"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
	from ... import PreTrainedTokenizerBase, TensorType

lowercase__ 	=     logging.get_logger(__name__)




class        __snake_case	(					__lowerCAmelCase    ):
						a__				=		"""vision-encoder-decoder"""
						a__				=		True



						def __init__(						self   ,      **lowercase)     ->      Optional[int]:





								'''simple docstring'''





								super().__init__(**lowercase)
								if "encoder" not in kwargs or "decoder" not in kwargs:
										raise ValueError(
										    f'A configuraton of type {self.model_type} cannot be instantiated because '
										    f'not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}')

								a__:					List[Any]      							=     kwargs.pop('encoder')
								a__:					int      							=     encoder_config.pop('model_type')
								a__:					List[Any]      							=     kwargs.pop('decoder')
								a__:					str      							=     decoder_config.pop('model_type')

								a__:					Tuple      							=     AutoConfig.for_model(lowercase   ,      **lowercase)
								a__:					Optional[Any]      							=     AutoConfig.for_model(lowercase   ,      **lowercase)
								a__:					str      							=     True



						@classmethod
						def   lowerCamelCase_       (						cls   ,      lowercase   ,      lowercase   ,      **lowercase)     ->      PretrainedConfig:





								'''simple docstring'''





								logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config')
								a__:					int      							=     True
								a__:					Tuple      							=     True

								return cls(encoder=encoder_config.to_dict()   ,      decoder=decoder_config.to_dict()   ,      **lowercase)



						def   lowerCamelCase_       (						self)     ->      Optional[int]:





								'''simple docstring'''





								a__:					List[str]      							=     copy.deepcopy(self.__dict__)
								a__:					int      							=     self.encoder.to_dict()
								a__:					List[Any]      							=     self.decoder.to_dict()
								a__:					List[Any]      							=     self.__class__.model_type
								return output







class        __snake_case	(					__lowerCAmelCase    ):
						a__				=		version.parse("""1.11"""    )



						@property
						def   lowerCamelCase_       (						self)     ->      Mapping[str, Mapping[int, str]]:





								'''simple docstring'''





								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])



						@property
						def   lowerCamelCase_       (						self)     ->      float:





								'''simple docstring'''





								return 1e-4



						@property
						def   lowerCamelCase_       (						self)     ->      Mapping[str, Mapping[int, str]]:





								'''simple docstring'''





								return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}})







class        __snake_case	(					__lowerCAmelCase    ):



						@property
						def   lowerCamelCase_       (						self)     ->      Mapping[str, Mapping[int, str]]:





								'''simple docstring'''





								a__:					Tuple      							=     OrderedDict()
								a__:					List[str]      							=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
								a__:					Any      							=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
								a__:					Any      							=     {0: 'batch', 1: 'encoder_sequence'}

								return common_inputs



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = -1   ,      lowercase = -1   ,      lowercase = False   ,      lowercase = None   ,      )     ->      Mapping[str, Any]:





								'''simple docstring'''





								import torch

								a__:					Union[str, Any]      							=     OrderedDict()

								a__:					str      							=     super().generate_dummy_inputs(
								    lowercase   ,      batch_size=lowercase   ,      seq_length=lowercase   ,      is_pair=lowercase   ,      framework=lowercase)

								a__:					Optional[int]      							=     dummy_input['input_ids'].shape
								a__:					List[str]      							=     (batch, encoder_sequence, self._config.encoder_hidden_size)
								a__:					Tuple      							=     dummy_input.pop('input_ids')
								a__:					Union[str, Any]      							=     dummy_input.pop('attention_mask')
								a__:					List[Any]      							=     torch.zeros(lowercase)

								return common_inputs







class        __snake_case	(					__lowerCAmelCase    ):



						@property
						def   lowerCamelCase_       (						self)     ->      None:





								'''simple docstring'''





								pass



						def   lowerCamelCase_       (						self   ,      lowercase)     ->      OnnxConfig:





								'''simple docstring'''





								return VisionEncoderDecoderEncoderOnnxConfig(lowercase)



						def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = "default")     ->      OnnxConfig:





								'''simple docstring'''





								a__:					Any      							=     encoder_config.hidden_size
								return VisionEncoderDecoderDecoderOnnxConfig(lowercase   ,      lowercase)



"""simple docstring"""
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class        __snake_case	(					unittest.TestCase    ):



      @property
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					str      							=     UNetaDModel(
            block_out_channels=(32, 64)   ,      layers_per_block=2   ,      sample_size=32   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					List[Any]      							=     VQModel(
            block_out_channels=[32, 64]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=3   ,      )
        return model



      @property
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:					Optional[int]      							=     CLIPTextConfig(
            bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=32   ,      intermediate_size=37   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=10_00   ,      )
        return CLIPTextModel(lowercase)



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.dummy_uncond_unet
        a__:					Optional[int]      							=     DDIMScheduler()
        a__:					Optional[int]      							=     self.dummy_vq_model

        a__:					Union[str, Any]      							=     LDMPipeline(unet=lowercase   ,      vqvae=lowercase   ,      scheduler=lowercase)
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					str      							=     torch.manual_seed(0)
        a__:					Dict      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy').images

        a__:					Union[str, Any]      							=     torch.manual_seed(0)
        a__:					int      							=     ldm(generator=lowercase   ,      num_inference_steps=2   ,      output_type='numpy'   ,      return_dict=lowercase)[0]

        a__:					Union[str, Any]      							=     image[0, -3:, -3:, -1]
        a__:					int      							=     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__:					int      							=     np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
        a__:					Optional[Any]      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance







@slow
@require_torch
class        __snake_case	(					unittest.TestCase    ):



      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256')
        ldm.to(lowercase)
        ldm.set_progress_bar_config(disable=lowercase)

        a__:					List[str]      							=     torch.manual_seed(0)
        a__:					Optional[int]      							=     ldm(generator=lowercase   ,      num_inference_steps=5   ,      output_type='numpy').images

        a__:					Optional[int]      							=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        a__:					int      							=     np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
        a__:					Any      							=     1e-2 if torch_device != 'mps' else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance


