


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Any		     =					DPTConfig(embedding_type='''hybrid'''       )

  if "large" in checkpoint_url:
    A_      :				int		     =					1_024
    A_      :				Any		     =					4_096
    A_      :				Optional[int]		     =					24
    A_      :				Union[str, Any]		     =					16
    A_      :				List[Any]		     =					[5, 11, 17, 23]
    A_      :				Optional[int]		     =					[256, 512, 1_024, 1_024]
    A_      :				List[Any]		     =					(1, 384, 384)

  if "nyu" or "midas" in checkpoint_url:
    A_      :				List[Any]		     =					768
    A_      :				Optional[int]		     =					[1, 1, 1, 0.5]
    A_      :				int		     =					[256, 512, 768, 768]
    A_      :				str		     =					150
    A_      :				Tuple		     =					16
    A_      :				Union[str, Any]		     =					(1, 384, 384)
    A_      :				List[Any]		     =					False
    A_      :				str		     =					'''project'''

  if "ade" in checkpoint_url:
    A_      :				Optional[Any]		     =					True
    A_      :				Union[str, Any]		     =					768
    A_      :				Union[str, Any]		     =					[1, 1, 1, 0.5]
    A_      :				Dict		     =					150
    A_      :				str		     =					16
    A_      :				int		     =					'''huggingface/label-files'''
    A_      :				Dict		     =					'''ade20k-id2label.json'''
    A_      :				Tuple		     =					json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			repo_type='''dataset'''       )       ) ,			'''r'''       )       )
    A_      :				List[Any]		     =					{int(SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}
    A_      :				Union[str, Any]		     =					idalabel
    A_      :				List[Any]		     =					{v: k for k, v in idalabel.items()}
    A_      :				Tuple		     =					[1, 150, 480, 480]

  return config, expected_shape


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Optional[Any]		     =					['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  if (
      "pretrained.model" in name
      and "cls_token" not in name
      and "pos_embed" not in name
      and "patch_embed" not in name
  ):
    A_      :				Any		     =					name.replace('''pretrained.model''' ,			'''dpt.encoder'''       )
  if "pretrained.model" in name:
    A_      :				str		     =					name.replace('''pretrained.model''' ,			'''dpt.embeddings'''       )
  if "patch_embed" in name:
    A_      :				List[Any]		     =					name.replace('''patch_embed''' ,			''''''       )
  if "pos_embed" in name:
    A_      :				Any		     =					name.replace('''pos_embed''' ,			'''position_embeddings'''       )
  if "attn.proj" in name:
    A_      :				List[Any]		     =					name.replace('''attn.proj''' ,			'''attention.output.dense'''       )
  if "proj" in name and "project" not in name:
    A_      :				Any		     =					name.replace('''proj''' ,			'''projection'''       )
  if "blocks" in name:
    A_      :				str		     =					name.replace('''blocks''' ,			'''layer'''       )
  if "mlp.fc1" in name:
    A_      :				str		     =					name.replace('''mlp.fc1''' ,			'''intermediate.dense'''       )
  if "mlp.fc2" in name:
    A_      :				List[str]		     =					name.replace('''mlp.fc2''' ,			'''output.dense'''       )
  if "norm1" in name and "backbone" not in name:
    A_      :				Tuple		     =					name.replace('''norm1''' ,			'''layernorm_before'''       )
  if "norm2" in name and "backbone" not in name:
    A_      :				int		     =					name.replace('''norm2''' ,			'''layernorm_after'''       )
  if "scratch.output_conv" in name:
    A_      :				List[Any]		     =					name.replace('''scratch.output_conv''' ,			'''head'''       )
  if "scratch" in name:
    A_      :				List[str]		     =					name.replace('''scratch''' ,			'''neck'''       )
  if "layer1_rn" in name:
    A_      :				Union[str, Any]		     =					name.replace('''layer1_rn''' ,			'''convs.0'''       )
  if "layer2_rn" in name:
    A_      :				Optional[int]		     =					name.replace('''layer2_rn''' ,			'''convs.1'''       )
  if "layer3_rn" in name:
    A_      :				Any		     =					name.replace('''layer3_rn''' ,			'''convs.2'''       )
  if "layer4_rn" in name:
    A_      :				List[str]		     =					name.replace('''layer4_rn''' ,			'''convs.3'''       )
  if "refinenet" in name:
    A_      :				List[str]		     =					int(name[len('''neck.refinenet'''       ) : len('''neck.refinenet'''       ) + 1]       )
    # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
    A_      :				Any		     =					name.replace(f'''refinenet{layer_idx}''' ,			f'''fusion_stage.layers.{abs(layer_idx-4       )}'''       )
  if "out_conv" in name:
    A_      :				List[str]		     =					name.replace('''out_conv''' ,			'''projection'''       )
  if "resConfUnit1" in name:
    A_      :				Dict		     =					name.replace('''resConfUnit1''' ,			'''residual_layer1'''       )
  if "resConfUnit2" in name:
    A_      :				str		     =					name.replace('''resConfUnit2''' ,			'''residual_layer2'''       )
  if "conv1" in name:
    A_      :				Optional[int]		     =					name.replace('''conv1''' ,			'''convolution1'''       )
  if "conv2" in name:
    A_      :				Union[str, Any]		     =					name.replace('''conv2''' ,			'''convolution2'''       )
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    A_      :				Optional[int]		     =					name.replace('''pretrained.act_postprocess1.0.project.0''' ,			'''neck.reassemble_stage.readout_projects.0.0'''       )
  if "pretrained.act_postprocess2.0.project.0" in name:
    A_      :				Union[str, Any]		     =					name.replace('''pretrained.act_postprocess2.0.project.0''' ,			'''neck.reassemble_stage.readout_projects.1.0'''       )
  if "pretrained.act_postprocess3.0.project.0" in name:
    A_      :				Optional[Any]		     =					name.replace('''pretrained.act_postprocess3.0.project.0''' ,			'''neck.reassemble_stage.readout_projects.2.0'''       )
  if "pretrained.act_postprocess4.0.project.0" in name:
    A_      :				Dict		     =					name.replace('''pretrained.act_postprocess4.0.project.0''' ,			'''neck.reassemble_stage.readout_projects.3.0'''       )

