



'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
__lowerCAmelCase									=   [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      int:
		for attribute in key.split('.'  ):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								_a     :      List[Any]        =							'lm_head'

				_a     :      Tuple        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

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

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      List[Any]        =							[]
		_a     :      Dict        =							fairseq_model.state_dict()

		_a     :      Union[str, Any]        =							hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				_a     :      Tuple        =							False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			hf_model.config.feat_extract_norm == 'group'						,			)
						_a     :      Union[str, Any]        =							True
				else:
						for key, mapped_key in MAPPING.items():
								_a     :      List[str]        =							'unispeech.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('w2v_model.'  )[-1] == name.split('.'  )[0]:
										_a     :      int        =							True
										if "*" in mapped_key:
												_a     :      Dict        =							name.split(lowerCAmelCase_  )[0].split('.'  )[-2]
												_a     :      int        =							mapped_key.replace('*'						,			lowerCAmelCase_  )
										if "weight_g" in name:
												_a     :      Any        =							'weight_g'
										elif "weight_v" in name:
												_a     :      List[Any]        =							'weight_v'
										elif "bias" in name:
												_a     :      Union[str, Any]        =							'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												_a     :      int        =							'weight'
										else:
												_a     :      List[str]        =							None
										set_recursively(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase_  )

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



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      Any:
		_a     :      int        =							full_name.split('conv_layers.'  )[-1]
		_a     :      Tuple        =							name.split('.'  )
		_a     :      Optional[Any]        =							int(items[0]  )
		_a     :      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."""
						)
						_a     :      int        =							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."""
						)
						_a     :      Optional[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."
						)
						_a     :      List[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."""
						)
						_a     :      Optional[Any]        =							value
						logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCAmelCase_  )



@torch.no_grad()
def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=True  )			->      Dict:
		if config_path is not None:
				_a     :      List[str]        =							UniSpeechConfig.from_pretrained(lowerCAmelCase_  )
		else:
				_a     :      Tuple        =							UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						_a     :      Optional[Any]        =							Dictionary.load_from_json(lowerCAmelCase_  )

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_a     :      Any        =							target_dict.pad_index
						_a     :      Optional[Any]        =							target_dict.bos_index
						_a     :      Optional[int]        =							target_dict.eos_index
						_a     :      List[Any]        =							len(target_dict.symbols  )
						_a     :      Tuple        =							os.path.join(lowerCAmelCase_						,			'vocab.json'  )
						if not os.path.isdir(lowerCAmelCase_  ):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase_  )  )
								return
						os.makedirs(lowerCAmelCase_						,			exist_ok=lowerCAmelCase_  )
						_a     :      List[str]        =							target_dict.indices

						# fairseq has the <pad> and <s> switched
						_a     :      List[Any]        =							42
						_a     :      Any        =							43
						with open(lowerCAmelCase_						,			'w'						,			encoding='utf-8'  ) as vocab_handle:
								json.dump(lowerCAmelCase_						,			lowerCAmelCase_  )
						_a     :      Optional[Any]        =							WavaVecaPhonemeCTCTokenizer(
						    lowerCAmelCase_						,			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=lowerCAmelCase_						,			)
						_a     :      List[Any]        =							True if config.feat_extract_norm == 'layer' else False
						_a     :      List[str]        =							WavaVecaFeatureExtractor(
						    feature_size=1						,			sampling_rate=16000						,			padding_value=0						,			do_normalize=lowerCAmelCase_						,			return_attention_mask=lowerCAmelCase_						,			)
						_a     :      Dict        =							WavaVecaProcessor(feature_extractor=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_  )
						processor.save_pretrained(lowerCAmelCase_  )

				_a     :      List[str]        =							UniSpeechForCTC(lowerCAmelCase_  )
		else:
				_a     :      int        =							UniSpeechForPreTraining(lowerCAmelCase_  )

		if is_finetuned:
				_a       ,					_a       ,					_a     :      List[str]        =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]						,			arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  ), 'w2v_path': checkpoint_path}  )
		else:
				_a       ,					_a       ,					_a     :      int        =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

		_a     :      Optional[Any]        =							model[0].eval()

		recursively_load_weights(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )

		hf_unispeech.save_pretrained(lowerCAmelCase_  )


if __name__ == "__main__":
							__lowerCAmelCase									=   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									=   parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)









'''simple docstring'''

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


__lowerCAmelCase									=   None
__lowerCAmelCase									=   '''<''' 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
__lowerCAmelCase									=   [
    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 	__magic_name__						:
							lowerCAmelCase       :    bool   				=					True
							lowerCAmelCase       :    Optional[str]   				=					None
							# Automatically constructed
							lowerCAmelCase       :    ClassVar[str]   				=					"PIL.Image.Image"
							lowerCAmelCase       :    ClassVar[Any]   				=					pa.struct({'bytes': pa.binary(), 'path': pa.string()}   )
							lowerCAmelCase       :    str   				=					field(default='Image'			,						init=_UpperCamelCase			,						repr=_UpperCamelCase   )







							def __call__(							self					:       Union[str, Any]		):
									return self.pa_type







