


'''simple docstring'''

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

a_  :				int						= [
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

a_  :				Optional[Any]						= [
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

a_  :				Union[str, Any]						= []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
     # loop over downblocks/upblocks

     for j in range(2):
          # loop over resnets/attentions for downblocks
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.resnets.{j}.'''
          a_  :				str						= f'''input_blocks.{3*i + j + 1}.0.'''
          unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

          if i < 3:
               # no attention layers in down_blocks.3
               a_  :				Optional[Any]						= f'''down_blocks.{i}.attentions.{j}.'''
               a_  :				Any						= f'''input_blocks.{3*i + j + 1}.1.'''
               unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

     for j in range(3):
          # loop over resnets/attentions for upblocks
          a_  :				Optional[int]						= f'''up_blocks.{i}.resnets.{j}.'''
          a_  :				str						= f'''output_blocks.{3*i + j}.0.'''
          unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

          if i > 0:
               # no attention layers in up_blocks.0
               a_  :				str						= f'''up_blocks.{i}.attentions.{j}.'''
               a_  :				int						= f'''output_blocks.{3*i + j}.1.'''
               unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

     if i < 3:
          # no downsample in down_blocks.3
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.conv.'''
          a_  :				str						= f'''input_blocks.{3*(i+1)}.0.op.'''
          unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

          # no upsample in up_blocks.3
          a_  :				Optional[int]						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				Any						= f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
          unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

a_  :				Optional[Any]						= """mid_block.attentions.0."""
a_  :				Any						= """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
     a_  :				Dict						= f'''mid_block.resnets.{j}.'''
     a_  :				Union[str, Any]						= f'''middle_block.{2*j}.'''
     unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 # buyer beware: this is a *brittle* function,
 # and correct output requires that all of these pieces interact in
 # the exact order in which I have arranged them.
 lowerCamelCase_      =  {k: k for k in unet_state_dict.keys()}
 for sd_name, hf_name in unet_conversion_map:
  lowerCamelCase_      =  sd_name
 for k, v in mapping.items():
  if "resnets" in k:
   for sd_part, hf_part in unet_conversion_map_resnet:
    lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 for k, v in mapping.items():
  for sd_part, hf_part in unet_conversion_map_layer:
   lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: unet_state_dict[k] for k, v in mapping.items()}
 return new_state_dict


# ================#
# VAE Conversion #
# ================#

a_  :				int						= [
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
     # down_blocks have two resnets
     for j in range(2):
          a_  :				Dict						= f'''encoder.down_blocks.{i}.resnets.{j}.'''
          a_  :				List[str]						= f'''encoder.down.{i}.block.{j}.'''
          vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

     if i < 3:
          a_  :				Union[str, Any]						= f'''down_blocks.{i}.downsamplers.0.'''
          a_  :				List[Any]						= f'''down.{i}.downsample.'''
          vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

          a_  :				str						= f'''up_blocks.{i}.upsamplers.0.'''
          a_  :				List[Any]						= f'''up.{3-i}.upsample.'''
          vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

     # up_blocks have three resnets
     # also, up blocks in hf are numbered in reverse from sd
     for j in range(3):
          a_  :				List[Any]						= f'''decoder.up_blocks.{i}.resnets.{j}.'''
          a_  :				Dict						= f'''decoder.up.{3-i}.block.{j}.'''
          vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
     a_  :				Optional[int]						= f'''mid_block.resnets.{i}.'''
     a_  :				List[Any]						= f'''mid.block_{i+1}.'''
     vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


a_  :				str						= [
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 # convert HF linear weights to SD conv2d weights
 return w.reshape(*w.shape					,				1					,				1		)





def __snake_case					(      UpperCAmelCase_						:						Optional[Any]		):
 lowerCamelCase_      =  {k: k for k in vae_state_dict.keys()}
 for k, v in mapping.items():
  for sd_part, hf_part in vae_conversion_map:
   lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v
 for k, v in mapping.items():
  if "attentions" in k:
   for sd_part, hf_part in vae_conversion_map_attn:
    lowerCamelCase_      =  v.replace(UpperCAmelCase_					,				UpperCAmelCase_		)
   lowerCamelCase_      =  v
 lowerCamelCase_      =  {v: vae_state_dict[k] for k, v in mapping.items()}
 lowerCamelCase_      =  ["q", "k", "v", "proj_out"]
 for k, v in new_state_dict.items():
  for weight_name in weights_to_convert:
   if F'''mid.attn_1.{weight_name}.weight''' in k:
    print(F'''Reshaping {k} for SD format'''		)
    lowerCamelCase_      =  reshape_weight_for_sd(UpperCAmelCase_		)
 return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


a_  :				int						= [
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
a_  :				str						= {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
a_  :				int						= re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
a_  :				Dict						= {"""q""": 0, """k""": 1, """v""": 2}





