





'''simple docstring'''







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case_  :							Any            =	logging.get_logger(__name__)

snake_case_  :							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",
    "mask_emb": "masked_spec_embed",
}





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      int )			->						List[Any]:
						for attribute in key.split('''.''' ):
												UpperCAmelCase_      :   int      =	getattr(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						if weight_type is not None:
												UpperCAmelCase_      :   Dict      =	getattr(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ ).shape
						else:
												UpperCAmelCase_      :   Union[str, Any]      =	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":
												UpperCAmelCase_      :   Tuple      =	value
						elif weight_type == "weight_g":
												UpperCAmelCase_      :   Union[str, Any]      =	value
						elif weight_type == "weight_v":
												UpperCAmelCase_      :   Any      =	value
						elif weight_type == "bias":
												UpperCAmelCase_      :   int      =	value
						else:
												UpperCAmelCase_      :   Dict      =	value

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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Union[str, Any]:
						UpperCAmelCase_      :   Any      =	[]
						UpperCAmelCase_      :   List[Any]      =	fairseq_model.state_dict()

						UpperCAmelCase_      :   Tuple      =	hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												UpperCAmelCase_      :   str      =	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      hf_model.config.feat_extract_norm == '''group''',      )
																		UpperCAmelCase_      :   List[Any]      =	True
												else:
																		for key, mapped_key in MAPPING.items():
																								UpperCAmelCase_      :   Optional[Any]      =	mapped_key

																								if key in name:
																														UpperCAmelCase_      :   int      =	True
																														if "*" in mapped_key:
																																				UpperCAmelCase_      :   str      =	name.split(SCREAMING_SNAKE_CASE__ )[0].split('''.''' )[-2]
																																				UpperCAmelCase_      :   Union[str, Any]      =	mapped_key.replace('''*''',      SCREAMING_SNAKE_CASE__ )
																														if "weight_g" in name:
																																				UpperCAmelCase_      :   Union[str, Any]      =	'''weight_g'''
																														elif "weight_v" in name:
																																				UpperCAmelCase_      :   Union[str, Any]      =	'''weight_v'''
																														elif "weight" in name:
																																				UpperCAmelCase_      :   str      =	'''weight'''
																														elif "bias" in name:
																																				UpperCAmelCase_      :   Optional[int]      =	'''bias'''
																														else:
																																				UpperCAmelCase_      :   List[str]      =	None
																														set_recursively(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																								continue
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE__ )

						logger.warning(F"""Unused weights: {unused_weights}""" )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      str )			->						Any:
						UpperCAmelCase_      :   str      =	full_name.split('''conv_layers.''' )[-1]
						UpperCAmelCase_      :   Optional[Any]      =	name.split('''.''' )
						UpperCAmelCase_      :   Any      =	int(items[0] )
						UpperCAmelCase_      :   List[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."""
																		)
																		UpperCAmelCase_      :   Optional[int]      =	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."""
																		)
																		UpperCAmelCase_      :   int      =	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."
																		)
																		UpperCAmelCase_      :   Optional[int]      =	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."""
																		)
																		UpperCAmelCase_      :   Optional[int]      =	value
																		logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
						else:
												unused_weights.append(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						Union[str, Any]:
						UpperCAmelCase_      :   Optional[int]      =	HubertConfig()
						UpperCAmelCase_      :   Dict      =	model.config

						UpperCAmelCase_      :   str      =	fs_config.activation_dropout
						UpperCAmelCase_      :   List[Any]      =	False
						UpperCAmelCase_      :   str      =	fs_config.attention_dropout
						UpperCAmelCase_      :   Optional[int]      =	False
						UpperCAmelCase_      :   str      =	eval(fs_config.extractor_conv_feature_layers )
						UpperCAmelCase_      :   Dict      =	[x[0] for x in conv_layers]
						UpperCAmelCase_      :   List[Any]      =	[x[1] for x in conv_layers]
						UpperCAmelCase_      :   Any      =	[x[2] for x in conv_layers]
						UpperCAmelCase_      :   Union[str, Any]      =	'''gelu'''
						UpperCAmelCase_      :   Optional[Any]      =	'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						UpperCAmelCase_      :   str      =	False
						UpperCAmelCase_      :   Tuple      =	0.0
						UpperCAmelCase_      :   Optional[int]      =	0.0
						UpperCAmelCase_      :   Union[str, Any]      =	fs_config.activation_fn
						UpperCAmelCase_      :   Optional[Any]      =	fs_config.dropout
						UpperCAmelCase_      :   Any      =	fs_config.encoder_embed_dim
						UpperCAmelCase_      :   int      =	0.02
						UpperCAmelCase_      :   List[str]      =	fs_config.encoder_ffn_embed_dim
						UpperCAmelCase_      :   List[str]      =	1E-5
						UpperCAmelCase_      :   Union[str, Any]      =	0.0
						UpperCAmelCase_      :   List[str]      =	fs_config.encoder_attention_heads
						UpperCAmelCase_      :   str      =	fs_config.conv_pos_groups
						UpperCAmelCase_      :   str      =	fs_config.conv_pos
						UpperCAmelCase_      :   Tuple      =	len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   int      =	fs_config.encoder_layers

						return config





@torch.no_grad()
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      List[str]=None )			->						Union[str, Any]:
						UpperCAmelCase_      :   Optional[Any]      =	distilhubert().model.model

						if config_path is not None:
												UpperCAmelCase_      :   str      =	HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE__ )
						else:
												UpperCAmelCase_      :   Any      =	convert_config(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Any      =	model.eval()

