

'''simple docstring'''





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()
UpperCamelCase_						:      Optional[int]       =						logging.get_logger(__name__)

UpperCamelCase_						:      Optional[int]       =						{
    '''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''',
}
UpperCamelCase_						:      Optional[Any]       =						[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]





def      __a     (       _UpperCamelCase:   int   )						->			Tuple:

     """simple docstring"""




     _snake_case							=				{}
     with open(_UpperCamelCase ,						"r"   ) as file:
          for line_number, line in enumerate(_UpperCamelCase   ):
               _snake_case							=				line.strip()
               if line:
                    _snake_case							=				line.split()
                    _snake_case							=				line_number
                    _snake_case							=				words[0]
                    _snake_case							=				value
     return result





def      __a     (       _UpperCamelCase:   int ,						_UpperCamelCase:   str ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   str   )						->			Dict:

     """simple docstring"""




     for attribute in key.split("."   ):
          _snake_case							=				getattr(_UpperCamelCase ,						_UpperCamelCase   )

     _snake_case							=				None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_UpperCamelCase   ):
               _snake_case							=				PARAM_MAPPING[full_name.split("."   )[-1]]
               _snake_case							=				"param"

     if weight_type is not None and weight_type != "param":
          _snake_case							=				getattr(_UpperCamelCase ,						_UpperCamelCase   ).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(_UpperCamelCase ,						_UpperCamelCase   )
          _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(_UpperCamelCase ,						_UpperCamelCase   )
          _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      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Optional[Any] ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   List[str] ,						_UpperCamelCase:   List[Any]   )						->			str:

     """simple docstring"""




     _snake_case							=				None
     for param_key in PARAM_MAPPING.keys():
          if full_name.endswith(_UpperCamelCase   ):
               _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]


UpperCamelCase_						:      Tuple       =						{
    '''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      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   Dict=None ,						_UpperCamelCase:   Dict=None   )						->			Tuple:

     """simple docstring"""




     _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(_UpperCamelCase   )[0].split("."   )[-2]
                    _snake_case							=				mapped_key.replace("*" ,						_UpperCamelCase   )
               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(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
               else:
                    set_recursively(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
               return is_used
     return is_used





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[Any]   )						->			int:

     """simple docstring"""




     _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(
                   _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						hf_model.config.feat_extract_norm == "group" ,						)
               _snake_case							=				True
          else:
               _snake_case							=				load_wavaveca_layer(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
          if not is_used:
               unused_weights.append(_UpperCamelCase   )

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





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Dict ,						_UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   Tuple   )						->			List[Any]:

     """simple docstring"""




     _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(_UpperCamelCase   )





@torch.no_grad()
def      __a     (       _UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   List[Any] ,						_UpperCamelCase:   List[str]=None ,						_UpperCamelCase:   Tuple=None ,						_UpperCamelCase:   List[Any]=True ,						_UpperCamelCase:   Optional[Any]=False   )						->			Optional[int]:

     """simple docstring"""




     if config_path is not None:
          _snake_case							=				WavaVecaConfig.from_pretrained(_UpperCamelCase   )
     else:
          _snake_case							=				WavaVecaConfig()

     if is_seq_class:
          _snake_case							=				read_txt_into_dict(_UpperCamelCase   )
          _snake_case							=				idalabel
          _snake_case							=				WavaVecaForSequenceClassification(_UpperCamelCase   )
          _snake_case							=				WavaVecaFeatureExtractor(
              feature_size=1 ,						sampling_rate=16_000 ,						padding_value=0 ,						do_normalize=_UpperCamelCase ,						return_attention_mask=_UpperCamelCase ,						)
          feature_extractor.save_pretrained(_UpperCamelCase   )

     elif is_finetuned:
          if dict_path:
               _snake_case							=				Dictionary.load(_UpperCamelCase   )

               # 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(_UpperCamelCase ,						"vocab.json"   )
               if not os.path.isdir(_UpperCamelCase   ):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_UpperCamelCase   )   )
                    return
               os.makedirs(_UpperCamelCase ,						exist_ok=_UpperCamelCase   )
               _snake_case							=				target_dict.indices

               # fairseq has the <pad> and <s> switched
               _snake_case							=				0
               _snake_case							=				1
               with open(_UpperCamelCase ,						"w" ,						encoding="utf-8"   ) as vocab_handle:
                    json.dump(_UpperCamelCase ,						_UpperCamelCase   )
               _snake_case							=				WavaVecaCTCTokenizer(
                   _UpperCamelCase ,						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=_UpperCamelCase ,						)
               _snake_case							=				True if config.feat_extract_norm == "layer" else False
               _snake_case							=				WavaVecaFeatureExtractor(
                   feature_size=1 ,						sampling_rate=16_000 ,						padding_value=0 ,						do_normalize=_UpperCamelCase ,						return_attention_mask=_UpperCamelCase ,						)
               _snake_case							=				WavaVecaProcessor(feature_extractor=_UpperCamelCase ,						tokenizer=_UpperCamelCase   )
               processor.save_pretrained(_UpperCamelCase   )

          _snake_case							=				WavaVecaForCTC(_UpperCamelCase   )
     else:
          _snake_case							=				WavaVecaForPreTraining(_UpperCamelCase   )

     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(_UpperCamelCase   )

          _snake_case							, _snake_case							, _snake_case							=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,						task=_UpperCamelCase   )

     _snake_case							=				model[0].eval()

     recursively_load_weights(_UpperCamelCase ,						_UpperCamelCase ,						not is_finetuned   )

     hf_wavavec.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
   UpperCamelCase_						:      int       =						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''',
   )
   UpperCamelCase_						:      Dict       =						parser.parse_args()

   UpperCamelCase_						:      Optional[Any]       =						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,
   )








'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:      int       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""vit_msn"""






     def __init__(			self ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,_SCREAMING_SNAKE_CASE=1e-06 ,_SCREAMING_SNAKE_CASE=224 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=True ,**_SCREAMING_SNAKE_CASE ,)							->      int:
          super().__init__(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				initializer_range
          _snake_case							=				layer_norm_eps
          _snake_case							=				image_size
          _snake_case							=				patch_size
          _snake_case							=				num_channels
          _snake_case							=				qkv_bias