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]		):
									if config.PIL_AVAILABLE:
											import PIL.Image
									else:
											raise ImportError('To support encoding images, please install \'Pillow\'.'		)

									if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											_a     :      Optional[Any]        =							np.array(_UpperCAmelCase		)

									if isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											return {"path": value, "bytes": None}
									elif isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
											return {"path": None, "bytes": value}
									elif isinstance(_UpperCAmelCase	,np.ndarray		):
											# convert the image array to PNG/TIFF bytes
											return encode_np_array(_UpperCAmelCase		)
									elif isinstance(_UpperCAmelCase	,PIL.Image.Image		):
											# convert the PIL image to bytes (default format is PNG/TIFF)
											return encode_pil_image(_UpperCAmelCase		)
									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 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       dict	,_UpperCAmelCase					:       Optional[int]=None		):
									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:
											_a     :      Dict        =							{}

									_a       ,					_a     :      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(_UpperCAmelCase		):
															_a     :      Any        =							PIL.Image.open(_UpperCAmelCase		)
													else:
															_a     :      List[Any]        =							path.split('::'		)[-1]
															try:
																	_a     :      str        =							string_to_dict(_UpperCAmelCase	,config.HUB_DATASETS_URL		)['repo_id']
																	_a     :      Optional[Any]        =							token_per_repo_id.get(_UpperCAmelCase		)
															except ValueError:
																	_a     :      int        =							None
															with xopen(_UpperCAmelCase	,'rb'	,use_auth_token=_UpperCAmelCase		) as f:
																	_a     :      Tuple        =							BytesIO(f.read()		)
															_a     :      Union[str, Any]        =							PIL.Image.open(bytes_		)
									else:
											_a     :      Optional[int]        =							PIL.Image.open(BytesIO(bytes_		)		)
									image.load()  # to avoid "Too many open files" errors
									return image







							def 							__lowercase			(							self					:       int		):
									from .features import Value

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







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







							def 							__lowercase			(							self					:       Dict	,_UpperCAmelCase					:       pa.StructArray		):

									@no_op_if_value_is_null
									def path_to_bytes(_UpperCAmelCase					:       Tuple		):
											with xopen(_UpperCAmelCase	,'rb'		) as f:
													_a     :      int        =							f.read()
											return bytes_

									_a     :      Any        =							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()	,)
									_a     :      Optional[Any]        =							pa.array(
									    [os.path.basename(_UpperCAmelCase		) if path is not None else None for path in storage.field('path'		).to_pylist()]	,type=pa.string()	,)
									_a     :      Dict        =							pa.StructArray.from_arrays([bytes_array, path_array]	,['bytes', 'path']	,mask=bytes_array.is_null()		)
									return array_cast(_UpperCAmelCase	,self.pa_type		)



def      __lowerCamelCase     (      )			->      List[str]:
		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()
				_a     :      Dict        =							list(set(PIL.Image.OPEN.keys()  ) & set(PIL.Image.SAVE.keys()  )  )
		return _IMAGE_COMPRESSION_FORMATS



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      bytes:
		_a     :      Optional[int]        =							BytesIO()
		if image.format in list_image_compression_formats():
				_a     :      Optional[Any]        =							image.format
		else:
				_a     :      str        =							'PNG' if image.mode in ['1', 'L', 'LA', 'RGB', 'RGBA'] else 'TIFF'
		image.save(lowerCAmelCase_						,			format=lowerCAmelCase_  )
		return buffer.getvalue()



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      dict:
		if hasattr(lowerCAmelCase_						,			'filename'  ) and image.filename != "":
				return {"path": image.filename, "bytes": None}
		else:
				return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_  )}



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

		_a     :      List[Any]        =							array.dtype
		_a     :      Optional[int]        =							dtype.byteorder if dtype.byteorder != '=' else _NATIVE_BYTEORDER
		_a     :      Union[str, Any]        =							dtype.kind
		_a     :      Union[str, Any]        =							dtype.itemsize

		_a     :      List[Any]        =							None

		# Multi-channel array case (only np.dtype("|u1") is allowed)
		if array.shape[2:]:
				_a     :      Optional[int]        =							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:
				_a     :      Union[str, Any]        =							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:
						_a     :      str        =							dtype_byteorder + dtype_kind + str(lowerCAmelCase_  )
						_a     :      List[Any]        =							np.dtype(lowerCAmelCase_  )
						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}"""  )
		_a     :      Union[str, Any]        =							PIL.Image.fromarray(array.astype(lowerCAmelCase_  )  )
		return {"path": None, "bytes": image_to_bytes(lowerCAmelCase_  )}



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[dict]:
		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError('To support encoding images, please install \'Pillow\'.'  )

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




