


'''simple docstring'''





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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       str						       =				{
    'post_extract_proj': 'feature_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.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	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				Any    , SCREAMING_SNAKE_CASE	:				Dict    , SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



 for attribute in key.split('''.'''     ):
  UpperCamelCase__					:    Dict          =   getattr(_SCREAMING_SNAKE_CASE    , _SCREAMING_SNAKE_CASE     )

 if weight_type is not None:
  UpperCamelCase__					:    str          =   getattr(_SCREAMING_SNAKE_CASE    , _SCREAMING_SNAKE_CASE     ).shape
 else:
  UpperCamelCase__					:    Optional[Any]          =   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":
  UpperCamelCase__					:    str          =   value
 elif weight_type == "weight_g":
  UpperCamelCase__					:    Optional[Any]          =   value
 elif weight_type == "weight_v":
  UpperCamelCase__					:    Optional[Any]          =   value
 elif weight_type == "bias":
  UpperCamelCase__					:    Optional[Any]          =   value
 else:
  UpperCamelCase__					:    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	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				Tuple     ):







 """simple docstring"""



 UpperCamelCase__					:    Dict          =   []
 UpperCamelCase__					:    Dict          =   fairseq_model.state_dict()

 UpperCamelCase__					:    Union[str, Any]          =   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCamelCase__					:    Tuple          =   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'''    , )
   UpperCamelCase__					:    str          =   True
  else:
   for key, mapped_key in MAPPING.items():
    UpperCamelCase__					:    Any          =   '''sew.''' + 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]:
     UpperCamelCase__					:    Tuple          =   True
     if "*" in mapped_key:
      UpperCamelCase__					:    Dict          =   name.split(_SCREAMING_SNAKE_CASE     )[0].split('''.'''     )[-2]
      UpperCamelCase__					:    Tuple          =   mapped_key.replace('''*'''    , _SCREAMING_SNAKE_CASE     )
     if "weight_g" in name:
      UpperCamelCase__					:    List[Any]          =   '''weight_g'''
     elif "weight_v" in name:
      UpperCamelCase__					:    int          =   '''weight_v'''
     elif "weight" in name:
      UpperCamelCase__					:    Dict          =   '''weight'''
     elif "bias" in name:
      UpperCamelCase__					:    Any          =   '''bias'''
     else:
      UpperCamelCase__					:    Optional[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	:				str    , SCREAMING_SNAKE_CASE	:				Optional[Any]    , SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				List[str]    , SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 UpperCamelCase__					:    str          =   full_name.split('''conv_layers.'''     )[-1]
 UpperCamelCase__					:    str          =   name.split('''.'''     )
 UpperCamelCase__					:    Optional[Any]          =   int(items[0]     )
 UpperCamelCase__					:    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."
   )
   UpperCamelCase__					:    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."
   )
   UpperCamelCase__					:    List[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."
   )
   UpperCamelCase__					:    Optional[Any]          =   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."
   )
   UpperCamelCase__					:    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	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				Optional[int]     ):







 """simple docstring"""



 UpperCamelCase__					:    Any          =   SEWConfig()
 if is_finetuned:
  UpperCamelCase__					:    Optional[Any]          =   model.wav_encoder.wav_model.cfg
 else:
  UpperCamelCase__					:    Optional[int]          =   model.cfg

 UpperCamelCase__					:    Optional[Any]          =   fs_config.conv_bias
 UpperCamelCase__					:    List[str]          =   eval(fs_config.conv_feature_layers     )
 UpperCamelCase__					:    List[str]          =   [x[0] for x in conv_layers]
 UpperCamelCase__					:    Optional[Any]          =   [x[1] for x in conv_layers]
 UpperCamelCase__					:    str          =   [x[2] for x in conv_layers]
 UpperCamelCase__					:    List[str]          =   '''gelu'''
 UpperCamelCase__					:    Any          =   '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
 UpperCamelCase__					:    Optional[Any]          =   0.0
 UpperCamelCase__					:    Optional[int]          =   fs_config.activation_fn.name
 UpperCamelCase__					:    Tuple          =   fs_config.encoder_embed_dim
 UpperCamelCase__					:    List[Any]          =   0.02
 UpperCamelCase__					:    Optional[int]          =   fs_config.encoder_ffn_embed_dim
 UpperCamelCase__					:    Optional[Any]          =   1E-5
 UpperCamelCase__					:    str          =   fs_config.encoder_layerdrop
 UpperCamelCase__					:    List[str]          =   fs_config.encoder_attention_heads
 UpperCamelCase__					:    Optional[Any]          =   fs_config.conv_pos_groups
 UpperCamelCase__					:    Tuple          =   fs_config.conv_pos
 UpperCamelCase__					:    Union[str, Any]          =   len(_SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Tuple          =   fs_config.encoder_layers
 UpperCamelCase__					:    str          =   fs_config.squeeze_factor

