"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class    __UpperCAmelCase						(	unittest.TestCase	):

				@property
				def 		_a	(      self      ):
										lowerCamelCase__					=1
										lowerCamelCase__					=3
										lowerCamelCase__					=8
										lowerCamelCase__					=8

										lowerCamelCase__					=torch.rand((batch_size, num_channels, height, width)      )

										return sample

				def 		_a	(      self	, _lowerCamelCase      ):
										if isinstance(image[0]	, PIL.Image.Image      ):
																return np.stack([np.array(_lowerCamelCase      ) for i in image]	, axis=0      )
										elif isinstance(_lowerCamelCase	, torch.Tensor      ):
																return image.cpu().numpy().transpose(0	, 2	, 3	, 1      )
										return image

				def 		_a	(      self      ):
										lowerCamelCase__					=VaeImageProcessor(do_resize=_lowerCamelCase	, do_normalize=_lowerCamelCase      )

										lowerCamelCase__					=self.dummy_sample
										lowerCamelCase__					=self.to_np(_lowerCamelCase      )

										for output_type in ["pt", "np", "pil"]:
																lowerCamelCase__					=image_processor.postprocess(
																    image_processor.preprocess(_lowerCamelCase      )	, output_type=_lowerCamelCase	, )
																lowerCamelCase__					=self.to_np(_lowerCamelCase      )
																lowerCamelCase__					=(input_np * 255).round() if output_type == "pil" else input_np
																assert (
																    np.abs(in_np - out_np      ).max() < 1E-6
																), F'''decoded output does not match input for output_type {output_type}'''

				def 		_a	(      self      ):
										lowerCamelCase__					=VaeImageProcessor(do_resize=_lowerCamelCase	, do_normalize=_lowerCamelCase      )
										lowerCamelCase__					=self.dummy_sample.cpu().numpy().transpose(0	, 2	, 3	, 1      )

										for output_type in ["pt", "np", "pil"]:
																lowerCamelCase__					=image_processor.postprocess(image_processor.preprocess(_lowerCamelCase      )	, output_type=_lowerCamelCase      )

																lowerCamelCase__					=self.to_np(_lowerCamelCase      )
																lowerCamelCase__					=(input_np * 255).round() if output_type == "pil" else input_np
																assert (
																    np.abs(in_np - out_np      ).max() < 1E-6
																), F'''decoded output does not match input for output_type {output_type}'''

				def 		_a	(      self      ):
										lowerCamelCase__					=VaeImageProcessor(do_resize=_lowerCamelCase	, do_normalize=_lowerCamelCase      )

										lowerCamelCase__					=self.dummy_sample.cpu().numpy().transpose(0	, 2	, 3	, 1      )
										lowerCamelCase__					=image_processor.numpy_to_pil(_lowerCamelCase      )

										for output_type in ["pt", "np", "pil"]:
																lowerCamelCase__					=image_processor.postprocess(image_processor.preprocess(_lowerCamelCase      )	, output_type=_lowerCamelCase      )
																for i, o in zip(_lowerCamelCase	, _lowerCamelCase      ):
																						lowerCamelCase__					=np.array(_lowerCamelCase      )
																						lowerCamelCase__					=self.to_np(_lowerCamelCase      ) if output_type == "pil" else (self.to_np(_lowerCamelCase      ) * 255).round()
																						assert (
																						    np.abs(in_np - out_np      ).max() < 1E-6
																						), F'''decoded output does not match input for output_type {output_type}'''

				def 		_a	(      self      ):
										lowerCamelCase__					=VaeImageProcessor(do_resize=_lowerCamelCase	, do_normalize=_lowerCamelCase      )

										lowerCamelCase__					=self.dummy_sample
										lowerCamelCase__					=input_pt_ad.squeeze(0      )

										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )
										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )

										lowerCamelCase__					=self.to_np(self.dummy_sample      )
										lowerCamelCase__					=input_np_ad.squeeze(0      )

										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )
										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )

										assert np.abs(out_pt_ad - out_pt_ad      ).max() < 1E-6
										assert np.abs(out_np_ad - out_np_ad      ).max() < 1E-6






				def 		_a	(      self      ):
										lowerCamelCase__					=VaeImageProcessor(do_resize=_lowerCamelCase	, do_normalize=_lowerCamelCase      )

										lowerCamelCase__					=self.dummy_sample
										lowerCamelCase__					=list(_lowerCamelCase      )

										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )

										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )

										lowerCamelCase__					=self.to_np(self.dummy_sample      )
										list(_lowerCamelCase      )

										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )

										lowerCamelCase__					=image_processor.postprocess(
										    image_processor.preprocess(_lowerCamelCase      )	, output_type="np"	, )

										assert np.abs(out_pt_ad - out_pt_list      ).max() < 1E-6
										assert np.abs(out_np_ad - out_np_list      ).max() < 1E-6



"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a							=logging.get_logger(__name__)






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Any  							=       '''hybrid-clip'''
				A__      :   Optional[Any]  							=       True

				def __init__(      self	, _lowerCamelCase=512	, **_lowerCamelCase      ):
										super().__init__(**_lowerCamelCase      )

										if "text_config" not in kwargs:
																raise ValueError("`text_config` can not be `None`."      )

										if "vision_config" not in kwargs:
																raise ValueError("`vision_config` can not be `None`."      )

										lowerCamelCase__					=kwargs.pop("text_config"      )
										lowerCamelCase__					=kwargs.pop("vision_config"      )

										lowerCamelCase__					=text_config.pop("model_type"      )
										lowerCamelCase__					=vision_config.pop("model_type"      )

										from transformers import AutoConfig

										lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										if vision_model_type == "clip":
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      ).vision_config
										elif vision_model_type == "clip_vision_model":
																from transformers import CLIPVisionConfig

																lowerCamelCase__					=CLIPVisionConfig(**_lowerCamelCase      )
										else:
																lowerCamelCase__					=AutoConfig.for_model(_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=projection_dim
										lowerCamelCase__					=1.0

				@classmethod
				def 		_a	(      cls	, _lowerCamelCase	, _lowerCamelCase	, **_lowerCamelCase      ):
										return cls(text_config=text_config.to_dict()	, vision_config=vision_config.to_dict()	, **_lowerCamelCase      )






				def 		_a	(      self      ):
										lowerCamelCase__					=copy.deepcopy(self.__dict__      )
										lowerCamelCase__					=self.text_config.to_dict()
										lowerCamelCase__					=self.vision_config.to_dict()
										lowerCamelCase__					=self.__class__.model_type
										return output


