import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class 							lowercase						(						lowercase_ ,				lowercase_ ,				unittest.TestCase ):
   lowerCamelCase_    					=AutoencoderKL
   lowerCamelCase_    					='''sample'''
   lowerCamelCase_    					=1e-2


   @property
   def __UpperCAmelCase       (   self				:	Tuple)      ->					Tuple:
         lowercase_     =      4
         lowercase_     =      3
         lowercase_     =      (32, 32)

         lowercase_     =      floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

         return {"sample": image}


   @property
   def __UpperCAmelCase       (   self				:	Tuple)      ->					Any:
         return (3, 32, 32)


   @property
   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         return (3, 32, 32)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Optional[int]:
         lowercase_     =      {
             "block_out_channels": [32, 64],
             "in_channels": 3,
             "out_channels": 3,
             "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
             "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
             "latent_channels": 4,
         }
         lowercase_     =      self.dummy_input
         return init_dict, inputs_dict


   def __UpperCAmelCase       (   self				:	Dict)      ->					Tuple:
         pass


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[str]:
         pass


   @unittest.skipIf(torch_device == "mps"   ,		"Gradient checkpointing skipped on MPS")
   def __UpperCAmelCase       (   self				:	List[str])      ->					Optional[Any]:
         lowercase_ ,							lowercase_     =      self.prepare_init_args_and_inputs_for_common()
         lowercase_     =      self.model_class(**UpperCamelCase__)
         model.to(UpperCamelCase__)

         assert not model.is_gradient_checkpointing and model.training

         lowercase_     =      model(**UpperCamelCase__).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model.zero_grad()

         lowercase_     =      torch.randn_like(UpperCamelCase__)
         lowercase_     =      (out - labels).mean()
         loss.backward()

         # re-instantiate the model now enabling gradient checkpointing
         lowercase_     =      self.model_class(**UpperCamelCase__)
         # clone model
         model_a.load_state_dict(model.state_dict())
         model_a.to(UpperCamelCase__)
         model_a.enable_gradient_checkpointing()

         assert model_a.is_gradient_checkpointing and model_a.training

         lowercase_     =      model_a(**UpperCamelCase__).sample
         # run the backwards pass on the model. For backwards pass, for simplicity purpose,
         # we won't calculate the loss and rather backprop on out.sum()
         model_a.zero_grad()
         lowercase_     =      (out_a - labels).mean()
         loss_a.backward()

         # compare the output and parameters gradients
         self.assertTrue((loss - loss_a).abs() < 1e-5)
         lowercase_     =      dict(model.named_parameters())
         lowercase_     =      dict(model_a.named_parameters())
         for name, param in named_params.items():
               self.assertTrue(torch_all_close(param.grad.data   ,		named_params_a[name].grad.data   ,		atol=5e-5))


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Tuple:
         lowercase_ ,							lowercase_     =      AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy"   ,		output_loading_info=UpperCamelCase__)
         self.assertIsNotNone(UpperCamelCase__)
         self.assertEqual(len(loading_info["missing_keys"])   ,		0)

         model.to(UpperCamelCase__)
         lowercase_     =      model(**self.dummy_input)

         assert image is not None, "Make sure output is not None"


   def __UpperCAmelCase       (   self				:	int)      ->					List[Any]:
         lowercase_     =      AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
         lowercase_     =      model.to(UpperCamelCase__)
         model.eval()

         if torch_device == "mps":
               lowercase_     =      torch.manual_seed(0)
         else:
               lowercase_     =      torch.Generator(device=UpperCamelCase__).manual_seed(0)

         lowercase_     =      torch.randn(
             1   ,		model.config.in_channels   ,		model.config.sample_size   ,		model.config.sample_size   ,		generator=torch.manual_seed(0)   ,		)
         lowercase_     =      image.to(UpperCamelCase__)
         with torch.no_grad():
               lowercase_     =      model(UpperCamelCase__   ,		sample_posterior=UpperCamelCase__   ,		generator=UpperCamelCase__).sample

         lowercase_     =      output[0, -1, -3:, -3:].flatten().cpu()

         # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
         # the expected output slices are not the same for CPU and GPU.
         if torch_device == "mps":
               lowercase_     =      torch.tensor(
                   [
                       -4.00_78e-01,
                       -3.83_23e-04,
                       -1.26_81e-01,
                       -1.14_62e-01,
                       2.00_95e-01,
                       1.08_93e-01,
                       -8.82_47e-02,
                       -3.03_61e-01,
                       -9.86_44e-03,
                   ])
         elif torch_device == "cpu":
               lowercase_     =      torch.tensor(
                   [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026])
         else:
               lowercase_     =      torch.tensor(
                   [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485])

         self.assertTrue(torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		rtol=1e-2))





