




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()
__a          =			logging.get_logger(__name__)

__a          =			{
    '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__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     for attribute in key.split('''.'''				):
          UpperCAmelCase_      :							Union[str, Any]       =      getattr(_lowercase			,						_lowercase				)

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

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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      []
     UpperCAmelCase_      :							int       =      fairseq_model.state_dict()

     UpperCAmelCase_      :							Union[str, Any]       =      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_      :							int       =      False
          if "conv_layers" in name:
               load_conv_layer(
                   _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						hf_model.config.feat_extract_norm == '''group'''			,						)
               UpperCAmelCase_      :							Optional[Any]       =      True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_      :							Dict       =      '''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):
                         UpperCAmelCase_      :							List[Any]       =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_      :							str       =      name.split(_lowercase				)[0].split('''.'''				)[-2]
                              UpperCAmelCase_      :							Optional[Any]       =      mapped_key.replace('''*'''			,						_lowercase				)
                         if "weight_g" in name:
                              UpperCAmelCase_      :							Tuple       =      '''weight_g'''
                         elif "weight_v" in name:
                              UpperCAmelCase_      :							str       =      '''weight_v'''
                         elif "weight" in name:
                              UpperCAmelCase_      :							str       =      '''weight'''
                         elif "bias" in name:
                              UpperCAmelCase_      :							Any       =      '''bias'''
                         else:
                              UpperCAmelCase_      :							Optional[Any]       =      None
                         set_recursively(_lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				)
                    continue
          if not is_used:
               unused_weights.append(_lowercase				)

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







def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase			,						_lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      full_name.split('''conv_layers.'''				)[-1]
     UpperCAmelCase_      :							Union[str, Any]       =      name.split('''.'''				)
     UpperCAmelCase_      :							Tuple       =      int(items[0]				)
     UpperCAmelCase_      :							List[Any]       =      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_      :							Tuple       =      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_      :							str       =      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_      :							Tuple       =      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_      :							Optional[Any]       =      value
               logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''				)
     else:
          unused_weights.append(_lowercase				)







@torch.no_grad()
def lowerCamelCase__       (      _lowercase			,						_lowercase			,						_lowercase=None			,						_lowercase=None			,						_lowercase=True				):

     '''simple docstring'''


     if config_path is not None:
          UpperCAmelCase_      :							Dict       =      HubertConfig.from_pretrained(_lowercase				)
     else:
          UpperCAmelCase_      :							Optional[int]       =      HubertConfig()

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_      :							str       =      Dictionary.load(_lowercase				)

               # 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_      :							Any       =      target_dict.bos_index
               UpperCAmelCase_      :							Tuple       =      target_dict.eos_index
               UpperCAmelCase_      :							Optional[int]       =      len(target_dict.symbols				)
               UpperCAmelCase_      :							Any       =      os.path.join(_lowercase			,						'''vocab.json'''				)
               if not os.path.isdir(_lowercase				):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowercase				)				)
                    return
               os.makedirs(_lowercase			,						exist_ok=_lowercase				)
               with open(_lowercase			,						'''w'''			,						encoding='''utf-8'''				) as vocab_handle:
                    json.dump(target_dict.indices			,						_lowercase				)
               UpperCAmelCase_      :							List[Any]       =      WavaVecaCTCTokenizer(
                   _lowercase			,						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=_lowercase			,						)
               UpperCAmelCase_      :							int       =      True if config.feat_extract_norm == '''layer''' else False
               UpperCAmelCase_      :							Union[str, Any]       =      WavaVecaFeatureExtractor(
                   feature_size=1			,						sampling_rate=16000			,						padding_value=0			,						do_normalize=_lowercase			,						return_attention_mask=_lowercase			,						)
               UpperCAmelCase_      :							Dict       =      WavaVecaProcessor(feature_extractor=_lowercase			,						tokenizer=_lowercase				)
               processor.save_pretrained(_lowercase				)

          UpperCAmelCase_      :							Union[str, Any]       =      HubertForCTC(_lowercase				)
     else:
          UpperCAmelCase_      :							str       =      HubertModel(_lowercase				)

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

     UpperCAmelCase_      :							str       =      model[0].eval()

     recursively_load_weights(_lowercase			,						_lowercase			,						_lowercase				)

     hf_wavavec.save_pretrained(_lowercase				)


if __name__ == "__main__":
     __a          =			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'
     )
     __a          =			parser.parse_args()
     convert_hubert_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
     import PIL.Image

     from .features import FeatureType


__a          =			None
__a          =			'<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__a          =			[
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				True
    lowerCAmelCase        =				None
    # Automatically constructed
    lowerCAmelCase        =				"PIL.Image.Image"
    lowerCAmelCase        =				pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}       )
    lowerCAmelCase        =				field(default='''Image'''    ,						init=_a    ,						repr=_a       )





    def __call__(				self  )			->		Optional[Any]:
         return self.pa_type





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		dict:
         if config.PIL_AVAILABLE:
              import PIL.Image
         else:
              raise ImportError('''To support encoding images, please install \'Pillow\'.'''  )

