


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


snake_case							: Union[str, Any]      =   {
    '''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 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]     ):
							a__    =  ['layers', 'blocks']
							for k in ignore_keys:
														state_dict.pop(__lowerCAmelCase      ,					__lowerCAmelCase     )


snake_case							: Tuple      =   {
    '''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 							__lowercase     (				__lowerCAmelCase							:       Dict     ):
							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(__lowerCAmelCase      ,					__lowerCAmelCase     )

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

														a__    =  s_dict.pop(__lowerCAmelCase     )
							return s_dict






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							a__   ,      a__    =  emb.weight.shape
							a__    =  nn.Linear(__lowerCAmelCase      ,					__lowerCAmelCase      ,					bias=__lowerCAmelCase     )
							a__    =  emb.weight.data
							return lin_layer






def 							__lowercase     (				__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       str     ):
							os.makedirs(__lowerCAmelCase      ,					exist_ok=__lowerCAmelCase     )
							a__    =  os.path.basename(__lowerCAmelCase     )

							a__    =  url.split('/'     )[-2]
							a__    =  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     ):
														a__    =  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=8_0      ,					unit='iB'      ,					unit_scale=__lowerCAmelCase      ,					unit_divisor=1_0_2_4     ) as loop:
																					while True:
																												a__    =  source.read(8_1_9_2     )
																												if not buffer:
																																			break

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

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

							a__    =  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']      ,					)

							a__    =  WhisperForConditionalGeneration(__lowerCAmelCase     )
							a__   ,      a__    =  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:
														a__    =  make_linear_from_emb(model.model.decoder.embed_tokens     )
							else:
														a__    =  proj_out_weights

							model.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
						snake_case							: Optional[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.''')
						snake_case							: Optional[Any]      =   parser.parse_args()

						convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if length <= 0 or not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise ValueError('Length must be a positive integer.'     )
							return [n * (2 * n - 1) for n in range(__lowerCAmelCase     )]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))




