




def        __UpperCAmelCase    (       UpperCAmelCase = 1000       )->       int:



			"""simple docstring"""







			lowercase							=		2**power
			lowercase							=		str(_lowerCamelCase       )
			lowercase							=		list(_lowerCamelCase       )
			lowercase							=		0

			for i in list_num:
						sum_of_num += int(_lowerCamelCase       )

			return sum_of_num


if __name__ == "__main__":
						A_      =      int(input("Enter the power of 2: ").strip())
						print("2 ^ ", power, " = ", 2**power)
						A_      =      solution(power)
						print("Sum of the digits is: ", result)






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        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







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


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        __UpperCAmelCase    (       UpperCAmelCase       )->       List[str]:



			"""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(UpperCAmelCase,		UpperCAmelCase       )

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

						lowercase							=		s_dict.pop(UpperCAmelCase       )
			return s_dict






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase		,lowercase							=		emb.weight.shape
			lowercase							=		nn.Linear(UpperCAmelCase,		UpperCAmelCase,		bias=UpperCAmelCase       )
			lowercase							=		emb.weight.data
			return lin_layer






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       bytes:



			"""simple docstring"""







			os.makedirs(UpperCAmelCase,		exist_ok=UpperCAmelCase       )
			lowercase							=		os.path.basename(UpperCAmelCase       )

			lowercase							=		url.split('''/'''       )[-2]
			lowercase							=		os.path.join(UpperCAmelCase,		UpperCAmelCase       )

			if os.path.exists(UpperCAmelCase       ) and not os.path.isfile(UpperCAmelCase       ):
						raise RuntimeError(f'{download_target} exists and is not a regular file'       )

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

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

			lowercase							=		open(UpperCAmelCase,		'''rb'''       ).read()
			if hashlib.shaaaa(UpperCAmelCase       ).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    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



			"""simple docstring"""







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

			lowercase							=		WhisperConfig(
			    vocab_size=dimensions['''n_vocab'''],		encoder_ffn_dim=UpperCAmelCase,		decoder_ffn_dim=UpperCAmelCase,		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(UpperCAmelCase       )
			lowercase		,lowercase							=		model.model.load_state_dict(UpperCAmelCase,		strict=UpperCAmelCase       )
			if len(UpperCAmelCase       ) > 0 and not set(UpperCAmelCase       ) <= {
			    "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(UpperCAmelCase       )


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)





