





import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Tuple              =						UniSpeechSatForSequenceClassification.from_pretrained(_lowerCamelCase ,    config=_lowerCamelCase    )
			A							:					Optional[int]              =						downstream_dict["projector.weight"]
			A							:					List[str]              =						downstream_dict["projector.bias"]
			A							:					List[str]              =						downstream_dict["model.post_net.linear.weight"]
			A							:					List[str]              =						downstream_dict["model.post_net.linear.bias"]
			return model




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					str              =						UniSpeechSatForAudioFrameClassification.from_pretrained(_lowerCamelCase ,    config=_lowerCamelCase    )
			A							:					Tuple              =						downstream_dict["model.linear.weight"]
			A							:					str              =						downstream_dict["model.linear.bias"]
			return model




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					int              =						UniSpeechSatForXVector.from_pretrained(_lowerCamelCase ,    config=_lowerCamelCase    )
			A							:					Optional[int]              =						downstream_dict["connector.weight"]
			A							:					str              =						downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
						A							:					Dict              =						downstream_dict[
						    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
						]
						A							:					Union[str, Any]              =						downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

			A							:					List[Any]              =						downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
			A							:					str              =						downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
			A							:					Optional[Any]              =						downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
			A							:					Optional[Any]              =						downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
			A							:					Optional[Any]              =						downstream_dict["objective.W"]
			return model




@torch.no_grad()
def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						torch.load(_lowerCamelCase ,    map_location="cpu"    )

			A							:					Dict              =						checkpoint["Downstream"]

			A							:					Dict              =						UniSpeechSatConfig.from_pretrained(_lowerCamelCase    )
			A							:					Any              =						WavaVecaFeatureExtractor.from_pretrained(
			    _lowerCamelCase ,    return_attention_mask=_lowerCamelCase ,    do_normalize=_lowerCamelCase    )

			A							:					Dict              =						hf_config.architectures[0]
			if arch.endswith("ForSequenceClassification"    ):
						A							:					str              =						convert_classification(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			elif arch.endswith("ForAudioFrameClassification"    ):
						A							:					Optional[int]              =						convert_diarization(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			elif arch.endswith("ForXVector"    ):
						A							:					Optional[Any]              =						convert_xvector(_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    )
			else:
						raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""    )

			if hf_config.use_weighted_layer_sum:
						A							:					Union[str, Any]              =						checkpoint["Featurizer"]["weights"]

			hf_feature_extractor.save_pretrained(_lowerCamelCase    )
			hf_model.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE           =  argparse.ArgumentParser()
							parser.add_argument(
							    """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
							)
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
							parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
							__SCREAMING_SNAKE_CASE           =  parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE           =  {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE           =  {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__SCREAMING_SNAKE_CASE           =  {
    """allenai/led-base-16384""": 16384,
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  VOCAB_FILES_NAMES
						a__   =  PRETRAINED_VOCAB_FILES_MAP
						a__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						a__   =  LEDTokenizer
						a__   =  ["input_ids", "attention_mask"]







						def __init__(	self      :			int	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			Optional[int]=None	,	__lowerCamelCase      :			str="replace"	,	__lowerCamelCase      :			Optional[int]="<s>"	,	__lowerCamelCase      :			str="</s>"	,	__lowerCamelCase      :			Any="</s>"	,	__lowerCamelCase      :			Dict="<s>"	,	__lowerCamelCase      :			Optional[int]="<unk>"	,	__lowerCamelCase      :			Union[str, Any]="<pad>"	,	__lowerCamelCase      :			Dict="<mask>"	,	__lowerCamelCase      :			Union[str, Any]=False	,	__lowerCamelCase      :			str=True	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				Optional[int]:
									super().__init__(
									    __lowerCamelCase	,	__lowerCamelCase	,	tokenizer_file=__lowerCamelCase	,	errors=__lowerCamelCase	,	bos_token=__lowerCamelCase	,	eos_token=__lowerCamelCase	,	sep_token=__lowerCamelCase	,	cls_token=__lowerCamelCase	,	unk_token=__lowerCamelCase	,	pad_token=__lowerCamelCase	,	mask_token=__lowerCamelCase	,	add_prefix_space=__lowerCamelCase	,	trim_offsets=__lowerCamelCase	,	**__lowerCamelCase	,	)