  # resize blocks
  if "pretrained.act_postprocess1.3" in name:
    A_      :				str		     =					name.replace('''pretrained.act_postprocess1.3''' ,			'''neck.reassemble_stage.layers.0.projection'''       )
  if "pretrained.act_postprocess1.4" in name:
    A_      :				Tuple		     =					name.replace('''pretrained.act_postprocess1.4''' ,			'''neck.reassemble_stage.layers.0.resize'''       )
  if "pretrained.act_postprocess2.3" in name:
    A_      :				Optional[int]		     =					name.replace('''pretrained.act_postprocess2.3''' ,			'''neck.reassemble_stage.layers.1.projection'''       )
  if "pretrained.act_postprocess2.4" in name:
    A_      :				int		     =					name.replace('''pretrained.act_postprocess2.4''' ,			'''neck.reassemble_stage.layers.1.resize'''       )
  if "pretrained.act_postprocess3.3" in name:
    A_      :				Any		     =					name.replace('''pretrained.act_postprocess3.3''' ,			'''neck.reassemble_stage.layers.2.projection'''       )
  if "pretrained.act_postprocess4.3" in name:
    A_      :				Optional[Any]		     =					name.replace('''pretrained.act_postprocess4.3''' ,			'''neck.reassemble_stage.layers.3.projection'''       )
  if "pretrained.act_postprocess4.4" in name:
    A_      :				List[Any]		     =					name.replace('''pretrained.act_postprocess4.4''' ,			'''neck.reassemble_stage.layers.3.resize'''       )
  if "pretrained" in name:
    A_      :				Optional[int]		     =					name.replace('''pretrained''' ,			'''dpt'''       )
  if "bn" in name:
    A_      :				Tuple		     =					name.replace('''bn''' ,			'''batch_norm'''       )
  if "head" in name:
    A_      :				List[Any]		     =					name.replace('''head''' ,			'''head.head'''       )
  if "encoder.norm" in name:
    A_      :				Dict		     =					name.replace('''encoder.norm''' ,			'''layernorm'''       )
  if "auxlayer" in name:
    A_      :				Any		     =					name.replace('''auxlayer''' ,			'''auxiliary_head.head'''       )
  if "backbone" in name:
    A_      :				List[str]		     =					name.replace('''backbone''' ,			'''backbone.bit.encoder'''       )

