


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase						 =	{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}




def      a_				(   SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Dict		,      SCREAMING_SNAKE_CASE__ :				Dict					):
       '''simple docstring'''





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

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

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




def      a_				(   SCREAMING_SNAKE_CASE__ :				int		,      SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =[]
       _lowerCamelCase	:		Any          =fairseq_model.state_dict()

       _lowerCamelCase	:		Optional[Any]          =hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _lowerCamelCase	:		Optional[Any]          =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'		,      )
                     _lowerCamelCase	:		Optional[int]          =True
              else:
                     for key, mapped_key in MAPPING.items():
                            _lowerCamelCase	:		str          ='sew_d.' + 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]:
                                   _lowerCamelCase	:		str          =True
                                   if "*" in mapped_key:
                                          _lowerCamelCase	:		Dict          =name.split(SCREAMING_SNAKE_CASE__					)[0].split('.'					)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          _lowerCamelCase	:		str          =mapped_key.replace('*'		,      SCREAMING_SNAKE_CASE__					)
                                   if "weight_g" in name:
                                          _lowerCamelCase	:		Tuple          ='weight_g'
                                   elif "weight_v" in name:
                                          _lowerCamelCase	:		List[Any]          ='weight_v'
                                   elif "weight" in name:
                                          _lowerCamelCase	:		Any          ='weight'
                                   elif "bias" in name:
                                          _lowerCamelCase	:		str          ='bias'
                                   else:
                                          _lowerCamelCase	:		Any          =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      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]		,      SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				int		,      SCREAMING_SNAKE_CASE__ :				List[str]					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          =full_name.split('conv_layers.'					)[-1]
       _lowerCamelCase	:		Tuple          =name.split('.'					)
       _lowerCamelCase	:		Union[str, Any]          =int(items[0]					)
       _lowerCamelCase	:		Optional[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.'''
                     )
                     _lowerCamelCase	:		List[Any]          =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.'''
                     )
                     _lowerCamelCase	:		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."
                     )
                     _lowerCamelCase	:		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.'''
                     )
                     _lowerCamelCase	:		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      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[int]          =SEWDConfig()
       if is_finetuned:
              _lowerCamelCase	:		List[Any]          =model.wav_encoder.wav_model.cfg
       else:
              _lowerCamelCase	:		List[Any]          =model.cfg

       _lowerCamelCase	:		List[Any]          =fs_config.conv_bias
       _lowerCamelCase	:		int          =eval(fs_config.conv_feature_layers					)
       _lowerCamelCase	:		int          =[x[0] for x in conv_layers]
       _lowerCamelCase	:		List[Any]          =[x[1] for x in conv_layers]
       _lowerCamelCase	:		Dict          =[x[2] for x in conv_layers]
       _lowerCamelCase	:		List[Any]          ='gelu'
       _lowerCamelCase	:		Optional[int]          ='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       _lowerCamelCase	:		List[str]          =0.0
       _lowerCamelCase	:		int          =fs_config.activation_fn.name
       _lowerCamelCase	:		Tuple          =fs_config.encoder_embed_dim
       _lowerCamelCase	:		List[Any]          =0.02
       _lowerCamelCase	:		Dict          =fs_config.encoder_ffn_embed_dim
       _lowerCamelCase	:		Optional[int]          =1e-5
       _lowerCamelCase	:		Dict          =fs_config.encoder_layerdrop
       _lowerCamelCase	:		Any          =fs_config.encoder_attention_heads
       _lowerCamelCase	:		int          =fs_config.conv_pos_groups
       _lowerCamelCase	:		Any          =fs_config.conv_pos
       _lowerCamelCase	:		List[Any]          =len(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Any          =fs_config.encoder_layers
       _lowerCamelCase	:		Optional[Any]          =fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       _lowerCamelCase	:		Dict          =fs_config.max_position_embeddings
       _lowerCamelCase	:		Union[str, Any]          =fs_config.position_buckets
       _lowerCamelCase	:		str          =fs_config.share_att_key
       _lowerCamelCase	:		Dict          =fs_config.relative_attention
       _lowerCamelCase	:		Any          =fs_config.position_biased_input
       _lowerCamelCase	:		str          =tuple(fs_config.pos_att_type.split('|'					)					)
       _lowerCamelCase	:		Union[str, Any]          =fs_config.norm_rel_ebd

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              _lowerCamelCase	:		Optional[int]          =model.cfg
              _lowerCamelCase	:		List[str]          =fs_config.final_dropout
              _lowerCamelCase	:		List[Any]          =fs_config.layerdrop
       _lowerCamelCase	:		str          =fs_config.activation_dropout
       _lowerCamelCase	:		Union[str, Any]          =fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       _lowerCamelCase	:		Optional[int]          =fs_config.attention_dropout
       _lowerCamelCase	:		Optional[Any]          =fs_config.dropout_input
       _lowerCamelCase	:		Tuple          =fs_config.dropout
       _lowerCamelCase	:		Optional[int]          =fs_config.mask_channel_length
       _lowerCamelCase	:		Dict          =fs_config.mask_channel_prob
       _lowerCamelCase	:		int          =fs_config.mask_length
       _lowerCamelCase	:		Dict          =fs_config.mask_prob

