





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

snake_case_				=			{
    '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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
snake_case_				=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				Tuple:
   __snake_case							       =   {}
   with open(snake_case_       ,      '''r'''       ) as file:
      for line_number, line in enumerate(snake_case_       ):
         __snake_case							       =   line.strip()
         if line:
            __snake_case							       =   line.split()
            __snake_case							       =   line_number
            __snake_case							       =   words[0]
            __snake_case							       =   value
   return result






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  Tuple       ,      snake_case_	:  List[Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Optional[Any]       )      ->				List[Any]:
   for attribute in key.split('''.'''       ):
      __snake_case							       =   getattr(snake_case_       ,      snake_case_       )

   __snake_case							       =   None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case_       ):
         __snake_case							       =   PARAM_MAPPING[full_name.split('''.'''       )[-1]]
         __snake_case							       =   '''param'''

   if weight_type is not None and weight_type != "param":
      __snake_case							       =   getattr(snake_case_       ,      snake_case_       ).shape
   elif weight_type is not None and weight_type == "param":
      __snake_case							       =   hf_pointer
      for attribute in hf_param_name.split('''.'''       ):
         __snake_case							       =   getattr(snake_case_       ,      snake_case_       )
      __snake_case							       =   shape_pointer.shape

      # let's reduce dimension
      __snake_case							       =   value[0]
   else:
      __snake_case							       =   hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          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":
      __snake_case							       =   value
   elif weight_type == "weight_g":
      __snake_case							       =   value
   elif weight_type == "weight_v":
      __snake_case							       =   value
   elif weight_type == "bias":
      __snake_case							       =   value
   elif weight_type == "param":
      for attribute in hf_param_name.split('''.'''       ):
         __snake_case							       =   getattr(snake_case_       ,      snake_case_       )
      __snake_case							       =   value
   else:
      __snake_case							       =   value

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






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  Any       ,      snake_case_	:  Dict       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  List[str]       )      ->				str:
   __snake_case							       =   None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case_       ):
         __snake_case							       =   PARAM_MAPPING[full_name.split('''.'''       )[-1]]
         __snake_case							       =   '''param'''

   if weight_type is not None and weight_type != "param":
      __snake_case							       =   '''.'''.join([key, weight_type]       )
   elif weight_type is not None and weight_type == "param":
      __snake_case							       =   '''.'''.join([key, hf_param_name]       )
   else:
      __snake_case							       =   key

   __snake_case							       =   value if '''lm_head''' in full_key else value[0]


snake_case_				=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  List[Any]       ,      snake_case_	:  List[str]=None       ,      snake_case_	:  Dict=None       )      ->				str:
   __snake_case							       =   False
   for key, mapped_key in MAPPING.items():
      __snake_case							       =   '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
      if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
         __snake_case							       =   True
         if "*" in mapped_key:
            __snake_case							       =   name.split(snake_case_       )[0].split('''.'''       )[-2]
            __snake_case							       =   mapped_key.replace('''*'''       ,      snake_case_       )
         if "weight_g" in name:
            __snake_case							       =   '''weight_g'''
         elif "weight_v" in name:
            __snake_case							       =   '''weight_v'''
         elif "bias" in name:
            __snake_case							       =   '''bias'''
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __snake_case							       =   '''weight'''
         else:
            __snake_case							       =   None
         if hf_dict is not None:
            rename_dict(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
         else:
            set_recursively(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
         return is_used
   return is_used






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  List[str]       )      ->				int:
   __snake_case							       =   []
   __snake_case							       =   fairseq_model.state_dict()

   __snake_case							       =   hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case							       =   False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      hf_model.config.feat_extract_norm == '''group'''       ,      )
         __snake_case							       =   True
      else:
         __snake_case							       =   load_wavaveca_layer(snake_case_       ,      snake_case_       ,      snake_case_       )
      if not is_used:
         unused_weights.append(snake_case_       )

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






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Any       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  List[str]       )      ->				List[Any]:
   __snake_case							       =   full_name.split('''conv_layers.'''       )[-1]
   __snake_case							       =   name.split('''.'''       )
   __snake_case							       =   int(items[0]       )
   __snake_case							       =   int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""       )
         __snake_case							       =   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:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
   else:
      unused_weights.append(snake_case_       )