  if ".." in name:
    A_      :				int		     =					name.replace('''..''' ,			'''.'''       )

  if "stem.conv" in name:
    A_      :				str		     =					name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''       )
  if "blocks" in name:
    A_      :				Dict		     =					name.replace('''blocks''' ,			'''layers'''       )
  if "convolution" in name and "backbone" in name:
    A_      :				Union[str, Any]		     =					name.replace('''convolution''' ,			'''conv'''       )
  if "layer" in name and "backbone" in name:
    A_      :				Dict		     =					name.replace('''layer''' ,			'''layers'''       )
  if "backbone.bit.encoder.bit" in name:
    A_      :				Tuple		     =					name.replace('''backbone.bit.encoder.bit''' ,			'''backbone.bit'''       )
  if "embedder.conv" in name:
    A_      :				Tuple		     =					name.replace('''embedder.conv''' ,			'''embedder.convolution'''       )
  if "backbone.bit.encoder.stem.norm" in name:
    A_      :				Dict		     =					name.replace('''backbone.bit.encoder.stem.norm''' ,			'''backbone.bit.embedder.norm'''       )
  return name


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  for i in range(config.num_hidden_layers       ):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    A_      :				int		     =					state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
    A_      :				Tuple		     =					state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''       )
    # next, add query, keys and values (in that order) to the state dict
    A_      :				Tuple		     =					in_proj_weight[: config.hidden_size, :]
    A_      :				List[Any]		     =					in_proj_bias[: config.hidden_size]
    A_      :				Dict		     =					in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    A_      :				int		     =					in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    A_      :				Dict		     =					in_proj_weight[
        -config.hidden_size :, :
    ]
    A_      :				Optional[int]		     =					in_proj_bias[-config.hidden_size :]


def   _SCREAMING_SNAKE_CASE	( ):
  A_      :				Optional[Any]		     =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  A_      :				Any		     =					Image.open(requests.get(SCREAMING_SNAKE_CASE ,			stream=SCREAMING_SNAKE_CASE       ).raw       )
  return im


@torch.no_grad()
def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  A_     ,       A_      :				Any		     =					get_dpt_config(SCREAMING_SNAKE_CASE       )
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  A_      :				List[Any]		     =					torch.load(SCREAMING_SNAKE_CASE ,			map_location='''cpu'''       )
  # remove certain keys
  remove_ignore_keys_(SCREAMING_SNAKE_CASE       )
  # rename keys
  for key in state_dict.copy().keys():
    A_      :				Optional[Any]		     =					state_dict.pop(SCREAMING_SNAKE_CASE       )
    A_      :				Optional[int]		     =					val
  # read in qkv matrices
  read_in_q_k_v(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  # load HuggingFace model
  A_      :				Any		     =					DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE       ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE       )
  model.load_state_dict(SCREAMING_SNAKE_CASE       )
  model.eval()

  # Check outputs on an image
  A_      :				str		     =					480 if '''ade''' in checkpoint_url else 384
  A_      :				List[str]		     =					DPTImageProcessor(size=SCREAMING_SNAKE_CASE       )

  A_      :				List[str]		     =					prepare_img()
  A_      :				Tuple		     =					image_processor(SCREAMING_SNAKE_CASE ,			return_tensors='''pt'''       )

  # forward pass
  A_      :				List[Any]		     =					model(**SCREAMING_SNAKE_CASE       ).logits if '''ade''' in checkpoint_url else model(**SCREAMING_SNAKE_CASE       ).predicted_depth

  if show_prediction:
    A_      :				Tuple		     =					(
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1       ) ,			size=(image.size[1], image.size[0]) ,			mode='''bicubic''' ,			align_corners=SCREAMING_SNAKE_CASE ,			)
        .squeeze()
        .cpu()
        .numpy()
    )

