


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

UpperCamelCase								=	logging.get_logger(__name__)






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "vision-encoder-decoder"
       snake_case      =    True



       def __init__(      self				,       **_SCREAMING_SNAKE_CASE		)->int:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)
         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_      :				Union[str, Any]		     =					kwargs.pop('''encoder'''		)
         A_      :				Optional[int]		     =					encoder_config.pop('''model_type'''		)
         A_      :				List[Any]		     =					kwargs.pop('''decoder'''		)
         A_      :				Tuple		     =					decoder_config.pop('''model_type'''		)

         A_      :				str		     =					AutoConfig.for_model(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					AutoConfig.for_model(_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					True



       @classmethod
       def        _snake_case       (      cls				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->PretrainedConfig:




         '''simple docstring'''





         logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
         A_      :				str		     =					True
         A_      :				List[str]		     =					True

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







       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





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






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    version.parse("1.11"			)



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




         '''simple docstring'''





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



       @property
       def        _snake_case       (      self		)->float:




         '''simple docstring'''





         return 1e-4







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




         '''simple docstring'''





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






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



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




         '''simple docstring'''





         A_      :				Optional[int]		     =					OrderedDict()
         A_      :				Optional[Any]		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         A_      :				Dict		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         A_      :				Dict		     =					{0: '''batch''', 1: '''encoder_sequence'''}

         return common_inputs







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




         '''simple docstring'''





         import torch

         A_      :				List[Any]		     =					OrderedDict()

         A_      :				Tuple		     =					super().generate_dummy_inputs(
             _SCREAMING_SNAKE_CASE				,       batch_size=_SCREAMING_SNAKE_CASE				,       seq_length=_SCREAMING_SNAKE_CASE				,       is_pair=_SCREAMING_SNAKE_CASE				,       framework=_SCREAMING_SNAKE_CASE		)

         A_     ,       A_      :				Optional[Any]		     =					dummy_input['''input_ids'''].shape
         A_      :				List[Any]		     =					(batch, encoder_sequence, self._config.encoder_hidden_size)
         A_      :				int		     =					dummy_input.pop('''input_ids'''		)
         A_      :				Optional[Any]		     =					dummy_input.pop('''attention_mask'''		)
         A_      :				Optional[int]		     =					torch.zeros(_SCREAMING_SNAKE_CASE		)

         return common_inputs






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       @property
       def        _snake_case       (      self		)->None:




         '''simple docstring'''





         pass



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->OnnxConfig:




         '''simple docstring'''





         return VisionEncoderDecoderEncoderOnnxConfig(_SCREAMING_SNAKE_CASE		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = "default"		)->OnnxConfig:




         '''simple docstring'''





         A_      :				List[str]		     =					encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)








import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    DDIMPipeline
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
       snake_case      =    PipelineTesterMixin.required_optional_params - {
           "num_images_per_prompt",
           "latents",
           "callback",
           "callback_steps",
       }
       snake_case      =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
       snake_case      =    False



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				List[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''')				,       )
         A_      :				Optional[Any]		     =					DDIMScheduler()
         A_      :				str		     =					{'''unet''': unet, '''scheduler''': scheduler}
         return components



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0		)->Optional[Any]:




         '''simple docstring'''





         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Any		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				Optional[int]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					{
             '''batch_size''': 1,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					'''cpu'''

         A_      :				Dict		     =					self.get_dummy_components()
         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         pipe.to(_SCREAMING_SNAKE_CASE		)
         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				str		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe(**_SCREAMING_SNAKE_CASE		).images
         A_      :				int		     =					image[0, -3:, -3:, -1]

         self.assertEqual(image.shape				,       (1, 32, 32, 3)		)
         A_      :				List[Any]		     =					np.array(
             [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]		)
         A_      :				str		     =					np.abs(image_slice.flatten() - expected_slice		).max()
         self.assertLessEqual(_SCREAMING_SNAKE_CASE				,       1e-3		)



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_local(expected_max_difference=3e-3		)



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




         '''simple docstring'''





         super().test_save_load_optional_components(expected_max_difference=3e-3		)







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         super().test_inference_batch_single_identical(expected_max_diff=3e-3		)






@slow
@require_torch_gpu
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				int		     =					'''google/ddpm-cifar10-32'''

         A_      :				Tuple		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					DDIMScheduler()

         A_      :				str		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddim.to(_SCREAMING_SNAKE_CASE		)
         ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					torch.manual_seed(0		)
         A_      :				Any		     =					ddim(generator=_SCREAMING_SNAKE_CASE				,       eta=0.0				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_      :				Any		     =					np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					'''google/ddpm-ema-bedroom-256'''

         A_      :				int		     =					UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					DDIMPipeline(unet=_SCREAMING_SNAKE_CASE				,       scheduler=_SCREAMING_SNAKE_CASE		)
         ddpm.to(_SCREAMING_SNAKE_CASE		)
         ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					torch.manual_seed(0		)
         A_      :				List[str]		     =					ddpm(generator=_SCREAMING_SNAKE_CASE				,       output_type='''numpy'''		).images

         A_      :				Any		     =					image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         A_      :				Tuple		     =					np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2




