_weights

		model.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   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_   						=   parser.parse_args()

							convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""







from ..utils import DummyObject, requires_backends
class        __A       (	metaclass=A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				["torch", "torchsde"]







		def __init__(	self						:  Tuple			,*_snake_case						:  Union[str, Any]			,**_snake_case						:  Any						)					->		Union[str, Any]:






				"""simple docstring"""



				requires_backends(self			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[str]			,*_snake_case						:  int			,**_snake_case						:  Union[str, Any]						)					->		str:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)







		@classmethod
		def 					UpperCAmelCase    (	cls						:  List[Any]			,*_snake_case						:  List[Any]			,**_snake_case						:  List[str]						)					->		List[Any]:






				"""simple docstring"""



				requires_backends(cls			,['''torch''', '''torchsde''']						)
