




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()
__a							     =					logging.get_logger(__name__)

__a							     =					{
    "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 lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     for attribute in key.split('''.''' ):
          UpperCAmelCase_     :				List[Any]  =     getattr(lowerCAmelCase__					,		lowerCAmelCase__ )

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

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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     []
     UpperCAmelCase_     :				Union[str, Any]  =     fairseq_model.state_dict()

     UpperCAmelCase_     :				Union[str, Any]  =     hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_     :				Union[str, Any]  =     False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__					,		hf_model.config.feat_extract_norm == '''group'''					,		)
               UpperCAmelCase_     :				Optional[int]  =     True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_     :				List[Any]  =     '''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):
                         UpperCAmelCase_     :				List[str]  =     True
                         if "*" in mapped_key:
                              UpperCAmelCase_     :				Tuple  =     name.split(lowerCAmelCase__ )[0].split('''.''' )[-2]
                              UpperCAmelCase_     :				str  =     mapped_key.replace('''*'''					,		lowerCAmelCase__ )
                         if "weight_g" in name:
                              UpperCAmelCase_     :				List[Any]  =     '''weight_g'''
                         elif "weight_v" in name:
                              UpperCAmelCase_     :				Any  =     '''weight_v'''
                         elif "weight" in name:
                              UpperCAmelCase_     :				Tuple  =     '''weight'''
                         elif "bias" in name:
                              UpperCAmelCase_     :				Tuple  =     '''bias'''
                         else:
                              UpperCAmelCase_     :				Optional[int]  =     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 lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     full_name.split('''conv_layers.''' )[-1]
     UpperCAmelCase_     :				List[Any]  =     name.split('''.''' )
     UpperCAmelCase_     :				str  =     int(items[0] )
     UpperCAmelCase_     :				int  =     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_     :				Dict  =     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_     :				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."
               )
               UpperCAmelCase_     :				Tuple  =     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_     :				Tuple  =     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 lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase=None					,		_lowercase=None					,		_lowercase=True ):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_     :				str  =     HubertConfig.from_pretrained(lowerCAmelCase__ )
     else:
          UpperCAmelCase_     :				Any  =     HubertConfig()

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_     :				List[Any]  =     Dictionary.load(lowerCAmelCase__ )

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               UpperCAmelCase_     :				Any  =     target_dict.pad_index
               UpperCAmelCase_     :				Optional[Any]  =     target_dict.bos_index
               UpperCAmelCase_     :				Tuple  =     target_dict.eos_index
               UpperCAmelCase_     :				Optional[Any]  =     len(target_dict.symbols )
               UpperCAmelCase_     :				List[Any]  =     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__ )
               UpperCAmelCase_     :				Optional[int]  =     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__					,		)
               UpperCAmelCase_     :				Tuple  =     True if config.feat_extract_norm == '''layer''' else False
               UpperCAmelCase_     :				Union[str, Any]  =     WavaVecaFeatureExtractor(
                   feature_size=1					,		sampling_rate=16000					,		padding_value=0					,		do_normalize=lowerCAmelCase__					,		return_attention_mask=lowerCAmelCase__					,		)
               UpperCAmelCase_     :				List[Any]  =     WavaVecaProcessor(feature_extractor=lowerCAmelCase__					,		tokenizer=lowerCAmelCase__ )
               processor.save_pretrained(lowerCAmelCase__ )

          UpperCAmelCase_     :				Any  =     HubertForCTC(lowerCAmelCase__ )
     else:
          UpperCAmelCase_     :				Tuple  =     HubertModel(lowerCAmelCase__ )

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

     UpperCAmelCase_     :				Optional[int]  =     model[0].eval()

     recursively_load_weights(lowerCAmelCase__					,		lowerCAmelCase__					,		lowerCAmelCase__ )

     hf_wavavec.save_pretrained(lowerCAmelCase__ )


if __name__ == "__main__":
     __a							     =					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'
     )
     __a							     =					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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     ["""image_processor""", """tokenizer"""]
				_a    	=     """CLIPImageProcessor"""
				_a    	=     ("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")



				def __init__(    self,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.',     _lowercase,     )
																		SCREAMING_SNAKE_CASE_				=				kwargs.pop('feature_extractor'       )

											SCREAMING_SNAKE_CASE_				=				image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.'       )
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.'       )

											super().__init__(_lowercase,     _lowercase       )



				def __call__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     **_lowercase       )     ->							str:

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'       )

											if text is not None:
																		SCREAMING_SNAKE_CASE_				=				self.tokenizer(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if images is not None:
																		SCREAMING_SNAKE_CASE_				=				self.image_processor(_lowercase,     return_tensors=_lowercase,     **_lowercase       )

											if text is not None and images is not None:
																		SCREAMING_SNAKE_CASE_				=				image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_lowercase       ),     tensor_type=_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[int]:
											return self.tokenizer.batch_decode(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							Optional[Any]:
											return self.tokenizer.decode(*_lowercase,     **_lowercase       )





				@property
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				self.tokenizer.model_input_names
											SCREAMING_SNAKE_CASE_				=				self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





