

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()
UpperCamelCase__		   =					logging.get_logger(__name__)

UpperCamelCase__		   =					{
    '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 	lowerCamelCase__			(   __A						:str  ,__A						:Union[str, Any]  ,__A						:Optional[int]  ,__A						:Any  ,__A						:int		):


       """simple docstring"""




       for attribute in key.split("""."""		):
              __snake_case					  =       getattr(UpperCamelCase__  ,UpperCamelCase__		)

       if weight_type is not None:
              __snake_case					  =       getattr(UpperCamelCase__  ,UpperCamelCase__		).shape
       else:
              __snake_case					  =       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":
              __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
       else:
              __snake_case					  =       value

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







def 	lowerCamelCase__			(   __A						:str  ,__A						:Optional[Any]  ,__A						:int		):


       """simple docstring"""




       __snake_case					  =       []
       __snake_case					  =       fairseq_model.state_dict()

       __snake_case					  =       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.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:
                     for key, mapped_key in MAPPING.items():
                            __snake_case					  =       '''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]:
                                   __snake_case					  =       True
                                   if "*" in mapped_key:
                                          __snake_case					  =       name.split(UpperCamelCase__		)[0].split("""."""		)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          __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 "weight" in name:
                                          __snake_case					  =       '''weight'''
                                   elif "bias" in name:
                                          __snake_case					  =       '''bias'''
                                   else:
                                          __snake_case					  =       None
                                   set_recursively(UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__		)
                            continue
              if not is_used:
                     unused_weights.append(UpperCamelCase__		)

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







def 	lowerCamelCase__			(   __A						:Tuple  ,__A						:Optional[Any]  ,__A						:Tuple  ,__A						:Any  ,__A						:str		):


       """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:
                     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.'
                     )
                     __snake_case					  =       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.'
                     )
                     __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:
                     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."
                     )
                     __snake_case					  =       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.'
                     )
                     __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__		)







def 	lowerCamelCase__			(   __A						:List[Any]  ,__A						:Any		):


       """simple docstring"""




       __snake_case					  =       SEWDConfig()
       if is_finetuned:
              __snake_case					  =       model.wav_encoder.wav_model.cfg
       else:
              __snake_case					  =       model.cfg

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

       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
              __snake_case					  =       model.cfg
              __snake_case					  =       fs_config.final_dropout
              __snake_case					  =       fs_config.layerdrop
       __snake_case					  =       fs_config.activation_dropout
       __snake_case					  =       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       __snake_case					  =       fs_config.attention_dropout
       __snake_case					  =       fs_config.dropout_input
       __snake_case					  =       fs_config.dropout
       __snake_case					  =       fs_config.mask_channel_length
       __snake_case					  =       fs_config.mask_channel_prob
       __snake_case					  =       fs_config.mask_length
       __snake_case					  =       fs_config.mask_prob

       __snake_case					  =       '''Wav2Vec2FeatureExtractor'''
       __snake_case					  =       '''Wav2Vec2CTCTokenizer'''

       return config







@torch.no_grad()
def 	lowerCamelCase__			(   __A						:str  ,__A						:int  ,__A						:Tuple=None  ,__A						:Any=None  ,__A						:Union[str, Any]=True		):


       """simple docstring"""




       if is_finetuned:
              __snake_case					  =       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]  ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
       else:
              __snake_case					  =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		)

       if config_path is not None:
              __snake_case					  =       SEWDConfig.from_pretrained(UpperCamelCase__		)
       else:
              __snake_case					  =       convert_config(model[0]  ,UpperCamelCase__		)
       __snake_case					  =       model[0].eval()

       __snake_case					  =       True if config.feat_extract_norm == '''layer''' else False
       __snake_case					  =       WavaVecaFeatureExtractor(
           feature_size=1  ,sampling_rate=1_6_0_0_0  ,padding_value=0  ,do_normalize=UpperCamelCase__  ,return_attention_mask=UpperCamelCase__  ,)

       if 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.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__		)
                     with open(UpperCamelCase__  ,"""w"""  ,encoding="""utf-8"""		) as vocab_handle:
                            json.dump(target_dict.indices  ,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					  =       WavaVecaProcessor(feature_extractor=UpperCamelCase__  ,tokenizer=UpperCamelCase__		)
                     processor.save_pretrained(UpperCamelCase__		)

              __snake_case					  =       SEWDForCTC(UpperCamelCase__		)
       else:
              __snake_case					  =       SEWDModel(UpperCamelCase__		)
              feature_extractor.save_pretrained(UpperCamelCase__		)

       recursively_load_weights(UpperCamelCase__  ,UpperCamelCase__  ,UpperCamelCase__		)

       hf_model.save_pretrained(UpperCamelCase__		)


