



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          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''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          =       [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]
def 		UpperCamelCase	(   snake_case__       :    Any	)	->     List[Any]:
   UpperCamelCase    :  Tuple							=	{}
   with open(snake_case__       ,     'r'	) as file:
      for line_number, line in enumerate(snake_case__	):
         UpperCamelCase    :  Optional[int]							=	line.strip()
         if line:
            UpperCamelCase    :  List[Any]							=	line.split()
            UpperCamelCase    :  Union[str, Any]							=	line_number
            UpperCamelCase    :  Any							=	words[0]
            UpperCamelCase    :  Union[str, Any]							=	value
   return result
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    str       ,     snake_case__       :    str       ,     snake_case__       :    List[str]       ,     snake_case__       :    int	)	->     Optional[Any]:
   for attribute in key.split('.'	):
      UpperCamelCase    :  List[Any]							=	getattr(snake_case__       ,     snake_case__	)

   UpperCamelCase    :  List[Any]							=	None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case__	):
         UpperCamelCase    :  str							=	PARAM_MAPPING[full_name.split('.'	)[-1]]
         UpperCamelCase    :  List[str]							=	'param'

   if weight_type is not None and weight_type != "param":
      UpperCamelCase    :  Optional[int]							=	getattr(snake_case__       ,     snake_case__	).shape
   elif weight_type is not None and weight_type == "param":
      UpperCamelCase    :  Any							=	hf_pointer
      for attribute in hf_param_name.split('.'	):
         UpperCamelCase    :  Union[str, Any]							=	getattr(snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Dict							=	shape_pointer.shape

      # let's reduce dimension
      UpperCamelCase    :  Any							=	value[0]
   else:
      UpperCamelCase    :  str							=	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":
      UpperCamelCase    :  List[str]							=	value
   elif weight_type == "weight_g":
      UpperCamelCase    :  List[str]							=	value
   elif weight_type == "weight_v":
      UpperCamelCase    :  Dict							=	value
   elif weight_type == "bias":
      UpperCamelCase    :  List[str]							=	value
   elif weight_type == "param":
      for attribute in hf_param_name.split('.'	):
         UpperCamelCase    :  Optional[int]							=	getattr(snake_case__       ,     snake_case__	)
      UpperCamelCase    :  Dict							=	value
   else:
      UpperCamelCase    :  List[str]							=	value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)
def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    Optional[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    str       ,     snake_case__       :    str	)	->     Union[str, Any]:
   UpperCamelCase    :  Tuple							=	None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case__	):
         UpperCamelCase    :  Tuple							=	PARAM_MAPPING[full_name.split('.'	)[-1]]
         UpperCamelCase    :  Any							=	'param'

   if weight_type is not None and weight_type != "param":
      UpperCamelCase    :  Tuple							=	'.'.join([key, weight_type]	)
   elif weight_type is not None and weight_type == "param":
      UpperCamelCase    :  Optional[int]							=	'.'.join([key, hf_param_name]	)
   else:
      UpperCamelCase    :  List[Any]							=	key

   UpperCamelCase    :  str							=	value if 'lm_head' in full_key else value[0]


__UpperCAmelCase          =       {
    '''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 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    List[Any]       ,     snake_case__       :    Any=None       ,     snake_case__       :    Union[str, Any]=None	)	->     Optional[int]:
   UpperCamelCase    :  Optional[int]							=	False
   for key, mapped_key in MAPPING.items():
      UpperCamelCase    :  Optional[Any]							=	'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]:
         UpperCamelCase    :  Optional[Any]							=	True
         if "*" in mapped_key:
            UpperCamelCase    :  Optional[Any]							=	name.split(snake_case__	)[0].split('.'	)[-2]
            UpperCamelCase    :  Optional[int]							=	mapped_key.replace('*'       ,     snake_case__	)
         if "weight_g" in name:
            UpperCamelCase    :  Union[str, Any]							=	'weight_g'
         elif "weight_v" in name:
            UpperCamelCase    :  List[str]							=	'weight_v'
         elif "bias" in name:
            UpperCamelCase    :  Optional[int]							=	'bias'
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            UpperCamelCase    :  Union[str, Any]							=	'weight'
         else:
            UpperCamelCase    :  Tuple							=	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 		UpperCamelCase	(   snake_case__       :    Dict       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    int	)	->     int:
   UpperCamelCase    :  Tuple							=	[]
   UpperCamelCase    :  List[str]							=	fairseq_model.state_dict()

   UpperCamelCase    :  Optional[Any]							=	hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      UpperCamelCase    :  Dict							=	False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case__       ,     snake_case__       ,     snake_case__       ,     snake_case__       ,     hf_model.config.feat_extract_norm == 'group'       ,     )
         UpperCamelCase    :  Union[str, Any]							=	True
      else:
         UpperCamelCase    :  Any							=	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 		UpperCamelCase	(   snake_case__       :    List[Any]       ,     snake_case__       :    str       ,     snake_case__       :    Optional[int]       ,     snake_case__       :    Tuple       ,     snake_case__       :    Tuple	)	->     int:
   UpperCamelCase    :  Dict							=	full_name.split('conv_layers.'	)[-1]
   UpperCamelCase    :  Union[str, Any]							=	name.split('.'	)
   UpperCamelCase    :  int							=	int(items[0]	)
   UpperCamelCase    :  str							=	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."""	)
         UpperCamelCase    :  Tuple							=	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."""	)
         UpperCamelCase    :  Union[str, 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:
         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."""	)
         UpperCamelCase    :  Dict							=	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."""	)
         UpperCamelCase    :  Union[str, Any]							=	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 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    int=None       ,     snake_case__       :    Union[str, Any]=None       ,     snake_case__       :    Optional[int]=True       ,     snake_case__       :    str=False	)	->     Union[str, Any]:
   if config_path is not None:
      UpperCamelCase    :  List[Any]							=	WavaVecaConfig.from_pretrained(snake_case__	)
   else:
      UpperCamelCase    :  Optional[Any]							=	WavaVecaConfig()