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

 UpperCamelCase__					:    Any          =   '''Wav2Vec2FeatureExtractor'''
 UpperCamelCase__					:    str          =   '''Wav2Vec2CTCTokenizer'''

 return config

@torch.no_grad()
def 			_a  (				SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				int    , SCREAMING_SNAKE_CASE	:				List[Any]=None    , SCREAMING_SNAKE_CASE	:				Optional[Any]=None    , SCREAMING_SNAKE_CASE	:				int=True     ):







 """simple docstring"""



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

 if config_path is not None:
  UpperCamelCase__					:    Optional[int]          =   SEWConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    Tuple          =   convert_config(model[0]    , _SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Optional[Any]          =   model[0].eval()

 UpperCamelCase__					:    int          =   True if config.feat_extract_norm == '''layer''' else False
 UpperCamelCase__					:    int          =   WavaVecaFeatureExtractor(
     feature_size=1    , sampling_rate=16000    , padding_value=0    , do_normalize=_SCREAMING_SNAKE_CASE    , return_attention_mask=_SCREAMING_SNAKE_CASE    , )

 if is_finetuned:
  if dict_path:
   UpperCamelCase__					:    Union[str, Any]          =   Dictionary.load(_SCREAMING_SNAKE_CASE     )

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   UpperCamelCase__					:    Union[str, Any]          =   target_dict.pad_index
   UpperCamelCase__					:    Optional[int]          =   target_dict.bos_index
   UpperCamelCase__					:    Optional[Any]          =   target_dict.pad_index
   UpperCamelCase__					:    Union[str, Any]          =   target_dict.bos_index
   UpperCamelCase__					:    Optional[Any]          =   target_dict.eos_index
   UpperCamelCase__					:    Tuple          =   len(target_dict.symbols     )
   UpperCamelCase__					:    Tuple          =   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     )
   UpperCamelCase__					:    str          =   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    , )
   UpperCamelCase__					:    Optional[int]          =   WavaVecaProcessor(feature_extractor=_SCREAMING_SNAKE_CASE    , tokenizer=_SCREAMING_SNAKE_CASE     )
   processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

  UpperCamelCase__					:    List[Any]          =   SEWForCTC(_SCREAMING_SNAKE_CASE     )
 else:
  UpperCamelCase__					:    List[Any]          =   SEWModel(_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__":
       __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(
           "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
       )
       __UpperCamelCase       :       Any						       =				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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase       :       Dict						       =				{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       str						       =				["MobileViTFeatureExtractor"]
       __UpperCamelCase       :       Optional[Any]						       =				["MobileViTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Union[str, Any]						       =				[
           "MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MobileViTForImageClassification",
           "MobileViTForSemanticSegmentation",
           "MobileViTModel",
           "MobileViTPreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __UpperCamelCase       :       Optional[int]						       =				[
           "TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFMobileViTForImageClassification",
           "TFMobileViTForSemanticSegmentation",
           "TFMobileViTModel",
           "TFMobileViTPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_mobilevit import MobileViTFeatureExtractor
              from .image_processing_mobilevit import MobileViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mobilevit import (
                  MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MobileViTForImageClassification,
                  MobileViTForSemanticSegmentation,
                  MobileViTModel,
                  MobileViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mobilevit import (
                  TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFMobileViTForImageClassification,
                  TFMobileViTForSemanticSegmentation,
                  TFMobileViTModel,
                  TFMobileViTPreTrainedModel,
              )


else:
       import sys

       __UpperCamelCase       :       str						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