def __snake_case					(      UpperCAmelCase_						:						Tuple		):
 lowerCamelCase_      =  {}
 lowerCamelCase_      =  {}
 lowerCamelCase_      =  {}
 for k, v in text_enc_dict.items():
  if (
      k.endswith(".self_attn.q_proj.weight"		)
      or k.endswith(".self_attn.k_proj.weight"		)
      or k.endswith(".self_attn.v_proj.weight"		)
  ):
   lowerCamelCase_      =  k[: -len(".q_proj.weight"		)]
   lowerCamelCase_      =  k[-len("q_proj.weight"		)]
   if k_pre not in capture_qkv_weight:
    lowerCamelCase_      =  [None, None, None]
   lowerCamelCase_      =  v
   continue

  if (
      k.endswith(".self_attn.q_proj.bias"		)
      or k.endswith(".self_attn.k_proj.bias"		)
      or k.endswith(".self_attn.v_proj.bias"		)
  ):
   lowerCamelCase_      =  k[: -len(".q_proj.bias"		)]
   lowerCamelCase_      =  k[-len("q_proj.bias"		)]
   if k_pre not in capture_qkv_bias:
    lowerCamelCase_      =  [None, None, None]
   lowerCamelCase_      =  v
   continue

  lowerCamelCase_      =  textenc_pattern.sub(lambda UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  v

 for k_pre, tensors in capture_qkv_weight.items():
  if None in tensors:
   raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"		)
  lowerCamelCase_      =  textenc_pattern.sub(lambda UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_		)

 for k_pre, tensors in capture_qkv_bias.items():
  if None in tensors:
   raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing"		)
  lowerCamelCase_      =  textenc_pattern.sub(lambda UpperCAmelCase_		: protected[re.escape(m.group(0		)		)]					,				UpperCAmelCase_		)
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_		)

 return new_state_dict





def __snake_case					(      UpperCAmelCase_						:						List[Any]		):
 return text_enc_dict


if __name__ == "__main__":
     a_  :				List[str]						= argparse.ArgumentParser()

     parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
     parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
     parser.add_argument(
         """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
     )

     a_  :				List[Any]						= parser.parse_args()

     assert args.model_path is not None, "Must provide a model path!"

     assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

     # Path for safetensors
     a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[int]						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
     a_  :				Optional[Any]						= osp.join(args.model_path, """text_encoder""", """model.safetensors""")

     # Load models from safetensors if it exists, if it doesn't pytorch
     if osp.exists(unet_path):
          a_  :				int						= load_file(unet_path, device="""cpu""")
     else:
          a_  :				str						= osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
          a_  :				int						= torch.load(unet_path, map_location="""cpu""")

     if osp.exists(vae_path):
          a_  :				List[Any]						= load_file(vae_path, device="""cpu""")
     else:
          a_  :				Dict						= osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
          a_  :				Tuple						= torch.load(vae_path, map_location="""cpu""")

     if osp.exists(text_enc_path):
          a_  :				Union[str, Any]						= load_file(text_enc_path, device="""cpu""")
     else:
          a_  :				Tuple						= osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
          a_  :				int						= torch.load(text_enc_path, map_location="""cpu""")

     # Convert the UNet model
     a_  :				List[str]						= convert_unet_state_dict(unet_state_dict)
     a_  :				Tuple						= {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

     # Convert the VAE model
     a_  :				Any						= convert_vae_state_dict(vae_state_dict)
     a_  :				List[Any]						= {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

     # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
     a_  :				List[Any]						= """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

     if is_vaa_model:
          # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
          a_  :				List[Any]						= {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
          a_  :				List[Any]						= convert_text_enc_state_dict_vaa(text_enc_dict)
          a_  :				str						= {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
     else:
          a_  :				List[str]						= convert_text_enc_state_dict(text_enc_dict)
          a_  :				Tuple						= {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

     # Put together new checkpoint
     a_  :				Any						= {**unet_state_dict, **vae_state_dict, **text_enc_dict}
     if args.half:
          a_  :				Optional[Any]						= {k: v.half() for k, v in state_dict.items()}

     if args.use_safetensors:
          save_file(state_dict, args.checkpoint_path)
     else:
          a_  :				Optional[int]						= {"""state_dict""": state_dict}
          torch.save(state_dict, args.checkpoint_path)










'''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


lowercase_					= None
lowercase_					= """<""" 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
lowercase_					= [
    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'''
    UpperCamelCase     = True
    UpperCamelCase     = None
    # Automatically constructed
    UpperCamelCase     = "PIL.Image.Image"
    UpperCamelCase     = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}       )
    UpperCamelCase     = field(default='''Image'''      ,    init=snake_case_      ,    repr=snake_case_       )


    def __call__(		self       )		->   Tuple:
        return self.pa_type


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

        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			np.array(A       )

        if isinstance(A ,		A       ):
            return {"path": value, "bytes": None}
        elif isinstance(A ,		A       ):
            return {"path": None, "bytes": value}
        elif isinstance(A ,		np.ndarray       ):
            # convert the image array to PNG/TIFF bytes
            return encode_np_array(A       )
        elif isinstance(A ,		PIL.Image.Image       ):
            # convert the PIL image to bytes (default format is PNG/TIFF)
            return encode_pil_image(A       )
        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 ,		A ,		A=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:
            _SCREAMING_SNAKE_CASE									=			{}