   if is_seq_class:
      UpperCamelCase    :  Any							=	read_txt_into_dict(snake_case__	)
      UpperCamelCase    :  int							=	idalabel
      UpperCamelCase    :  Tuple							=	WavaVecaForSequenceClassification(snake_case__	)
      UpperCamelCase    :  Optional[Any]							=	WavaVecaFeatureExtractor(
          feature_size=1       ,     sampling_rate=16000       ,     padding_value=0       ,     do_normalize=snake_case__       ,     return_attention_mask=snake_case__       ,     )
      feature_extractor.save_pretrained(snake_case__	)

   elif is_finetuned:
      if dict_path:
         UpperCamelCase    :  Dict							=	Dictionary.load(snake_case__	)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         UpperCamelCase    :  int							=	target_dict.pad_index
         UpperCamelCase    :  Optional[Any]							=	target_dict.bos_index
         UpperCamelCase    :  List[str]							=	target_dict.eos_index
         UpperCamelCase    :  Dict							=	len(target_dict.symbols	)
         UpperCamelCase    :  int							=	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__	)
         UpperCamelCase    :  Any							=	target_dict.indices

         # fairseq has the <pad> and <s> switched
         UpperCamelCase    :  Optional[Any]							=	0
         UpperCamelCase    :  Any							=	1
         with open(snake_case__       ,     'w'       ,     encoding='utf-8'	) as vocab_handle:
            json.dump(snake_case__       ,     snake_case__	)
         UpperCamelCase    :  Optional[int]							=	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__       ,     )
         UpperCamelCase    :  Dict							=	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=snake_case__       ,     return_attention_mask=snake_case__       ,     )
         UpperCamelCase    :  List[str]							=	WavaVecaProcessor(feature_extractor=snake_case__       ,     tokenizer=snake_case__	)
         processor.save_pretrained(snake_case__	)

      UpperCamelCase    :  Tuple							=	WavaVecaForCTC(snake_case__	)
   else:
      UpperCamelCase    :  Tuple							=	WavaVecaForPreTraining(snake_case__	)

   if is_finetuned or is_seq_class:
      UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  Optional[int]							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]       ,     arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
   else:
      UpperCamelCase    :  Union[str, Any]							=	argparse.Namespace(task='audio_pretraining'	)
      UpperCamelCase    :  Optional[Any]							=	fairseq.tasks.setup_task(snake_case__	)

      UpperCamelCase	,     UpperCamelCase	,     UpperCamelCase    :  List[str]							=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,     task=snake_case__	)

   UpperCamelCase    :  Union[str, Any]							=	model[0].eval()

   recursively_load_weights(snake_case__       ,     snake_case__       ,     not is_finetuned	)

   hf_wavavec.save_pretrained(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       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          =       parser.parse_args()

    __UpperCAmelCase          =       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,
    )








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


__UpperCAmelCase          =       logging.get_logger(__name__)

__UpperCAmelCase          =       {
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					Any        =  "canine"
       def __init__(			self,			SCREAMING_SNAKE_CASE_=768,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=12,			SCREAMING_SNAKE_CASE_=3072,			SCREAMING_SNAKE_CASE_="gelu",			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=0.1,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=16,			SCREAMING_SNAKE_CASE_=0.02,			SCREAMING_SNAKE_CASE_=1e-12,			SCREAMING_SNAKE_CASE_=0,			SCREAMING_SNAKE_CASE_=0XE000,			SCREAMING_SNAKE_CASE_=0XE001,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=4,			SCREAMING_SNAKE_CASE_=8,			SCREAMING_SNAKE_CASE_=1_6384,			SCREAMING_SNAKE_CASE_=128,			**SCREAMING_SNAKE_CASE_,			)     ->		int:
          super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_,			bos_token_id=SCREAMING_SNAKE_CASE_,			eos_token_id=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Tuple							=	max_position_embeddings
          UpperCamelCase    :  Tuple							=	hidden_size
          UpperCamelCase    :  Union[str, Any]							=	num_hidden_layers
          UpperCamelCase    :  Optional[int]							=	num_attention_heads
          UpperCamelCase    :  Tuple							=	intermediate_size
          UpperCamelCase    :  List[str]							=	hidden_act
          UpperCamelCase    :  Union[str, Any]							=	hidden_dropout_prob
          UpperCamelCase    :  Optional[int]							=	attention_probs_dropout_prob
          UpperCamelCase    :  Optional[Any]							=	initializer_range
          UpperCamelCase    :  Tuple							=	type_vocab_size
          UpperCamelCase    :  Any							=	layer_norm_eps

          # Character config:
          UpperCamelCase    :  List[Any]							=	downsampling_rate
          UpperCamelCase    :  Optional[int]							=	upsampling_kernel_size
          UpperCamelCase    :  Tuple							=	num_hash_functions
          UpperCamelCase    :  Union[str, Any]							=	num_hash_buckets
          UpperCamelCase    :  str							=	local_transformer_stride