    Image.fromarray((prediction / prediction.max()) * 255       ).show()

  if pytorch_dump_folder_path is not None:
    Path(SCREAMING_SNAKE_CASE       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE       )
    print(f'''Saving model to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(SCREAMING_SNAKE_CASE       )
    print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE       )

  if push_to_hub:
    model.push_to_hub('''ybelkada/dpt-hybrid-midas'''       )
    image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''       )


if __name__ == "__main__":
     UpperCamelCase								=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
         type=str,
         help="""URL of the original DPT checkpoint you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=False,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
     )
     parser.add_argument(
         """--model_name""",
         default="""dpt-large""",
         type=str,
         help="""Name of the model, in case you're pushing to the hub.""",
     )
     parser.add_argument(
         """--show_prediction""",
         action="""store_true""",
     )

     UpperCamelCase								=	parser.parse_args()
     convert_dpt_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
     )








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


UpperCamelCase								=	None
UpperCamelCase								=	"""<""" 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
UpperCamelCase								=	[
    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   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    True
       snake_case      =    None
       # Automatically constructed
       snake_case      =    "PIL.Image.Image"
       snake_case      =    pa.struct({"bytes": pa.binary(), "path": pa.string()}			)
       snake_case      =    field(default="Image"					,							init=UpperCamelCase					,							repr=UpperCamelCase			)



       def __call__(      self		)->int:




         '''simple docstring'''





         return self.pa_type



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->dict:




         '''simple docstring'''





         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		):
           A_      :				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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None		)->"PIL.Image.Image":




         '''simple docstring'''





         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_      :				List[str]		     =					{}

         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(_SCREAMING_SNAKE_CASE		):
               A_      :				List[str]		     =					PIL.Image.open(_SCREAMING_SNAKE_CASE		)
             else:
               A_      :				List[str]		     =					path.split('''::'''		)[-1]
               try:
                 A_      :				int		     =					string_to_dict(_SCREAMING_SNAKE_CASE				,       config.HUB_DATASETS_URL		)['''repo_id''']
                 A_      :				Optional[int]		     =					token_per_repo_id.get(_SCREAMING_SNAKE_CASE		)
               except ValueError:
                 A_      :				Any		     =					None
               with xopen(_SCREAMING_SNAKE_CASE				,       '''rb'''				,       use_auth_token=_SCREAMING_SNAKE_CASE		) as f:
                 A_      :				Optional[Any]		     =					BytesIO(f.read()		)
               A_      :				Dict		     =					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        _snake_case       (      self		)->Union["FeatureType", Dict[str, "FeatureType"]]:




         '''simple docstring'''





         from .features import Value

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



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




         '''simple docstring'''





         if pa.types.is_string(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           A_      :				List[Any]		     =					pa.StructArray.from_arrays([bytes_array, storage]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_binary(storage.type		):
           A_      :				Dict		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[str]		     =					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_      :				Tuple		     =					storage.field('''bytes'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.binary()		)
           if storage.type.get_field_index('''path'''		) >= 0:
             A_      :				Optional[Any]		     =					storage.field('''path'''		)
           else:
             A_      :				Optional[int]		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				Union[str, Any]		     =					pa.StructArray.from_arrays([bytes_array, path_array]				,       ['''bytes''', '''path''']				,       mask=storage.is_null()		)
         elif pa.types.is_list(storage.type		):
           A_      :				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()				,       )
           A_      :				str		     =					pa.array([None] * len(_SCREAMING_SNAKE_CASE		)				,       type=pa.string()		)
           A_      :				List[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        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->pa.StructArray:




         '''simple docstring'''





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

         A_      :				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()				,       )
         A_      :				List[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()				,       )
         A_      :				str		     =					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   _SCREAMING_SNAKE_CASE	( ):
  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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Dict		     =					BytesIO()
  if image.format in list_image_compression_formats():
    A_      :				Tuple		     =					image.format
  else:
    A_      :				List[str]		     =					'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
  image.save(SCREAMING_SNAKE_CASE ,			format=SCREAMING_SNAKE_CASE       )
  return buffer.getvalue()


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


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

  A_      :				Union[str, Any]		     =					array.dtype
  A_      :				Dict		     =					dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
  A_      :				Any		     =					dtype.kind
  A_      :				Any		     =					dtype.itemsize

  A_      :				Dict		     =					None

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







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

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