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  int=None       ,      snake_case_	:  Any=None       ,      snake_case_	:  Any=True       ,      snake_case_	:  Any=False       )      ->				List[str]:
   if config_path is not None:
      __snake_case							       =   WavaVecaConfig.from_pretrained(snake_case_       )
   else:
      __snake_case							       =   WavaVecaConfig()

   if is_seq_class:
      __snake_case							       =   read_txt_into_dict(snake_case_       )
      __snake_case							       =   idalabel
      __snake_case							       =   WavaVecaForSequenceClassification(snake_case_       )
      __snake_case							       =   WavaVecaFeatureExtractor(
          feature_size=1       ,      sampling_rate=1_6000       ,      padding_value=0       ,      do_normalize=snake_case_       ,      return_attention_mask=snake_case_       ,      )
      feature_extractor.save_pretrained(snake_case_       )

   elif is_finetuned:
      if dict_path:
         __snake_case							       =   Dictionary.load(snake_case_       )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __snake_case							       =   target_dict.pad_index
         __snake_case							       =   target_dict.bos_index
         __snake_case							       =   target_dict.eos_index
         __snake_case							       =   len(target_dict.symbols       )
         __snake_case							       =   os.path.join(snake_case_       ,      '''vocab.json'''       )
         if not os.path.isdir(snake_case_       ):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case_       )       )
            return
         os.makedirs(snake_case_       ,      exist_ok=snake_case_       )
         __snake_case							       =   target_dict.indices

         # fairseq has the <pad> and <s> switched
         __snake_case							       =   0
         __snake_case							       =   1
         with open(snake_case_       ,      '''w'''       ,      encoding='''utf-8'''       ) as vocab_handle:
            json.dump(snake_case_       ,      snake_case_       )
         __snake_case							       =   WavaVecaCTCTokenizer(
             snake_case_       ,      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=snake_case_       ,      )
         __snake_case							       =   True if config.feat_extract_norm == '''layer''' else False
         __snake_case							       =   WavaVecaFeatureExtractor(
             feature_size=1       ,      sampling_rate=1_6000       ,      padding_value=0       ,      do_normalize=snake_case_       ,      return_attention_mask=snake_case_       ,      )
         __snake_case							       =   WavaVecaProcessor(feature_extractor=snake_case_       ,      tokenizer=snake_case_       )
         processor.save_pretrained(snake_case_       )

      __snake_case							       =   WavaVecaForCTC(snake_case_       )
   else:
      __snake_case							       =   WavaVecaForPreTraining(snake_case_       )

   if is_finetuned or is_seq_class:
      __snake_case       ,	__snake_case       ,	__snake_case							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]       ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
   else:
      __snake_case							       =   argparse.Namespace(task='''audio_pretraining'''       )
      __snake_case							       =   fairseq.tasks.setup_task(snake_case_       )

      __snake_case       ,	__snake_case       ,	__snake_case							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,      task=snake_case_       )

   __snake_case							       =   model[0].eval()

   recursively_load_weights(snake_case_       ,      snake_case_       ,      not is_finetuned       )

   hf_wavavec.save_pretrained(snake_case_       )


if __name__ == "__main__":
    snake_case_				=			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'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    snake_case_				=			parser.parse_args()

    snake_case_				=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )








import warnings

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

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):
      A_						:				List[str]							=    ['image_processor', 'tokenizer']
      A_						:				Optional[Any]							=    'CLIPImageProcessor'
      A_						:				Any							=    ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')


      def __init__(self       :      int				,			a__       :      int=None				,			a__       :      Dict=None				,			**a__       :      List[str]      ):





         """simple docstring"""



         __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.'''				,			a__				,			)
            __snake_case							       =   kwargs.pop('''feature_extractor'''      )

         __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__(a__				,			a__      )


      def __call__(self       :      Any				,			a__       :      Dict=None				,			a__       :      List[str]=None				,			a__       :      Dict=None				,			**a__       :      Tuple      ):





         """simple docstring"""



         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:
            __snake_case							       =   self.tokenizer(a__				,			return_tensors=a__				,			**a__      )

         if images is not None:
            __snake_case							       =   self.image_processor(a__				,			return_tensors=a__				,			**a__      )

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


      def    a (self       :      Union[str, Any]				,			*a__       :      int				,			**a__       :      List[Any]      ):





         """simple docstring"""



         return self.tokenizer.batch_decode(*a__				,			**a__      )


      def    a (self       :      Any				,			*a__       :      List[Any]				,			**a__       :      List[str]      ):





         """simple docstring"""



         return self.tokenizer.decode(*a__				,			**a__      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         __snake_case							       =   self.tokenizer.model_input_names
         __snake_case							       =   self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

