






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowerCamelCase      :       Any    =						{
    '''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''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}





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

     if weight_type is not None:
          __lowerCAmelCase             =       getattr(__snake_case       ,			__snake_case							).shape
     else:
          __lowerCAmelCase             =       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             =       value
     elif weight_type == "weight_g":
          __lowerCAmelCase             =       value
     elif weight_type == "weight_v":
          __lowerCAmelCase             =       value
     elif weight_type == "bias":
          __lowerCAmelCase             =       value
     else:
          __lowerCAmelCase             =       value

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       []
     __lowerCAmelCase             =       fairseq_model.state_dict()

     __lowerCAmelCase             =       hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          __lowerCAmelCase             =       False
          if "conv_layers" in name:
               load_conv_layer(
                   __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			hf_model.config.feat_extract_norm == "group"       ,			)
               __lowerCAmelCase             =       True
          else:
               for key, mapped_key in MAPPING.items():
                    __lowerCAmelCase             =       "hubert." + 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] and not is_finetuned):
                         __lowerCAmelCase             =       True
                         if "*" in mapped_key:
                              __lowerCAmelCase             =       name.split(__snake_case							)[0].split("."							)[-2]
                              __lowerCAmelCase             =       mapped_key.replace("*"       ,			__snake_case							)
                         if "weight_g" in name:
                              __lowerCAmelCase             =       "weight_g"
                         elif "weight_v" in name:
                              __lowerCAmelCase             =       "weight_v"
                         elif "weight" in name:
                              __lowerCAmelCase             =       "weight"
                         elif "bias" in name:
                              __lowerCAmelCase             =       "bias"
                         else:
                              __lowerCAmelCase             =       None
                         set_recursively(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
                    continue
          if not is_used:
               unused_weights.append(__snake_case							)

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       full_name.split("conv_layers."							)[-1]
     __lowerCAmelCase             =       name.split("."							)
     __lowerCAmelCase             =       int(items[0]							)
     __lowerCAmelCase             =       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             =       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             =       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             =       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             =       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    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=True							):
     if config_path is not None:
          __lowerCAmelCase             =       HubertConfig.from_pretrained(__snake_case							)
     else:
          __lowerCAmelCase             =       HubertConfig()

     if is_finetuned:
          if dict_path:
               __lowerCAmelCase             =       Dictionary.load(__snake_case							)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               __lowerCAmelCase             =       target_dict.pad_index
               __lowerCAmelCase             =       target_dict.bos_index
               __lowerCAmelCase             =       target_dict.eos_index
               __lowerCAmelCase             =       len(target_dict.symbols							)
               __lowerCAmelCase             =       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							)
               with open(__snake_case       ,			"w"       ,			encoding="utf-8"							) as vocab_handle:
                    json.dump(target_dict.indices       ,			__snake_case							)
               __lowerCAmelCase             =       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       ,			)
               __lowerCAmelCase             =       True if config.feat_extract_norm == "layer" else False
               __lowerCAmelCase             =       WavaVecaFeatureExtractor(
                   feature_size=1       ,			sampling_rate=1_6000       ,			padding_value=0       ,			do_normalize=__snake_case       ,			return_attention_mask=__snake_case       ,			)
               __lowerCAmelCase             =       WavaVecaProcessor(feature_extractor=__snake_case       ,			tokenizer=__snake_case							)
               processor.save_pretrained(__snake_case							)

          __lowerCAmelCase             =       HubertForCTC(__snake_case							)
     else:
          __lowerCAmelCase             =       HubertModel(__snake_case							)

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

     __lowerCAmelCase             =       model[0].eval()

     recursively_load_weights(__snake_case       ,			__snake_case       ,			__snake_case							)

     hf_wavavec.save_pretrained(__snake_case							)


if __name__ == "__main__":
 lowerCamelCase      :       Union[str, 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(
     '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
 )
 lowerCamelCase      :       Optional[Any]    =						parser.parse_args()
 convert_hubert_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )














from typing import TYPE_CHECKING

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


lowerCamelCase      :       List[Any]    =						{
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[Any]    =						['''OwlViTFeatureExtractor''']
 lowerCamelCase      :       str    =						['''OwlViTImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase      :       List[str]    =						[
     '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''OwlViTModel''',
     '''OwlViTPreTrainedModel''',
     '''OwlViTTextModel''',
     '''OwlViTVisionModel''',
     '''OwlViTForObjectDetection''',
 ]

if TYPE_CHECKING:
 from .configuration_owlvit import (
     OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     OwlViTConfig,
     OwlViTOnnxConfig,
     OwlViTTextConfig,
     OwlViTVisionConfig,
 )
 from .processing_owlvit import OwlViTProcessor

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_owlvit import OwlViTFeatureExtractor
  from .image_processing_owlvit import OwlViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_owlvit import (
      OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      OwlViTForObjectDetection,
      OwlViTModel,
      OwlViTPreTrainedModel,
      OwlViTTextModel,
      OwlViTVisionModel,
  )

else:
 import sys

 lowerCamelCase      :       List[str]    =						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