						UpperCAmelCase_      :   str      =	WavaVecaFeatureExtractor(
						    feature_size=1,      sampling_rate=16000,      padding_value=0,      do_normalize=SCREAMING_SNAKE_CASE__,      return_attention_mask=SCREAMING_SNAKE_CASE__,      )
						UpperCAmelCase_      :   str      =	HubertModel(SCREAMING_SNAKE_CASE__ )

						recursively_load_weights(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__ )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE__ )


if __name__ == "__main__":
					snake_case_  :							Any            =	argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					snake_case_  :							List[str]            =	parser.parse_args()
					convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


SCREAMING_SNAKE_CASE_  :      str							=      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_  :      List[Any]							=      {'''vocab_file''': '''vocab.txt'''}

SCREAMING_SNAKE_CASE_  :      int							=      {
    '''vocab_file''': {
        '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
        '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
        '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
        '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
    }
}

SCREAMING_SNAKE_CASE_  :      Union[str, Any]							=      {
    '''tau/splinter-base''': 512,
    '''tau/splinter-base-qass''': 512,
    '''tau/splinter-large''': 512,
    '''tau/splinter-large-qass''': 512,
}

SCREAMING_SNAKE_CASE_  :      List[Any]							=      {
    '''tau/splinter-base''': {'''do_lower_case''': False},
    '''tau/splinter-base-qass''': {'''do_lower_case''': False},
    '''tau/splinter-large''': {'''do_lower_case''': False},
    '''tau/splinter-large-qass''': {'''do_lower_case''': False},
}


class 		_A							(      __a       ):
      __a             =						VOCAB_FILES_NAMES
      __a             =						PRETRAINED_VOCAB_FILES_MAP
      __a             =						PRETRAINED_INIT_CONFIGURATION
      __a             =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __a             =						SplinterTokenizer





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=None     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__="[UNK]"     ,    SCREAMING_SNAKE_CASE__="[SEP]"     ,    SCREAMING_SNAKE_CASE__="[PAD]"     ,    SCREAMING_SNAKE_CASE__="[CLS]"     ,    SCREAMING_SNAKE_CASE__="[MASK]"     ,    SCREAMING_SNAKE_CASE__="[QUESTION]"     ,    SCREAMING_SNAKE_CASE__=True     ,    SCREAMING_SNAKE_CASE__=None     ,    **SCREAMING_SNAKE_CASE__     ,    ) ->       Optional[Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE__     ,    tokenizer_file=SCREAMING_SNAKE_CASE__     ,    do_lower_case=SCREAMING_SNAKE_CASE__     ,    unk_token=SCREAMING_SNAKE_CASE__     ,    sep_token=SCREAMING_SNAKE_CASE__     ,    pad_token=SCREAMING_SNAKE_CASE__     ,    cls_token=SCREAMING_SNAKE_CASE__     ,    mask_token=SCREAMING_SNAKE_CASE__     ,    tokenize_chinese_chars=SCREAMING_SNAKE_CASE__     ,    strip_accents=SCREAMING_SNAKE_CASE__     ,    additional_special_tokens=(question_token,)     ,    **SCREAMING_SNAKE_CASE__     ,    )

         lowerCamelCase__						       =      json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
         if (
             pre_tok_state.get("lowercase"     ,    SCREAMING_SNAKE_CASE__		) != do_lower_case
             or pre_tok_state.get("strip_accents"     ,    SCREAMING_SNAKE_CASE__		) != strip_accents
         ):
            lowerCamelCase__						       =      getattr(SCREAMING_SNAKE_CASE__     ,    pre_tok_state.pop("type"		)		)
            lowerCamelCase__						       =      do_lower_case
            lowerCamelCase__						       =      strip_accents
            lowerCamelCase__						       =      pre_tok_class(**SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						       =      do_lower_case





      @property
      def        _lowerCamelCase	(	self		) ->       Optional[Any]:
         return self.convert_tokens_to_ids(self.question_token		)





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       List[int]:
         if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         lowerCamelCase__						       =      [self.cls_token_id]
         lowerCamelCase__						       =      [self.sep_token_id]
         lowerCamelCase__						       =      [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
         if self.padding_side == "right":
            # Input is question-then-context
            return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
         else:
            # Input is context-then-question
            return cls + token_ids_a + sep + token_ids_a + question_suffix + sep





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       List[int]:
         lowerCamelCase__						       =      [self.sep_token_id]
         lowerCamelCase__						       =      [self.cls_token_id]
         lowerCamelCase__						       =      [self.question_token_id] + [self.convert_tokens_to_ids("."		)]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep		) * [0]

         if self.padding_side == "right":
            # Input is question-then-context
            return len(cls + token_ids_a + question_suffix + sep		) * [0] + len(token_ids_a + sep		) * [1]
         else:
            # Input is context-then-question
            return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + question_suffix + sep		) * [1]





      def        _lowerCamelCase	(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = None		) ->       Tuple[str]:
         lowerCamelCase__						       =      self._tokenizer.model.save(SCREAMING_SNAKE_CASE__     ,    name=SCREAMING_SNAKE_CASE__		)
         return tuple(SCREAMING_SNAKE_CASE__		)




