





"""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						: str     	=			{
    '''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		(	_lowercase						:    Union[str, Any]					) ->Any:






	'''simple docstring'''






	a      :		List[Any]	=				["layers", "blocks"]
	for k in ignore_keys:
		state_dict.pop(_lowercase    ,       _lowercase					)


a						: Union[str, Any]     	=			{
    '''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		(	_lowercase						:    Dict					) ->int:






	'''simple docstring'''






	a      :		str	=				list(s_dict.keys()					)
	for key in keys:
		a      :		Any	=				key
		for k, v in WHISPER_MAPPING.items():
			if k in key:
				a      :		List[Any]	=				new_key.replace(_lowercase    ,       _lowercase					)

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

		a      :		Union[str, Any]	=				s_dict.pop(_lowercase					)
	return s_dict






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Union[str, Any]					) ->List[str]:






	'''simple docstring'''






	a,       a      :		int	=				emb.weight.shape
	a      :		Optional[int]	=				nn.Linear(_lowercase    ,       _lowercase    ,       bias=_lowercase					)
	a      :		Any	=				emb.weight.data
	return lin_layer






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str					) ->bytes:






	'''simple docstring'''






	os.makedirs(_lowercase    ,       exist_ok=_lowercase					)
	a      :		Union[str, Any]	=				os.path.basename(_lowercase					)

	a      :		List[str]	=				url.split("/"					)[-2]
	a      :		int	=				os.path.join(_lowercase    ,       _lowercase					)

	if os.path.exists(_lowercase					) and not os.path.isfile(_lowercase					):
		raise RuntimeError(F"""{download_target} exists and is not a regular file"""					)

	if os.path.isfile(_lowercase					):
		a      :		Optional[int]	=				open(_lowercase    ,       "rb"					).read()
		if hashlib.shaaaa(_lowercase					).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(_lowercase					) as source, open(_lowercase    ,       "wb"					) as output:
		with tqdm(
		    total=int(source.info().get("Content-Length"					)					)    ,       ncols=80    ,       unit="iB"    ,       unit_scale=_lowercase    ,       unit_divisor=1024					) as loop:
			while True:
				a      :		int	=				source.read(8192					)
				if not buffer:
					break

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

	a      :		Optional[Any]	=				open(_lowercase    ,       "rb"					).read()
	if hashlib.shaaaa(_lowercase					).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		(	_lowercase						:    List[str]    ,       _lowercase						:    Union[str, Any]					) ->str:






	'''simple docstring'''






	if ".pt" not in checkpoint_path:
		a      :		List[Any]	=				_download(_MODELS[checkpoint_path]					)
	else:
		a      :		Optional[Any]	=				torch.load(_lowercase    ,       map_location="cpu"					)
	a      :		Tuple	=				original_checkpoint["dims"]
	a      :		Dict	=				original_checkpoint["model_state_dict"]
	a      :		Tuple	=				state_dict["decoder.token_embedding.weight"]
	remove_ignore_keys_(_lowercase					)
	rename_keys(_lowercase					)
	a      :		Any	=				True
	a      :		Tuple	=				state_dict["decoder.layers.0.fc1.weight"].shape[0]

	a      :		Optional[int]	=				WhisperConfig(
	    vocab_size=dimensions["n_vocab"]    ,       encoder_ffn_dim=_lowercase    ,       decoder_ffn_dim=_lowercase    ,       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"]    ,       )

	a      :		str	=				WhisperForConditionalGeneration(_lowercase					)
	a,       a      :		Tuple	=				model.model.load_state_dict(_lowercase    ,       strict=_lowercase					)
	if len(_lowercase					) > 0 and not set(_lowercase					) <= {
	    "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:
		a      :		List[str]	=				make_linear_from_emb(model.model.decoder.embed_tokens					)
	else:
		a      :		Dict	=				proj_out_weights

	model.save_pretrained(_lowercase					)


if __name__ == "__main__":
		a						: int     	=			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						: Any     	=			parser.parse_args()

		convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)





import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{'''vocab_file''': '''spiece.model'''}

lowerCAmelCase_       	=	{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase_       	=	{
    '''google/bigbird-roberta-base''': 40_96,
    '''google/bigbird-roberta-large''': 40_96,
    '''google/bigbird-base-trivia-itc''': 40_96,
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   List[Any]									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   List[Any]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[Any]									=				["input_ids", "attention_mask"]
			SCREAMING_SNAKE_CASE     :   List[int]									=				[]







			def __init__(     self :				List[str]							,   _UpperCamelCase :				List[str]							,   _UpperCamelCase :				Dict="<unk>"							,   _UpperCamelCase :				List[str]="<s>"							,   _UpperCamelCase :				Tuple="</s>"							,   _UpperCamelCase :				Any="<pad>"							,   _UpperCamelCase :				Any="[SEP]"							,   _UpperCamelCase :				Optional[Any]="[MASK]"							,   _UpperCamelCase :				Any="[CLS]"							,   _UpperCamelCase :				Optional[Dict[str, Any]] = None							,   **_UpperCamelCase :				Dict							,   )      ->None:
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else bos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else eos_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else unk_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else pad_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else cls_token
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else sep_token