       _lowerCamelCase	:		Dict          ='Wav2Vec2FeatureExtractor'
       _lowerCamelCase	:		List[Any]          ='Wav2Vec2CTCTokenizer'

       return config




@torch.no_grad()
def      a_				(   SCREAMING_SNAKE_CASE__ :				List[str]		,      SCREAMING_SNAKE_CASE__ :				Optional[int]		,      SCREAMING_SNAKE_CASE__ :				Any=None		,      SCREAMING_SNAKE_CASE__ :				Union[str, Any]=None		,      SCREAMING_SNAKE_CASE__ :				Tuple=True					):
       '''simple docstring'''





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

       if config_path is not None:
              _lowerCamelCase	:		List[str]          =SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE__					)
       else:
              _lowerCamelCase	:		Tuple          =convert_config(model[0]		,      SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[str]          =model[0].eval()

       _lowerCamelCase	:		List[Any]          =True if config.feat_extract_norm == 'layer' else False
       _lowerCamelCase	:		Optional[int]          =WavaVecaFeatureExtractor(
           feature_size=1		,      sampling_rate=16_000		,      padding_value=0		,      do_normalize=SCREAMING_SNAKE_CASE__		,      return_attention_mask=SCREAMING_SNAKE_CASE__		,      )

       if is_finetuned:
              if dict_path:
                     _lowerCamelCase	:		str          =Dictionary.load(SCREAMING_SNAKE_CASE__					)

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     _lowerCamelCase	:		int          =target_dict.pad_index
                     _lowerCamelCase	:		Any          =target_dict.bos_index
                     _lowerCamelCase	:		Union[str, Any]          =target_dict.pad_index
                     _lowerCamelCase	:		Optional[int]          =target_dict.bos_index
                     _lowerCamelCase	:		Union[str, Any]          =target_dict.eos_index
                     _lowerCamelCase	:		List[str]          =len(target_dict.symbols					)
                     _lowerCamelCase	:		Union[str, Any]          =os.path.join(SCREAMING_SNAKE_CASE__		,      'vocab.json'					)
                     if not os.path.isdir(SCREAMING_SNAKE_CASE__					):
                            logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE__					)					)
                            return
                     os.makedirs(SCREAMING_SNAKE_CASE__		,      exist_ok=SCREAMING_SNAKE_CASE__					)
                     with open(SCREAMING_SNAKE_CASE__		,      'w'		,      encoding='utf-8'					) as vocab_handle:
                            json.dump(target_dict.indices		,      SCREAMING_SNAKE_CASE__					)
                     _lowerCamelCase	:		Optional[Any]          =WavaVecaCTCTokenizer(
                         SCREAMING_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=SCREAMING_SNAKE_CASE__		,      )
                     _lowerCamelCase	:		Optional[int]          =WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,      tokenizer=SCREAMING_SNAKE_CASE__					)
                     processor.save_pretrained(SCREAMING_SNAKE_CASE__					)

              _lowerCamelCase	:		Any          =SEWDForCTC(SCREAMING_SNAKE_CASE__					)
       else:
              _lowerCamelCase	:		List[Any]          =SEWDModel(SCREAMING_SNAKE_CASE__					)
              feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__					)

       recursively_load_weights(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)

       hf_model.save_pretrained(SCREAMING_SNAKE_CASE__					)


if __name__ == "__main__":
 lowerCamelCase						 =	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(
     '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 lowerCamelCase						 =	parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =np.arange(start=3		,      stop=SCREAMING_SNAKE_CASE__		,      step=4					).tolist() if k > 0 else [10]
       _lowerCamelCase	:		List[Any]          =_quantize(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       # count number of elements per group
       _lowerCamelCase	:		Union[str, Any]          =np.unique(SCREAMING_SNAKE_CASE__		,      return_counts=SCREAMING_SNAKE_CASE__					)[1]
       _lowerCamelCase	:		int          =[0] + bins + [np.inf]
       logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE__					)					)
       logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE__					)					)
       return groups




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =self.group_ids[idx]
                    buffer_per_group[group_id].append(lowercase_			)
                    samples_per_group[group_id].append(lowercase_			)
                    if len(buffer_per_group[group_id]			) == self.batch_size:
                           yield buffer_per_group[group_id]  # TODO
                           num_batches += 1
                           del buffer_per_group[group_id]
                    assert len(buffer_per_group[group_id]			) < self.batch_size

             # now we have run out of elements that satisfy
             # the group criteria, let's return the remaining
             # elements so that the size of the sampler is
             # deterministic
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =batch_idx[self.batch_size :]
                                  num_remaining -= 1
                    if len(lowercase_			) > 0:
                           yield batch_idx
                           num_remaining -= 1
             assert num_remaining == 0






      def __len__( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




             return (len(self.sampler			) + self.batch_size - 1) // self.batch_size