@slow
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Union[str, Any])      ->					str:
         return F'gaussian_noise_s={seed}_shape={"_".join([str(UpperCamelCase__) for s in shape])}.npy'


   def __UpperCAmelCase       (   self				:	Tuple)      ->					str:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	int=0   ,		__lowerCAmelCase				:	Dict=(4, 3, 512, 512)   ,		__lowerCAmelCase				:	str=False)      ->					Optional[int]:
         lowercase_     =      torch.floataa if fpaa else torch.floataa
         lowercase_     =      torch.from_numpy(load_hf_numpy(self.get_file_format(UpperCamelCase__   ,		UpperCamelCase__))).to(UpperCamelCase__).to(UpperCamelCase__)
         return image


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[int]="CompVis/stable-diffusion-v1-4"   ,		__lowerCAmelCase				:	Tuple=False)      ->					str:
         lowercase_     =      "fp16" if fpaa else None
         lowercase_     =      torch.floataa if fpaa else torch.floataa

         lowercase_     =      AutoencoderKL.from_pretrained(
             UpperCamelCase__   ,		subfolder="vae"   ,		torch_dtype=UpperCamelCase__   ,		revision=UpperCamelCase__   ,		)
         model.to(UpperCamelCase__).eval()

         return model


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	List[str]=0)      ->					Union[str, Any]:







         if torch_device == "mps":
               return torch.manual_seed(UpperCamelCase__)
         return torch.Generator(device=UpperCamelCase__).manual_seed(UpperCamelCase__)


   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
           [47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
           # fmt: on
       ])
   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Tuple)      ->					Any:
         lowercase_     =      self.get_sd_vae_model()
         lowercase_     =      self.get_sd_image(UpperCamelCase__)
         lowercase_     =      self.get_generator(UpperCamelCase__)

         with torch.no_grad():
               lowercase_     =      model(UpperCamelCase__   ,		generator=UpperCamelCase__   ,		sample_posterior=UpperCamelCase__).sample

         assert sample.shape == image.shape

         lowercase_     =      sample[-1, -2:, -2:, :2].flatten().float().cpu()
         lowercase_     =      torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=3e-3)


   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
           [47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
           # fmt: on
       ])
   @require_torch_gpu
   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Any)      ->					Optional[Any]:
         lowercase_     =      self.get_sd_vae_model(fpaa=UpperCamelCase__)
         lowercase_     =      self.get_sd_image(UpperCamelCase__   ,		fpaa=UpperCamelCase__)
         lowercase_     =      self.get_generator(UpperCamelCase__)

         with torch.no_grad():
               lowercase_     =      model(UpperCamelCase__   ,		generator=UpperCamelCase__   ,		sample_posterior=UpperCamelCase__).sample

         assert sample.shape == image.shape

         lowercase_     =      sample[-1, -2:, :2, -2:].flatten().float().cpu()
         lowercase_     =      torch.tensor(UpperCamelCase__)

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=1e-2)


   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
           [47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
           # fmt: on
       ])
   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[int])      ->					Optional[int]:
         lowercase_     =      self.get_sd_vae_model()
         lowercase_     =      self.get_sd_image(UpperCamelCase__)

         with torch.no_grad():
               lowercase_     =      model(UpperCamelCase__).sample

         assert sample.shape == image.shape

         lowercase_     =      sample[-1, -2:, -2:, :2].flatten().float().cpu()
         lowercase_     =      torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=3e-3)


   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
           [37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
           # fmt: on
       ])
   @require_torch_gpu
   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Optional[int])      ->					List[str]:
         lowercase_     =      self.get_sd_vae_model()
         lowercase_     =      self.get_sd_image(UpperCamelCase__   ,		shape=(3, 4, 64, 64))

         with torch.no_grad():
               lowercase_     =      model.decode(UpperCamelCase__).sample

         assert list(sample.shape) == [3, 3, 512, 512]

         lowercase_     =      sample[-1, -2:, :2, -2:].flatten().cpu()
         lowercase_     =      torch.tensor(UpperCamelCase__)

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=1e-3)


   @parameterized.expand(
       [
           # fmt: off
           [27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
           [16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
           # fmt: on
       ])
   @require_torch_gpu
   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str])      ->					str:
         lowercase_     =      self.get_sd_vae_model(fpaa=UpperCamelCase__)
         lowercase_     =      self.get_sd_image(UpperCamelCase__   ,		shape=(3, 4, 64, 64)   ,		fpaa=UpperCamelCase__)

         with torch.no_grad():
               lowercase_     =      model.decode(UpperCamelCase__).sample

         assert list(sample.shape) == [3, 3, 512, 512]

         lowercase_     =      sample[-1, -2:, :2, -2:].flatten().float().cpu()
         lowercase_     =      torch.tensor(UpperCamelCase__)

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=5e-3)


   @parameterized.expand([(13,), (16,), (27,)])
   @require_torch_gpu
   @unittest.skipIf(not is_xformers_available()   ,		reason="xformers is not required when using PyTorch 2.0.")
   def __UpperCAmelCase       (   self				:	int   ,		__lowerCAmelCase				:	List[str])      ->					Any:
         lowercase_     =      self.get_sd_vae_model(fpaa=UpperCamelCase__)
         lowercase_     =      self.get_sd_image(UpperCamelCase__   ,		shape=(3, 4, 64, 64)   ,		fpaa=UpperCamelCase__)

         with torch.no_grad():
               lowercase_     =      model.decode(UpperCamelCase__).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
               lowercase_     =      model.decode(UpperCamelCase__).sample

         assert list(sample.shape) == [3, 3, 512, 512]

