



"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowercase : Optional[Any]      						=						logging.get_logger(__name__)

_lowercase : List[Any]      						=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    Union[str, Any]	):
			"""simple docstring"""
			for attribute in key.split('''.'''	):
						lowerCamelCase__		:		List[str]			=getattr(__lowerCamelCase							,		__lowerCamelCase	)

			if weight_type is not None:
						lowerCamelCase__		:		str			=getattr(__lowerCamelCase							,		__lowerCamelCase	).shape
			else:
						lowerCamelCase__		:		Tuple			=hf_pointer.shape

			assert hf_shape == value.shape, (
			    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
			    f''' {value.shape} for {full_name}'''
			)

			if weight_type == "weight":
						lowerCamelCase__		:		int			=value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		Any			=value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		Optional[int]			=value
			elif weight_type == "bias":
						lowerCamelCase__		:		Dict			=value
			else:
						lowerCamelCase__		:		Union[str, Any]			=value

			logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''	)






def      snake_case__   (		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    Union[str, Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		int			=[]
			lowerCamelCase__		:		Tuple			=fairseq_model.state_dict()

			lowerCamelCase__		:		Tuple			=hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						lowerCamelCase__		:		str			=False
						if "conv_layers" in name:
									load_conv_layer(
									    __lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		hf_model.config.feat_extract_norm == '''group'''							,		)
									lowerCamelCase__		:		Union[str, Any]			=True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCamelCase__		:		Optional[int]			='''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

												if key in name or (key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0] and not is_finetuned):
															lowerCamelCase__		:		List[str]			=True
															if "*" in mapped_key:
																		lowerCamelCase__		:		Any			=name.split(__lowerCamelCase	)[0].split('''.'''	)[-2]
																		lowerCamelCase__		:		Tuple			=mapped_key.replace('''*'''							,		__lowerCamelCase	)
															if "weight_g" in name:
																		lowerCamelCase__		:		int			='''weight_g'''
															elif "weight_v" in name:
																		lowerCamelCase__		:		List[Any]			='''weight_v'''
															elif "weight" in name:
																		lowerCamelCase__		:		List[Any]			='''weight'''
															elif "bias" in name:
																		lowerCamelCase__		:		List[Any]			='''bias'''
															else:
																		lowerCamelCase__		:		List[Any]			=None
															set_recursively(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)
												continue
						if not is_used:
									unused_weights.append(__lowerCamelCase	)

			logger.warning(f'''Unused weights: {unused_weights}'''	)






def      snake_case__   (		__lowerCamelCase				:    Optional[int]							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Optional[Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			=full_name.split('''conv_layers.'''	)[-1]
			lowerCamelCase__		:		Dict			=name.split('''.'''	)
			lowerCamelCase__		:		List[Any]			=int(items[0]	)
			lowerCamelCase__		:		str			=int(items[1]	)

			if type_id == 0:
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
									)
									lowerCamelCase__		:		Union[str, Any]			=value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
									)
									lowerCamelCase__		:		List[Any]			=value
									logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''	)
			elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
						if "bias" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
									    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
									    " found."
									)
									lowerCamelCase__		:		Any			=value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
						elif "weight" in name:
									assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
									    f'''{full_name} has size {value.shape}, but'''
									    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
									)
									lowerCamelCase__		:		Any			=value
									logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
			else:
						unused_weights.append(__lowerCamelCase	)






@torch.no_grad()
def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    List[Any]=None							,		__lowerCamelCase				:    Any=None							,		__lowerCamelCase				:    List[Any]=True	):
			"""simple docstring"""
			if config_path is not None:
						lowerCamelCase__		:		Dict			=HubertConfig.from_pretrained(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		Union[str, Any]			=HubertConfig()

