
'''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    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]):
				lowerCamelCase					:			Union[str, Any]									= ['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__							:							Union[str, Any]):
				lowerCamelCase					:			Optional[Any]									= list(s_dict.keys())
				for key in keys:
								lowerCamelCase					:			int									= key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																lowerCamelCase					:			Optional[Any]									= new_key.replace(UpperCAmelCase__       ,							UpperCAmelCase__)

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

								lowerCamelCase					:			Tuple									= s_dict.pop(UpperCAmelCase__)
				return s_dict





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[Any]):
				lowerCamelCase			, lowerCamelCase					:			Union[str, Any]									= emb.weight.shape
				lowerCamelCase					:			str									= nn.Linear(UpperCAmelCase__       ,							UpperCAmelCase__       ,							bias=UpperCAmelCase__)
				lowerCamelCase					:			Dict									= emb.weight.data
				return lin_layer





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							str):
				os.makedirs(UpperCAmelCase__       ,							exist_ok=UpperCAmelCase__)
				lowerCamelCase					:			Any									= os.path.basename(UpperCAmelCase__)

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

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

				lowerCamelCase					:			Union[str, Any]									= 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__							:							Any       ,							UpperCAmelCase__							:							List[str]):
				if ".pt" not in checkpoint_path:
								lowerCamelCase					:			List[str]									= _download(_MODELS[checkpoint_path])
				else:
								lowerCamelCase					:			int									= torch.load(UpperCAmelCase__       ,							map_location='cpu')
				lowerCamelCase					:			Tuple									= original_checkpoint['dims']
				lowerCamelCase					:			Union[str, Any]									= original_checkpoint['model_state_dict']
				lowerCamelCase					:			Tuple									= state_dict['decoder.token_embedding.weight']
				remove_ignore_keys_(UpperCAmelCase__)
				rename_keys(UpperCAmelCase__)
				lowerCamelCase					:			Any									= True
				lowerCamelCase					:			str									= state_dict['decoder.layers.0.fc1.weight'].shape[0]

				lowerCamelCase					:			Optional[Any]									= 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']       ,							)

				lowerCamelCase					:			Any									= WhisperForConditionalGeneration(UpperCAmelCase__)
				lowerCamelCase			, lowerCamelCase					:			Any									= 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:
								lowerCamelCase					:			Any									= make_linear_from_emb(model.model.decoder.embed_tokens)
				else:
								lowerCamelCase					:			List[str]									= 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)


'''simple docstring'''






from __future__ import annotations

A			      =      '#'




class     __snake_case		:
					def __init__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			dict									= {}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self._trie
									for char in text:
													if char not in trie:
																	lowerCamelCase					:			Dict									= {}
													lowerCamelCase					:			Optional[int]									= trie[char]
									lowerCamelCase					:			Optional[Any]									= True
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self._trie
									for char in prefix:
													if char in trie:
																	lowerCamelCase					:			int									= trie[char]
													else:
																	return []
									return self._elements(A      )




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= []
									for c, v in d.items():
													lowerCamelCase					:			Optional[Any]									= [' '] if c == END else [(c + s) for s in self._elements(A      )]
													result.extend(A      )
									return tuple(A      )





A			      =      Trie()
A			      =      ('depart', 'detergent', 'daring', 'dog', 'deer', 'deal')
for word in words:
					trie.insert_word(word)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			Any									= trie.find_word(UpperCAmelCase__)
				return tuple(string + word for word in suffixes)





def    UpperCAmelCase							(  ):
				print(autocomplete_using_trie('de'))


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()