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			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(A       ):
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(A       )
                else:
                    _SCREAMING_SNAKE_CASE									=			path.split("""::"""       )[-1]
                    try:
                        _SCREAMING_SNAKE_CASE									=			string_to_dict(A ,		config.HUB_DATASETS_URL       )["""repo_id"""]
                        _SCREAMING_SNAKE_CASE									=			token_per_repo_id.get(A       )
                    except ValueError:
                        _SCREAMING_SNAKE_CASE									=			None
                    with xopen(A ,		"""rb""" ,		use_auth_token=A       ) as f:
                        _SCREAMING_SNAKE_CASE									=			BytesIO(f.read()       )
                    _SCREAMING_SNAKE_CASE									=			PIL.Image.open(bytes_       )
        else:
            _SCREAMING_SNAKE_CASE									=			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"]]:
        from .features import Value

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


    def 			snake_case_(		self ,		A       )		->   pa.StructArray:
        if pa.types.is_string(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.binary()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays([bytes_array, storage] ,		["""bytes""", """path"""] ,		mask=storage.is_null()       )
        elif pa.types.is_binary(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			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:
                _SCREAMING_SNAKE_CASE									=			storage.field("""bytes"""       )
            else:
                _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.binary()       )
            if storage.type.get_field_index("""path"""       ) >= 0:
                _SCREAMING_SNAKE_CASE									=			storage.field("""path"""       )
            else:
                _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays([bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=storage.is_null()       )
        elif pa.types.is_list(storage.type       ):
            _SCREAMING_SNAKE_CASE									=			pa.array(
                [encode_np_array(np.array(A       )       )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] ,		type=pa.binary() ,		)
            _SCREAMING_SNAKE_CASE									=			pa.array([None] * len(A       ) ,		type=pa.string()       )
            _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays(
                [bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=bytes_array.is_null()       )
        return array_cast(A ,		self.pa_type       )





    def 			snake_case_(		self ,		A       )		->   pa.StructArray:

        @no_op_if_value_is_null
        def path_to_bytes(A       ):
            with xopen(A ,		"""rb"""       ) as f:
                _SCREAMING_SNAKE_CASE									=			f.read()
            return bytes_

        _SCREAMING_SNAKE_CASE									=			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() ,		)
        _SCREAMING_SNAKE_CASE									=			pa.array(
            [os.path.basename(A       ) if path is not None else None for path in storage.field("""path"""       ).to_pylist()] ,		type=pa.string() ,		)
        _SCREAMING_SNAKE_CASE									=			pa.StructArray.from_arrays([bytes_array, path_array] ,		["""bytes""", """path"""] ,		mask=bytes_array.is_null()       )
        return array_cast(A ,		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()
        _SCREAMING_SNAKE_CASE									=			list(set(PIL.Image.OPEN.keys()    ) & set(PIL.Image.SAVE.keys()    )    )
    return _IMAGE_COMPRESSION_FORMATS





def lowerCamelCase						(     __lowerCamelCase :    "PIL.Image.Image"    )						->bytes:
    _SCREAMING_SNAKE_CASE									=			BytesIO()
    if image.format in list_image_compression_formats():
        _SCREAMING_SNAKE_CASE									=			image.format
    else:
        _SCREAMING_SNAKE_CASE									=			"""PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
    image.save(__lowerCamelCase					,       format=__lowerCamelCase    )
    return buffer.getvalue()





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

    _SCREAMING_SNAKE_CASE									=			array.dtype
    _SCREAMING_SNAKE_CASE									=			dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
    _SCREAMING_SNAKE_CASE									=			dtype.kind
    _SCREAMING_SNAKE_CASE									=			dtype.itemsize

    _SCREAMING_SNAKE_CASE									=			None

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






def lowerCamelCase						(     __lowerCamelCase :    Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]    )						->List[dict]:
    if config.PIL_AVAILABLE:
        import PIL.Image
    else:
        raise ImportError("""To support encoding images, please install 'Pillow'."""    )

    if objs:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			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    ):
            _SCREAMING_SNAKE_CASE									=			no_op_if_value_is_null(__lowerCamelCase    )
            return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
        elif isinstance(__lowerCamelCase					,       PIL.Image.Image    ):
            _SCREAMING_SNAKE_CASE									=			no_op_if_value_is_null(__lowerCamelCase    )
            return [obj_to_image_dict_func(__lowerCamelCase    ) for obj in objs]
        else:
            return objs
    else:
        return objs



