


"""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_       							=    {
    "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__ (				__lowercase   )	->	Dict:
				_A					  =			["layers", "blocks"]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )


a_       							=    {
    "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__ (				__lowercase   )	->	Optional[Any]:
				_A					  =			list(s_dict.keys()   )
				for key in keys:
								_A					  =			key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																_A					  =			new_key.replace(__lowercase     ,			__lowercase   )

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

								_A					  =			s_dict.pop(__lowercase   )
				return s_dict






def      a__ (				__lowercase   )	->	int:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase   )	->	bytes:
				os.makedirs(__lowercase     ,			exist_ok=__lowercase   )
				_A					  =			os.path.basename(__lowercase   )

				_A					  =			url.split("/"   )[-2]
				_A					  =			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					  =			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					  =			source.read(8192   )
																if not buffer:
																				break

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

				_A					  =			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      a__ (				__lowercase     ,			__lowercase   )	->	int:
				if ".pt" not in checkpoint_path:
								_A					  =			_download(_MODELS[checkpoint_path]   )
				else:
								_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )
				_A					  =			original_checkpoint["dims"]
				_A					  =			original_checkpoint["model_state_dict"]
				_A					  =			state_dict["decoder.token_embedding.weight"]
				remove_ignore_keys_(__lowercase   )
				rename_keys(__lowercase   )
				_A					  =			True
				_A					  =			state_dict["decoder.layers.0.fc1.weight"].shape[0]

				_A					  =			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					  =			WhisperForConditionalGeneration(__lowercase   )
				_A  ,		_A					  =			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					  =			make_linear_from_emb(model.model.decoder.embed_tokens   )
				else:
								_A					  =			proj_out_weights

				model.save_pretrained(__lowercase   )


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

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)



"""simple docstring"""


import numpy as np






def      a__ (				__lowercase     ,			__lowercase   )	->	np.ndarray:
				return np.where(vector > 0     ,			__lowercase     ,			(alpha * (np.exp(__lowercase   ) - 1))   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()