									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_   =	AddedToken(_UpperCamelCase							,   lstrip=_UpperCamelCase							,   rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase							,   _UpperCamelCase							) else mask_token

									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=_UpperCamelCase							,   eos_token=_UpperCamelCase							,   unk_token=_UpperCamelCase							,   pad_token=_UpperCamelCase							,   sep_token=_UpperCamelCase							,   mask_token=_UpperCamelCase							,   cls_token=_UpperCamelCase							,   sp_model_kwargs=self.sp_model_kwargs							,   **_UpperCamelCase							,   )

									snake_case_   =	vocab_file

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(_UpperCamelCase							)







			@property
			def 				snake_case__(     self :				str							)      ->List[Any]:
									return self.sp_model.get_piece_size()







			def 				snake_case__(     self :				int							)      ->Union[str, Any]:
									snake_case_   =	{self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def __getstate__(     self :				Tuple							)      ->Any:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									return state







			def __setstate__(     self :				str							,   _UpperCamelCase :				List[Any]							)      ->List[str]:
									snake_case_   =	d

									# for backward compatibility
									if not hasattr(self							,   '''sp_model_kwargs'''							):
															snake_case_   =	{}

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(self.vocab_file							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(_UpperCamelCase							,   out_type=_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[str]							)      ->Tuple:
									return self.sp_model.piece_to_id(_UpperCamelCase							)







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				str							)      ->List[Any]:
									snake_case_   =	self.sp_model.IdToPiece(_UpperCamelCase							)
									return token







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				Optional[int]							)      ->List[str]:
									snake_case_   =	[]
									snake_case_   =	''''''
									snake_case_   =	False
									for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																					if not prev_is_special:
																											out_string += " "
																					out_string += self.sp_model.decode(_UpperCamelCase							) + token
																					snake_case_   =	True
																					snake_case_   =	[]
															else:
																					current_sub_tokens.append(_UpperCamelCase							)
																					snake_case_   =	False
									out_string += self.sp_model.decode(_UpperCamelCase							)
									return out_string.strip()







			def 				snake_case__(     self :				int							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				bool = None							,   _UpperCamelCase :				bool = True							,   **_UpperCamelCase :				List[str]							,   )      ->str:
									snake_case_   =	kwargs.pop('''use_source_tokenizer'''							,   _UpperCamelCase							)

									snake_case_   =	self.convert_ids_to_tokens(_UpperCamelCase							,   skip_special_tokens=_UpperCamelCase							)

									# To avoid mixing byte-level and unicode for byte-level BPT
									# we need to build string separately for added tokens and byte-level tokens
									# cf. https://github.com/huggingface/transformers/issues/1133
									snake_case_   =	[]
									snake_case_   =	[]
									for token in filtered_tokens:
															if skip_special_tokens and token in self.all_special_ids:
																					continue
															if token in self.added_tokens_encoder:
																					if current_sub_text:
																											sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)
																											snake_case_   =	[]
																					sub_texts.append(_UpperCamelCase							)
															else:
																					current_sub_text.append(_UpperCamelCase							)
									if current_sub_text:
															sub_texts.append(self.convert_tokens_to_string(_UpperCamelCase							)							)

									# Mimic the behavior of the Rust tokenizer:
									# No space before [MASK] and [SEP]
									if spaces_between_special_tokens:
															snake_case_   =	re.sub(R''' (\[(MASK|SEP)\])'''							,   R'''\1'''							,   ''' '''.join(_UpperCamelCase							)							)
									else:
															snake_case_   =	''''''.join(_UpperCamelCase							)

									snake_case_   =	(
									    clean_up_tokenization_spaces
									    if clean_up_tokenization_spaces is not None
									    else self.clean_up_tokenization_spaces
									)
									if clean_up_tokenization_spaces:
															snake_case_   =	self.clean_up_tokenization(_UpperCamelCase							)
															return clean_text
									else:
															return text







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = None							)      ->Tuple[str]:
									if not os.path.isdir(_UpperCamelCase							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	os.path.join(
									    _UpperCamelCase							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

									if os.path.abspath(self.vocab_file							) != os.path.abspath(_UpperCamelCase							) and os.path.isfile(self.vocab_file							):
															copyfile(self.vocab_file							,   _UpperCamelCase							)
									elif not os.path.isfile(self.vocab_file							):
															with open(_UpperCamelCase							,   '''wb'''							) as fi:
																					snake_case_   =	self.sp_model.serialized_model_proto()
																					fi.write(_UpperCamelCase							)

									return (out_vocab_file,)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									snake_case_   =	[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							,   _UpperCamelCase :				bool = False							)      ->List[int]:
									if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=_UpperCamelCase							,   token_ids_a=_UpperCamelCase							,   already_has_special_tokens=_UpperCamelCase							)

									if token_ids_a is None:
															return [1] + ([0] * len(_UpperCamelCase							)) + [1]
									return [1] + ([0] * len(_UpperCamelCase							)) + [1] + ([0] * len(_UpperCamelCase							)) + [1]







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->List[int]:
									snake_case_   =	[self.sep_token_id]
									snake_case_   =	[self.cls_token_id]
									if token_ids_a is None:
															return len(cls + token_ids_a + sep							) * [0]
									return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]