         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Optional[int]       =      np.array(_SCREAMING_SNAKE_CASE  )

         if isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              return {"path": value, "bytes": None}
         elif isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              return {"path": None, "bytes": value}
         elif isinstance(_SCREAMING_SNAKE_CASE       ,np.ndarray  ):
              # convert the image array to PNG/TIFF bytes
              return encode_np_array(_SCREAMING_SNAKE_CASE  )
         elif isinstance(_SCREAMING_SNAKE_CASE       ,PIL.Image.Image  ):
              # convert the PIL image to bytes (default format is PNG/TIFF)
              return encode_pil_image(_SCREAMING_SNAKE_CASE  )
         elif value.get('''path'''  ) is not None and os.path.isfile(value['''path''']  ):
              # we set "bytes": None to not duplicate the data if they're already available locally
              return {"bytes": None, "path": value.get('''path'''  )}
         elif value.get('''bytes'''  ) is not None or value.get('''path'''  ) is not None:
              # store the image bytes, and path is used to infer the image format using the file extension
              return {"bytes": value.get('''bytes'''  ), "path": value.get('''path'''  )}
         else:
              raise ValueError(
                  f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=None  )			->		"PIL.Image.Image":
         if not self.decode:
              raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''  )

         if config.PIL_AVAILABLE:
              import PIL.Image
         else:
              raise ImportError('''To support decoding images, please install \'Pillow\'.'''  )

         if token_per_repo_id is None:
              UpperCAmelCase_      :							str       =      {}

         UpperCAmelCase_,   UpperCAmelCase_      :							List[str]       =      value['''path'''], value['''bytes''']
         if bytes_ is None:
              if path is None:
                   raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''  )
              else:
                   if is_local_path(_SCREAMING_SNAKE_CASE  ):
                        UpperCAmelCase_      :							int       =      PIL.Image.open(_SCREAMING_SNAKE_CASE  )
                   else:
                        UpperCAmelCase_      :							int       =      path.split('''::'''  )[-1]
                        try:
                             UpperCAmelCase_      :							str       =      string_to_dict(_SCREAMING_SNAKE_CASE       ,config.HUB_DATASETS_URL  )['''repo_id''']
                             UpperCAmelCase_      :							Optional[Any]       =      token_per_repo_id.get(_SCREAMING_SNAKE_CASE  )
                        except ValueError:
                             UpperCAmelCase_      :							Any       =      None
                        with xopen(_SCREAMING_SNAKE_CASE       ,'''rb'''       ,use_auth_token=_SCREAMING_SNAKE_CASE  ) as f:
                             UpperCAmelCase_      :							List[str]       =      BytesIO(f.read()  )
                        UpperCAmelCase_      :							Optional[int]       =      PIL.Image.open(bytes_  )
         else:
              UpperCAmelCase_      :							Dict       =      PIL.Image.open(BytesIO(bytes_  )  )
         image.load()  # to avoid "Too many open files" errors
         return image





    def 							a__				(				self  )			->		Union["FeatureType", Dict[str, "FeatureType"]]:
         from .features import Value

         return (
             self
             if self.decode
             else {
                 "bytes": Value('''binary'''  ),
                 "path": Value('''string'''  ),
             }
         )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		pa.StructArray:
         if pa.types.is_string(storage.type  ):
              UpperCAmelCase_      :							Union[str, Any]       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.binary()  )
              UpperCAmelCase_      :							int       =      pa.StructArray.from_arrays([bytes_array, storage]       ,['''bytes''', '''path''']       ,mask=storage.is_null()  )
         elif pa.types.is_binary(storage.type  ):
              UpperCAmelCase_      :							Tuple       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.string()  )
              UpperCAmelCase_      :							int       =      pa.StructArray.from_arrays([storage, path_array]       ,['''bytes''', '''path''']       ,mask=storage.is_null()  )
         elif pa.types.is_struct(storage.type  ):
              if storage.type.get_field_index('''bytes'''  ) >= 0:
                   UpperCAmelCase_      :							str       =      storage.field('''bytes'''  )
              else:
                   UpperCAmelCase_      :							Union[str, Any]       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.binary()  )
              if storage.type.get_field_index('''path'''  ) >= 0:
                   UpperCAmelCase_      :							List[str]       =      storage.field('''path'''  )
              else:
                   UpperCAmelCase_      :							List[Any]       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.string()  )
              UpperCAmelCase_      :							Any       =      pa.StructArray.from_arrays([bytes_array, path_array]       ,['''bytes''', '''path''']       ,mask=storage.is_null()  )
         elif pa.types.is_list(storage.type  ):
              UpperCAmelCase_      :							Optional[Any]       =      pa.array(
                  [encode_np_array(np.array(_SCREAMING_SNAKE_CASE  )  )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]       ,type=pa.binary()       ,)
              UpperCAmelCase_      :							Any       =      pa.array([None] * len(_SCREAMING_SNAKE_CASE  )       ,type=pa.string()  )
              UpperCAmelCase_      :							Any       =      pa.StructArray.from_arrays(
                  [bytes_array, path_array]       ,['''bytes''', '''path''']       ,mask=bytes_array.is_null()  )
         return array_cast(_SCREAMING_SNAKE_CASE       ,self.pa_type  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(_SCREAMING_SNAKE_CASE  ):
              with xopen(_SCREAMING_SNAKE_CASE       ,'''rb'''  ) as f:
                   UpperCAmelCase_      :							List[Any]       =      f.read()
              return bytes_

