






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    (      __lowerCAmelCase	)->  Optional[Any]:





						"""simple docstring"""



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


_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    (      __lowerCAmelCase	)->  Any:





						"""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(__lowerCAmelCase ,		__lowerCAmelCase	)

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

												_UpperCAmelCase									=			s_dict.pop(__lowerCAmelCase	)
						return s_dict


def __A    (      __lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



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


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  bytes:





						"""simple docstring"""



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

						_UpperCAmelCase									=			url.split('/'	)[-2]
						_UpperCAmelCase									=			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	):
												_UpperCAmelCase									=			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=80 ,		unit='iB' ,		unit_scale=__lowerCAmelCase ,		unit_divisor=1_024	) as loop:
																		while True:
																								_UpperCAmelCase									=			source.read(8_192	)
																								if not buffer:
																														break

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

						_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  Optional[int]:





						"""simple docstring"""



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

						_UpperCAmelCase									=			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'] ,		)

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

						model.save_pretrained(__lowerCAmelCase	)


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)








from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def __A    (      )->  tuple[list[int], int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[randint(-1_000 ,		1_000	) for i in range(10	)]
						_UpperCAmelCase									=			randint(-5_000 ,		5_000	)
						return (arr, r)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						for i in range(n - 1	):
												_UpperCAmelCase					, _UpperCAmelCase									=			i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)


def __A    (      )->  tuple[float, float]:





						"""simple docstring"""



						_UpperCAmelCase									=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						_UpperCAmelCase									=			'\ntriplet_sum1(*dataset)\n'
						_UpperCAmelCase									=			'\ntriplet_sum2(*dataset)\n'
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
