

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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__     )    ->	Tuple:
	__lowerCamelCase							:							int   			=					["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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__     )    ->	int:
	__lowerCamelCase							:							List[Any]   			=					list(s_dict.keys()     )
	for key in keys:
		__lowerCamelCase							:							Optional[int]   			=					key
		for k, v in WHISPER_MAPPING.items():
			if k in key:
				__lowerCamelCase							:							str   			=					new_key.replace(_lowerCAmelCase      ,					_lowerCAmelCase     )

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

		__lowerCamelCase							:							str   			=					s_dict.pop(_lowerCAmelCase     )
	return s_dict






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__     )    ->	Dict:
	__lowerCamelCase							:							List[str]   			=					emb.weight.shape
	__lowerCamelCase							:							Dict   			=					nn.Linear(_lowerCAmelCase      ,					_lowerCAmelCase      ,					bias=_lowerCAmelCase     )
	__lowerCamelCase							:							List[Any]   			=					emb.weight.data
	return lin_layer






def   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__     )    ->	bytes:
	os.makedirs(_lowerCAmelCase      ,					exist_ok=_lowerCAmelCase     )
	__lowerCamelCase							:							List[str]   			=					os.path.basename(_lowerCAmelCase     )

	__lowerCamelCase							:							int   			=					url.split('/'     )[-2]
	__lowerCamelCase							:							List[str]   			=					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     ):
		__lowerCamelCase							:							Any   			=					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:
				__lowerCamelCase							:							List[str]   			=					source.read(8_1_9_2     )
				if not buffer:
					break

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

	__lowerCamelCase							:							Optional[int]   			=					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   SCREAMING_SNAKE_CASE__			(    lowerCamelCase__      ,					lowerCamelCase__     )    ->	Optional[int]:
	if ".pt" not in checkpoint_path:
		__lowerCamelCase							:							Any   			=					_download(_MODELS[checkpoint_path]     )
	else:
		__lowerCamelCase							:							Tuple   			=					torch.load(_lowerCAmelCase      ,					map_location='cpu'     )
	__lowerCamelCase							:							Any   			=					original_checkpoint["dims"]
	__lowerCamelCase							:							Optional[Any]   			=					original_checkpoint["model_state_dict"]
	__lowerCamelCase							:							Any   			=					state_dict["decoder.token_embedding.weight"]
	remove_ignore_keys_(_lowerCAmelCase     )
	rename_keys(_lowerCAmelCase     )
	__lowerCamelCase							:							Tuple   			=					True
	__lowerCamelCase							:							Optional[int]   			=					state_dict["decoder.layers.0.fc1.weight"].shape[0]

	__lowerCamelCase							:							Optional[int]   			=					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']      ,					)

	__lowerCamelCase							:							Optional[Any]   			=					WhisperForConditionalGeneration(_lowerCAmelCase     )
	__lowerCamelCase							:							Dict   			=					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:
		__lowerCamelCase							:							Optional[Any]   			=					make_linear_from_emb(model.model.decoder.embed_tokens     )
	else:
		__lowerCamelCase							:							Optional[Any]   			=					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)











__lowerCamelCase			:	Any					    =	9.8_0_6_6_5



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = g	)		->					float:

 if fluid_density <= 0:
  raise ValueError("Impossible fluid density"	)
 if volume < 0:
  raise ValueError("Impossible Object volume"	)
 if gravity <= 0:
  raise ValueError("Impossible Gravity"	)

 return fluid_density * gravity * volume


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()
