


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()
_SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE    :				Dict       =					{
    "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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):



     """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """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     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):



     """simple docstring"""






     if is_finetuned:
          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				,						snake_case				,						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_60_00							,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__":
 _SCREAMING_SNAKE_CASE    :				List[Any]       =					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"
 )
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )








from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    "cross_attention",
    "0.20.0",
    "Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.",
    standard_warn=False,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					AttentionProcessor

class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)






class 						A__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      *__snake_case    ,      **__snake_case					):
         snake_case					=       F'''{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"					)					)} instead.'''
         deprecate('''cross_attention'''    ,      '''0.20.0'''    ,      __snake_case    ,      standard_warn=__snake_case					)
         super().__init__(*__snake_case    ,      **__snake_case					)