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=1e-1)


   @parameterized.expand([(13,), (16,), (37,)])
   @require_torch_gpu
   @unittest.skipIf(not is_xformers_available()   ,		reason="xformers is not required when using PyTorch 2.0.")
   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[str])      ->					str:
         lowercase_     =      self.get_sd_vae_model()
         lowercase_     =      self.get_sd_image(UpperCamelCase__   ,		shape=(3, 4, 64, 64))

         with torch.no_grad():
               lowercase_     =      model.decode(UpperCamelCase__).sample

         model.enable_xformers_memory_efficient_attention()
         with torch.no_grad():
               lowercase_     =      model.decode(UpperCamelCase__).sample

         assert list(sample.shape) == [3, 3, 512, 512]

         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=1e-2)


   @parameterized.expand(
       [
           # fmt: off
           [33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
           [47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
           # fmt: on
       ])
   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	List[Any])      ->					Optional[Any]:
         lowercase_     =      self.get_sd_vae_model()
         lowercase_     =      self.get_sd_image(UpperCamelCase__)
         lowercase_     =      self.get_generator(UpperCamelCase__)

         with torch.no_grad():
               lowercase_     =      model.encode(UpperCamelCase__).latent_dist
               lowercase_     =      dist.sample(generator=UpperCamelCase__)

         assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

         lowercase_     =      sample[0, -1, -3:, -3:].flatten().cpu()
         lowercase_     =      torch.tensor(UpperCamelCase__)

         lowercase_     =      3e-3 if torch_device != "mps" else 1e-2
         assert torch_all_close(UpperCamelCase__   ,		UpperCamelCase__   ,		atol=UpperCamelCase__)







'''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_ : Optional[List[str]]						  =      None
lowerCAmelCase_ : str						  =      "<" 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_ : Any						  =      [
    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 							lowercase						:
   lowerCamelCase_    					=True
   lowerCamelCase_    					=None
   # Automatically constructed
   lowerCamelCase_    					="PIL.Image.Image"
   lowerCamelCase_    					=pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
   lowerCamelCase_    					=field(default='Image' ,				init=__lowerCamelCase ,				repr=__lowerCamelCase )


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


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

         if isinstance(__lowerCAmelCase   ,		__lowerCAmelCase):
               lowercase_     =      np.array(__lowerCAmelCase)

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

         lowercase_ ,							lowercase_     =      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(__lowerCAmelCase):
                           lowercase_     =      PIL.Image.open(__lowerCAmelCase)
                     else:
                           lowercase_     =      path.split("::")[-1]
                           try:
                                 lowercase_     =      string_to_dict(__lowerCAmelCase   ,		config.HUB_DATASETS_URL)["repo_id"]
                                 lowercase_     =      token_per_repo_id.get(__lowerCAmelCase)
                           except ValueError:
                                 lowercase_     =      None
                           with xopen(__lowerCAmelCase   ,		"rb"   ,		use_auth_token=__lowerCAmelCase) as f:
                                 lowercase_     =      BytesIO(f.read())
                           lowercase_     =      PIL.Image.open(bytes_)
         else:
               lowercase_     =      PIL.Image.open(BytesIO(bytes_))
         image.load()  # to avoid "Too many open files" errors
         return image


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

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


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


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	pa.StructArray)      ->					pa.StructArray:

         @no_op_if_value_is_null
         def path_to_bytes(__lowerCAmelCase				:	int):
               with xopen(__lowerCAmelCase   ,		"rb") as f:
                     lowercase_     =      f.read()
               return bytes_

         lowercase_     =      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()   ,		)
         lowercase_     =      pa.array(
             [os.path.basename(__lowerCAmelCase) if path is not None else None for path in storage.field("path").to_pylist()]   ,		type=pa.string()   ,		)
         lowercase_     =      pa.StructArray.from_arrays([bytes_array, path_array]   ,		["bytes", "path"]   ,		mask=bytes_array.is_null())
         return array_cast(__lowerCAmelCase   ,		self.pa_type)




def __a (	)	->							List[str]:
      '''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()
            lowercase_     =      list(set(PIL.Image.OPEN.keys()				) & set(PIL.Image.SAVE.keys()				)				)
      return _IMAGE_COMPRESSION_FORMATS


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							bytes:
      '''simple docstring'''


      lowercase_     =      BytesIO()
      if image.format in list_image_compression_formats():
            lowercase_     =      image.format
      else:
            lowercase_     =      "PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
      image.save(__lowerCamelCase ,		format=__lowerCamelCase				)
      return buffer.getvalue()


def __a (	__lowerCamelCase			:						"PIL.Image.Image"				)	->							dict:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						np.ndarray				)	->							dict:
      '''simple docstring'''


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

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

      lowercase_     =      None

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






def __a (	__lowerCamelCase			:						Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]				)	->							List[dict]:
      '''simple docstring'''


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

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