         UpperCAmelCase_      :							Dict       =      pa.array(
             [
                 (path_to_bytes(x['''path''']  ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                 for x in storage.to_pylist()
             ]       ,type=pa.binary()       ,)
         UpperCAmelCase_      :							Union[str, Any]       =      pa.array(
             [os.path.basename(_SCREAMING_SNAKE_CASE  ) if path is not None else None for path in storage.field('''path'''  ).to_pylist()]       ,type=pa.string()       ,)
         UpperCAmelCase_      :							Tuple       =      pa.StructArray.from_arrays([bytes_array, path_array]       ,['''bytes''', '''path''']       ,mask=bytes_array.is_null()  )
         return array_cast(_SCREAMING_SNAKE_CASE       ,self.pa_type  )







def lowerCamelCase__       (      ):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.'''				)

     global _IMAGE_COMPRESSION_FORMATS
     if _IMAGE_COMPRESSION_FORMATS is None:
          PIL.Image.init()
          UpperCAmelCase_      :							Optional[Any]       =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
     return _IMAGE_COMPRESSION_FORMATS







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Optional[int]       =      BytesIO()
     if image.format in list_image_compression_formats():
          UpperCAmelCase_      :							List[str]       =      image.format
     else:
          UpperCAmelCase_      :							int       =      '''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
     image.save(_lowercase			,						format=_lowercase				)
     return buffer.getvalue()







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if hasattr(_lowercase			,						'''filename'''				) and image.filename != "":
          return {"path": image.filename, "bytes": None}
     else:
          return {"path": None, "bytes": image_to_bytes(_lowercase				)}







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.'''				)

     UpperCAmelCase_      :							int       =      array.dtype
     UpperCAmelCase_      :							Optional[Any]       =      dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
     UpperCAmelCase_      :							str       =      dtype.kind
     UpperCAmelCase_      :							int       =      dtype.itemsize

     UpperCAmelCase_      :							Optional[int]       =      None

     # Multi-channel array case (only np.dtype("|u1") is allowed)
     if array.shape[2:]:
          UpperCAmelCase_      :							str       =      np.dtype('''|u1'''				)
          if dtype_kind not in ["u", "i"]:
               raise TypeError(
                   f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''				)
          if dtype is not dest_dtype:
               warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
    # Exact match
     elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
          UpperCAmelCase_      :							Dict       =      dtype
     else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
          while dtype_itemsize >= 1:
               UpperCAmelCase_      :							Optional[int]       =      dtype_byteorder + dtype_kind + str(_lowercase				)
               UpperCAmelCase_      :							Dict       =      np.dtype(_lowercase				)
               if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
                    warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''				)
                    break
               else:
                    dtype_itemsize //= 2

     if dest_dtype is None:
          raise TypeError(
              f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''				)
     UpperCAmelCase_      :							Dict       =      PIL.Image.fromarray(array.astype(_lowercase				)				)
     return {"path": None, "bytes": image_to_bytes(_lowercase				)}







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     if config.PIL_AVAILABLE:
          import PIL.Image
     else:
          raise ImportError('''To support encoding images, please install \'Pillow\'.'''				)

     if objs:
          UpperCAmelCase_,   UpperCAmelCase_      :							List[Any]       =      first_non_null_value(_lowercase				)
          if isinstance(_lowercase			,						_lowercase				):
               return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
          if isinstance(_lowercase			,						np.ndarray				):
               UpperCAmelCase_      :							List[str]       =      no_op_if_value_is_null(_lowercase				)
               return [obj_to_image_dict_func(_lowercase				) for obj in objs]
          elif isinstance(_lowercase			,						PIL.Image.Image				):
               UpperCAmelCase_      :							Union[str, Any]       =      no_op_if_value_is_null(_lowercase				)
               return [obj_to_image_dict_func(_lowercase				) for obj in objs]
          else:
               return objs
     else:
          return objs