


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__    :Dict       =				{
    '''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 			lowerCAmelCase__					(     a__:						List[Any]			)    ->						List[str]:


							'''simple docstring'''





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


lowerCAmelCase__    :Optional[int]       =				{
    '''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 			lowerCAmelCase__					(     a__:						int			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     list(s_dict.keys()			)
							for key in keys:
														_UpperCAmelCase											=     key
														for k, v in WHISPER_MAPPING.items():
																					if k in key:
																												_UpperCAmelCase											=     new_key.replace(a__     ,    a__			)

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

														_UpperCAmelCase											=     s_dict.pop(a__			)
							return s_dict

def 			lowerCAmelCase__					(     a__:						Optional[int]			)    ->						int:


							'''simple docstring'''





							_UpperCAmelCase						,			_UpperCAmelCase											=     emb.weight.shape
							_UpperCAmelCase											=     nn.Linear(a__     ,    a__     ,    bias=a__			)
							_UpperCAmelCase											=     emb.weight.data
							return lin_layer

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						bytes:


							'''simple docstring'''





							os.makedirs(a__     ,    exist_ok=a__			)
							_UpperCAmelCase											=     os.path.basename(a__			)

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

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

							if os.path.isfile(a__			):
														_UpperCAmelCase											=     open(a__     ,    'rb'			).read()
														if hashlib.shaaaa(a__			).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(a__			) as source, open(a__     ,    'wb'			) as output:
														with tqdm(
														    total=int(source.info().get('Content-Length'			)			)     ,    ncols=8_0     ,    unit='iB'     ,    unit_scale=a__     ,    unit_divisor=1_0_2_4			) as loop:
																					while True:
																												_UpperCAmelCase											=     source.read(8_1_9_2			)
																												if not buffer:
																																			break

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

							_UpperCAmelCase											=     open(a__     ,    'rb'			).read()
							if hashlib.shaaaa(a__			).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 			lowerCAmelCase__					(     a__:						Dict     ,    a__:						Optional[int]			)    ->						List[str]:


							'''simple docstring'''





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

							_UpperCAmelCase											=     WhisperConfig(
							    vocab_size=dimensions['n_vocab']     ,    encoder_ffn_dim=a__     ,    decoder_ffn_dim=a__     ,    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']     ,    )

							_UpperCAmelCase											=     WhisperForConditionalGeneration(a__			)
							_UpperCAmelCase						,			_UpperCAmelCase											=     model.model.load_state_dict(a__     ,    strict=a__			)
							if len(a__			) > 0 and not set(a__			) <= {
							    "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:
														_UpperCAmelCase											=     make_linear_from_emb(model.model.decoder.embed_tokens			)
							else:
														_UpperCAmelCase											=     proj_out_weights

							model.save_pretrained(a__			)


if __name__ == "__main__":
		lowerCAmelCase__    :List[Any]       =				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__    :Optional[Any]       =				parser.parse_args()

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)




def 			lowerCAmelCase__					(     a__:						int     ,    a__:						int			)    ->						int:


							'''simple docstring'''





							return x if y == 0 else greatest_common_divisor(a__     ,    x % y			)

def 			lowerCAmelCase__					(     a__:						int     ,    a__:						int			)    ->						int:


							'''simple docstring'''





							return (x * y) // greatest_common_divisor(a__     ,    a__			)

def 			lowerCAmelCase__					(     a__:						int = 2_0			)    ->						int:


							'''simple docstring'''





							_UpperCAmelCase											=     1
							for i in range(1     ,    n + 1			):
														_UpperCAmelCase											=     lcm(a__     ,    a__			)
							return g


if __name__ == "__main__":
		print(f'''{solution() = }''')