									A							:					Union[str, Any]              =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
												A							:					Any              =						getattr(__lowerCamelCase	,	pre_tok_state.pop("type" ) )
												A							:					Any              =						add_prefix_space
												A							:					Tuple              =						pre_tok_class(**__lowerCamelCase )

									A							:					str              =						add_prefix_space

									# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
									A							:					List[str]              =						"post_processor"
									A							:					Union[str, Any]              =						getattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )
									if tokenizer_component_instance:
												A							:					Dict              =						json.loads(tokenizer_component_instance.__getstate__() )

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
															A							:					Union[str, Any]              =						tuple(state["sep"] )
												if "cls" in state:
															A							:					str              =						tuple(state["cls"] )

												A							:					int              =						False

												if state.get("add_prefix_space"	,	__lowerCamelCase ) != add_prefix_space:
															A							:					List[Any]              =						add_prefix_space
															A							:					Dict              =						True

												if state.get("trim_offsets"	,	__lowerCamelCase ) != trim_offsets:
															A							:					Dict              =						trim_offsets
															A							:					str              =						True

												if changes_to_apply:
															A							:					int              =						getattr(__lowerCamelCase	,	state.pop("type" ) )
															A							:					Dict              =						component_class(**__lowerCamelCase )
															setattr(self.backend_tokenizer	,	__lowerCamelCase	,	__lowerCamelCase )







						@property
						# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				str:
									if self._mask_token is None:
												if self.verbose:
															logger.error("Using mask_token, but it is not set yet." )
												return None
									return str(self._mask_token )







						@mask_token.setter
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			Any )      ->				Dict:
									A							:					Tuple              =						AddedToken(__lowerCamelCase	,	lstrip=__lowerCamelCase	,	rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase	,	__lowerCamelCase ) else value
									A							:					Tuple              =						value







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any]	,	*__lowerCamelCase      :			Optional[Any]	,	**__lowerCamelCase      :			List[str] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._batch_encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	*__lowerCamelCase      :			str	,	**__lowerCamelCase      :			Optional[Any] )      ->				BatchEncoding:
									A							:					List[str]              =						kwargs.get("is_split_into_words"	,	__lowerCamelCase )

									if is_split_into_words and not self.add_prefix_space:
												raise ValueError(
												    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
												    "to use it with pretokenized inputs." )

									return super()._encode_plus(*__lowerCamelCase	,	**__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			Optional[str] = None )      ->				Tuple[str]:
									A							:					Optional[Any]              =						self._tokenizer.model.save(__lowerCamelCase	,	name=__lowerCamelCase )
									return tuple(__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			List[Any]=None )      ->				List[str]:
									A							:					Optional[int]              =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
												return output

									return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str]	,	__lowerCamelCase      :			List[int]	,	__lowerCamelCase      :			Optional[List[int]] = None )      ->				List[int]:
									A							:					str              =						[self.sep_token_id]
									A							:					int              =						[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 + sep + token_ids_a + sep ) * [0]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			Union[Dict[str, EncodedInput], BatchEncoding]	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Optional[bool] = None	,	)      ->				dict:
									A							:					Dict              =						super()._pad(
									    encoded_inputs=__lowerCamelCase	,	max_length=__lowerCamelCase	,	padding_strategy=__lowerCamelCase	,	pad_to_multiple_of=__lowerCamelCase	,	return_attention_mask=__lowerCamelCase	,	)

									# Load from model defaults
									if return_attention_mask is None:
												A							:					List[Any]              =						"attention_mask" in self.model_input_names

									if return_attention_mask and "global_attention_mask" in encoded_inputs:
												A							:					Optional[int]              =						encoded_inputs[self.model_input_names[0]]
												# `global_attention_mask` need to have the same length as other (sequential) inputs.
												A							:					Tuple              =						len(encoded_inputs["global_attention_mask"] ) != len(__lowerCamelCase )

												if needs_to_be_padded:
															A							:					Any              =						len(__lowerCamelCase ) - len(encoded_inputs["global_attention_mask"] )

															if self.padding_side == "right":
																		# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																		A							:					Tuple              =						(
																		    encoded_inputs["global_attention_mask"] + [-1] * difference
																		)
															elif self.padding_side == "left":
																		A							:					Tuple              =						[-1] * difference + encoded_inputs[
																		    "global_attention_mask"
																		]
															else:
																		raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

									return encoded_inputs