			if is_finetuned:
						if dict_path:
									lowerCamelCase__		:		Union[str, Any]			=Dictionary.load(__lowerCamelCase	)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									lowerCamelCase__		:		Tuple			=target_dict.pad_index
									lowerCamelCase__		:		Tuple			=target_dict.bos_index
									lowerCamelCase__		:		int			=target_dict.eos_index
									lowerCamelCase__		:		List[Any]			=len(target_dict.symbols	)
									lowerCamelCase__		:		Tuple			=os.path.join(__lowerCamelCase							,		'''vocab.json'''	)
									if not os.path.isdir(__lowerCamelCase	):
												logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__lowerCamelCase	)	)
												return
									os.makedirs(__lowerCamelCase							,		exist_ok=__lowerCamelCase	)
									with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''	) as vocab_handle:
												json.dump(target_dict.indices							,		__lowerCamelCase	)
									lowerCamelCase__		:		List[Any]			=WavaVecaCTCTokenizer(
									    __lowerCamelCase							,		unk_token=target_dict.unk_word							,		pad_token=target_dict.pad_word							,		bos_token=target_dict.bos_word							,		eos_token=target_dict.eos_word							,		word_delimiter_token='''|'''							,		do_lower_case=__lowerCamelCase							,		)
									lowerCamelCase__		:		Any			=True if config.feat_extract_norm == '''layer''' else False
									lowerCamelCase__		:		Optional[Any]			=WavaVecaFeatureExtractor(
									    feature_size=1							,		sampling_rate=16000							,		padding_value=0							,		do_normalize=__lowerCamelCase							,		return_attention_mask=__lowerCamelCase							,		)
									lowerCamelCase__		:		Union[str, Any]			=WavaVecaProcessor(feature_extractor=__lowerCamelCase							,		tokenizer=__lowerCamelCase	)
									processor.save_pretrained(__lowerCamelCase	)

						lowerCamelCase__		:		str			=HubertForCTC(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		List[str]			=HubertModel(__lowerCamelCase	)

			if is_finetuned:
						lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Tuple			=fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]							,		arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''	)[:-1]	)}	)
			else:
						lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		Union[str, Any]			=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

			lowerCamelCase__		:		List[Any]			=model[0].eval()

			recursively_load_weights(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			hf_wavavec.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
		_lowercase : Any      						=						argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		parser.add_argument(
		    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
		)
		_lowercase : str      						=						parser.parse_args()
		convert_hubert_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)









"""simple docstring"""






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

import sentencepiece as spm

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


if is_torch_available():
		import torch


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


_lowercase : Optional[int]      						=						logging.get_logger(__name__)
_lowercase : Optional[int]      						=						{"vocab_file": "spiece.model"}

_lowercase : str      						=						{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

_lowercase : Optional[Any]      						=						{
    "AI-Sweden/gpt-sw3-126m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-350m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-1.6b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-6.7b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-20b": 2_0_4_8,
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a   			=							['input_ids', 'attention_mask']



					def __init__(       self							:  Dict,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  str=False,  lowerCamelCase							:  List[Any]=False,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Optional[int]=None,  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Dict,  )->					None:
								lowerCamelCase__		:		Any			={} if sp_model_kwargs is None else sp_model_kwargs

								lowerCamelCase__		:		Optional[int]			=kwargs.get('''name_or_path'''     )
								if name_or_path is None:
											logger.warning(
											    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
											    ''' you are testing the model, this can safely be ignored'''     )
											lowerCamelCase__		:		List[Any]			='''None'''

								# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
								lowerCamelCase__		:		Tuple			='''<|endoftext|>''' if eos_token is None else eos_token
								lowerCamelCase__		:		Dict			='''<unk>''' if unk_token is None else unk_token
								if "gpt-sw3-7b" in name_or_path:
											lowerCamelCase__		:		int			=unk_token if pad_token is None else pad_token
											lowerCamelCase__		:		str			=eos_token if bos_token is None else bos_token
								else:
											lowerCamelCase__		:		Any			='''<pad>''' if pad_token is None else pad_token
											lowerCamelCase__		:		str			='''<s>''' if bos_token is None else bos_token

								super().__init__(
								    do_lower_case=lowerCamelCase,  remove_space=lowerCamelCase,  keep_accents=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  unk_token=lowerCamelCase,  pad_token=lowerCamelCase,  sp_model_kwargs=self.sp_model_kwargs,  **lowerCamelCase,  )

								lowerCamelCase__		:		Union[str, Any]			=do_lower_case
								lowerCamelCase__		:		Union[str, Any]			=remove_space
								lowerCamelCase__		:		int			=keep_accents
								lowerCamelCase__		:		Tuple			=vocab_file

								lowerCamelCase__		:		List[str]			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(lowerCamelCase     )

								# Used for whitespace normalization in input texts
								# fmt : off
								lowerCamelCase__		:		List[Any]			={''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
								# fmt : on

								# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
								lowerCamelCase__		:		int			=re.compile(
								    F'''[{"".join(map(lowerCamelCase,  list(range(0,  9     )     ) + list(range(11,  32     )     ) + list(range(127,  160     )     ) + [160, 173, 8203]     )     )}]'''     )



					def __getstate__(       self							:  Any     )->					Optional[Any]:
								lowerCamelCase__		:		List[str]			=self.__dict__.copy()
								lowerCamelCase__		:		List[Any]			=None
								return state



					def __setstate__(       self							:  Optional[Any],  lowerCamelCase							:  Dict     )->					int:
								lowerCamelCase__		:		List[str]			=d

								# for backward compatibility
								if not hasattr(self,  '''sp_model_kwargs'''     ):
											lowerCamelCase__		:		Union[str, Any]			={}

								lowerCamelCase__		:		str			=spm.SentencePieceProcessor(**self.sp_model_kwargs     )
								self.sp_model.Load(self.vocab_file     )



					@property
					# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
					def 		snake_case       (       self							:  List[str]     )->					int:
								return len(self.sp_model     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  str     )->					str:
								lowerCamelCase__		:		Dict			=self.non_printing_characters_re.sub('''''',  lowerCamelCase     )

								# Normalize whitespaces
								lowerCamelCase__		:		List[Any]			=''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]     )

								# NFC Unicode normalization
								lowerCamelCase__		:		Union[str, Any]			=unicodedata.normalize('''NFC''',  lowerCamelCase     )
								return text



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  str,  **lowerCamelCase							:  str     )->					List[str]:
								lowerCamelCase__		:		int			=self.preprocess_text(lowerCamelCase     )
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase     )



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  str     )->					int:
								return self.sp_model.PieceToId(lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[int],  lowerCamelCase							:  int     )->					str:
								return self.sp_model.IdToPiece(lowerCamelCase     )



					@staticmethod
					def 		snake_case       (       lowerCamelCase							:  str     )->					str:
								return out_string



					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  List[str]     )->					str:
								lowerCamelCase__		:		Dict			=[]
								lowerCamelCase__		:		int			=''''''
								lowerCamelCase__		:		Any			=False
								for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
														# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
														if not prev_is_special:
																	out_string += " "

														out_string += self.sp_model.decode(lowerCamelCase     ) + token
														lowerCamelCase__		:		Tuple			=True
														lowerCamelCase__		:		Tuple			=[]
											else:
														current_sub_tokens.append(lowerCamelCase     )
														lowerCamelCase__		:		Optional[Any]			=False
								out_string += self.sp_model.decode(lowerCamelCase     )

								return out_string



					def 		snake_case       (       self							:  Union[str, Any]     )->					Dict[str, int]:
								lowerCamelCase__		:		Tuple			={self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
								vocab.update(self.added_tokens_encoder     )
								return vocab



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

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

								return (out_vocab_file,)



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Union[str, List[str]],  lowerCamelCase							:  Union[str, bool] = False     )->					Union[List[int], List[List[int]], "torch.Tensor"]:

								if isinstance(lowerCamelCase,  lowerCamelCase     ):
											lowerCamelCase__		:		str			=self.preprocess_text(lowerCamelCase     )
											lowerCamelCase__		:		Union[str, Any]			=self.sp_model.encode(lowerCamelCase     )
								else:
											lowerCamelCase__		:		int			=[self.preprocess_text(lowerCamelCase     ) for t in text]
											lowerCamelCase__		:		Tuple			=self.sp_model.encode(lowerCamelCase     )

								if return_tensors is True or return_tensors == "pt":
											lowerCamelCase__		:		Union[str, Any]			=torch.tensor(lowerCamelCase     )

								return token_ids



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Union[int, List[int]]     )->					str:
								return self.sp_model.decode(lowerCamelCase     )




					def 		snake_case       (       self							:  Tuple,  lowerCamelCase							:  "Conversation"     )->					List[int]:
								lowerCamelCase__		:		Any			=[F'''User: {text}''' if is_user else F'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
								lowerCamelCase__		:		str			=(
								    F'''{self.eos_token}{self.bos_token}''' + F'''{self.bos_token}'''.join(lowerCamelCase     ) + F'''{self.bos_token}Bot:'''
								)
								return self.encode(text=lowerCamelCase     )