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




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__		   =    logging.get_logger(__name__)

class 						__snake_case						(							snake_case__     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       ["input_values", "attention_mask"]


       def __init__(		self			,   _UpperCamelCase=1			,   _UpperCamelCase=1_60_00			,   _UpperCamelCase=0.0			,   _UpperCamelCase=False			,   _UpperCamelCase=True			,   **_UpperCamelCase			,   )			->       List[Any]:






              """simple docstring"""


              super().__init__(feature_size=_UpperCamelCase			,   sampling_rate=_UpperCamelCase			,   padding_value=_UpperCamelCase			,   **_UpperCamelCase      )
              __snake_case					  =       return_attention_mask
              __snake_case					  =       do_normalize


       @staticmethod
       def  a		(		_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase = 0.0      )			->       List[np.ndarray]:






              """simple docstring"""


              if attention_mask is not None:
                     __snake_case					  =       np.array(_UpperCamelCase			,   np.intaa      )
                     __snake_case					  =       []

                     for vector, length in zip(_UpperCamelCase			,   attention_mask.sum(-1      )      ):
                            __snake_case					  =       (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7      )
                            if length < normed_slice.shape[0]:
                                   __snake_case					  =       padding_value

                            normed_input_values.append(_UpperCamelCase      )
              else:
                     __snake_case					  =       [(x - x.mean()) / np.sqrt(x.var() + 1E-7      ) for x in input_values]

              return normed_input_values






       def __call__(		self			,   _UpperCamelCase			,   _UpperCamelCase = False			,   _UpperCamelCase = None			,   _UpperCamelCase = False			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   _UpperCamelCase = None			,   **_UpperCamelCase			,   )			->       BatchFeature:






              """simple docstring"""


              if sampling_rate is not None:
                     if sampling_rate != self.sampling_rate:
                            raise ValueError(
                                F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                                F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
                                F' {self.sampling_rate} and not {sampling_rate}.'      )
              else:
                     logger.warning(
                         """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
                         """Failing to do so can result in silent errors that might be hard to debug."""      )

              __snake_case					  =       isinstance(_UpperCamelCase			,   np.ndarray      ) and len(raw_speech.shape      ) > 1
              if is_batched_numpy and len(raw_speech.shape      ) > 2:
                     raise ValueError(F'Only mono-channel audio is supported for input to {self}'      )
              __snake_case					  =       is_batched_numpy or (
                  isinstance(_UpperCamelCase			,   (list, tuple)      ) and (isinstance(raw_speech[0]			,   (np.ndarray, tuple, list)      ))
              )

              # always return batch
              if not is_batched:
                     __snake_case					  =       [raw_speech]

              # convert into correct format for padding
              __snake_case					  =       BatchFeature({"""input_values""": raw_speech}      )

              __snake_case					  =       self.pad(
                  _UpperCamelCase			,   padding=_UpperCamelCase			,   max_length=_UpperCamelCase			,   truncation=_UpperCamelCase			,   pad_to_multiple_of=_UpperCamelCase			,   return_attention_mask=_UpperCamelCase			,   )

              # convert input values to correct format
              __snake_case					  =       padded_inputs["""input_values"""]
              if not isinstance(input_values[0]			,   np.ndarray      ):
                     __snake_case					  =       [np.asarray(_UpperCamelCase			,   dtype=np.floataa      ) for array in input_values]
              elif (
                  not isinstance(_UpperCamelCase			,   np.ndarray      )
                  and isinstance(input_values[0]			,   np.ndarray      )
                  and input_values[0].dtype is np.dtype(np.floataa      )
              ):
                     __snake_case					  =       [array.astype(np.floataa      ) for array in input_values]
              elif isinstance(_UpperCamelCase			,   np.ndarray      ) and input_values.dtype is np.dtype(np.floataa      ):
                     __snake_case					  =       input_values.astype(np.floataa      )

              # convert attention_mask to correct format
              __snake_case					  =       padded_inputs.get("""attention_mask"""      )
              if attention_mask is not None:
                     __snake_case					  =       [np.asarray(_UpperCamelCase			,   dtype=np.intaa      ) for array in attention_mask]

              # zero-mean and unit-variance normalization
              if self.do_normalize:
                     __snake_case					  =       (
                         attention_mask
                         if self._get_padding_strategies(_UpperCamelCase			,   max_length=_UpperCamelCase      ) is not PaddingStrategy.DO_NOT_PAD
                         else None
                     )
                     __snake_case					  =       self.zero_mean_unit_var_norm(
                         padded_inputs["""input_values"""]			,   attention_mask=_UpperCamelCase			,   padding_value=self.padding_value      )

              if return_tensors is not None:
                     __snake_case					  =       padded_inputs.convert_to_tensors(_UpperCamelCase      )

              return padded_inputs

