



from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCamelCase__	=       {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}


if TYPE_CHECKING:
						from .tokenization_bertweet import BertweetTokenizer

else:
						import sys

						lowerCamelCase__	=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







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_    =				{
    '''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 )		->						List[Any]:






				"""simple docstring"""


				snake_case_						:    List[str]				=  ['''layers''', '''blocks''']
				for k in ignore_keys:
								state_dict.pop(_UpperCamelCase ,				_UpperCamelCase )


lowerCAmelCase_    =				{
    '''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 )		->						int:






				"""simple docstring"""


				snake_case_						:    str				=  list(s_dict.keys() )
				for key in keys:
								snake_case_						:    Optional[int]				=  key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																snake_case_						:    List[str]				=  new_key.replace(_UpperCamelCase ,				_UpperCamelCase )

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

								snake_case_						:    Tuple				=  s_dict.pop(_UpperCamelCase )
				return s_dict






def      lowerCamelCase_						(				_UpperCamelCase )		->						int:






				"""simple docstring"""


				snake_case_			,							snake_case_						:    Dict				=  emb.weight.shape
				snake_case_						:    Tuple				=  nn.Linear(_UpperCamelCase ,				_UpperCamelCase ,				bias=_UpperCamelCase )
				snake_case_						:    Any				=  emb.weight.data
				return lin_layer






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						bytes:






				"""simple docstring"""


				os.makedirs(_UpperCamelCase ,				exist_ok=_UpperCamelCase )
				snake_case_						:    List[Any]				=  os.path.basename(_UpperCamelCase )

				snake_case_						:    Any				=  url.split('''/''' )[-2]
				snake_case_						:    str				=  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 ):
								snake_case_						:    Union[str, Any]				=  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=1_024 ) as loop:
												while True:
																snake_case_						:    Dict				=  source.read(8_192 )
																if not buffer:
																				break

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

				snake_case_						:    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      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						int:






				"""simple docstring"""


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

				snake_case_						:    List[str]				=  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'''] ,				)

				snake_case_						:    Union[str, Any]				=  WhisperForConditionalGeneration(_UpperCamelCase )
				snake_case_			,							snake_case_						:    List[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:
								snake_case_						:    List[str]				=  make_linear_from_emb(model.model.decoder.embed_tokens )
				else:
								snake_case_						:    Any				=  proj_out_weights

				model.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
	lowerCAmelCase_    =				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_    =				parser.parse_args()

	convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
