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 	lowerCamelCase					(	UpperCamelCase     :				Tuple							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  ["""layers""", """blocks"""]
		for k in ignore_keys:
				state_dict.pop(_A			,       _A							)


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 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    Dict:
		_lowerCamelCase				   =  list(s_dict.keys()							)
		for key in keys:
				_lowerCamelCase				   =  key
				for k, v in WHISPER_MAPPING.items():
						if k in key:
								_lowerCamelCase				   =  new_key.replace(_A			,       _A							)

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

				_lowerCamelCase				   =  s_dict.pop(_A							)
		return s_dict


def 	lowerCamelCase					(	UpperCamelCase     :				Union[str, Any]							)   ->    Optional[int]:
		_lowerCamelCase				   =  emb.weight.shape
		_lowerCamelCase				   =  nn.Linear(_A			,       _A			,       bias=_A							)
		_lowerCamelCase				   =  emb.weight.data
		return lin_layer


def 	lowerCamelCase					(	UpperCamelCase     :				str			,       UpperCamelCase     :				int							)   ->    bytes:
		os.makedirs(_A			,       exist_ok=_A							)
		_lowerCamelCase				   =  os.path.basename(_A							)

		_lowerCamelCase				   =  url.split('/'							)[-2]
		_lowerCamelCase				   =  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							):
				_lowerCamelCase				   =  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=80			,       unit='iB'			,       unit_scale=_A			,       unit_divisor=10_24							) as loop:
						while True:
								_lowerCamelCase				   =  source.read(81_92							)
								if not buffer:
										break

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

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

		_lowerCamelCase				   =  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']			,       )

		_lowerCamelCase				   =  WhisperForConditionalGeneration(_A							)
		_lowerCamelCase				   =  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:
				_lowerCamelCase				   =  make_linear_from_emb(model.model.decoder.embed_tokens							)
		else:
				_lowerCamelCase				   =  proj_out_weights

		model.save_pretrained(_A							)


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"""







import requests
from bsa import BeautifulSoup


def      UpperCamelCase       ( _A = "AAPL" )     ->       str:
				lowercase    :      Optional[Any]       = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
				lowercase    :      str       = BeautifulSoup(requests.get(_A ).text  ,				"""html.parser""" )
				lowercase    :      int       = """My(6px) Pos(r) smartphone_Mt(6px)"""
				return soup.find("""div"""  ,				class_=class_ ).find("""span""" ).text


if __name__ == "__main__":
				for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
								print(F'Current {symbol:<4} stock price is {stock_price(symbol):>8}')
