



"""simple docstring"""
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       SCREAMING_SNAKE_CASE__  (					lowerCamelCase_      ,			unittest.TestCase ):
   __lowerCAmelCase  :   List[str]						=						DDIMPipeline
   __lowerCAmelCase  :   Optional[Any]						=						UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   __lowerCAmelCase  :   Union[str, Any]						=						PipelineTesterMixin.required_optional_params - {
       'num_images_per_prompt',
       'latents',
       'callback',
       'callback_steps',
   }
   __lowerCAmelCase  :   Tuple						=						UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   __lowerCAmelCase  :   List[Any]						=						False

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





    '''simple docstring'''




    torch.manual_seed(0						)
    UpperCAmelCase	:					int	=	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""")      ,     )
    UpperCAmelCase	:					List[str]	=	DDIMScheduler()
    UpperCAmelCase	:					Dict	=	{'''unet''': unet, '''scheduler''': scheduler}
    return components

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





    '''simple docstring'''




    if str(__SCREAMING_SNAKE_CASE						).startswith("""mps"""						):
     UpperCAmelCase	:					List[str]	=	torch.manual_seed(__SCREAMING_SNAKE_CASE						)
    else:
     UpperCAmelCase	:					List[Any]	=	torch.Generator(device=__SCREAMING_SNAKE_CASE						).manual_seed(__SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Dict	=	{
        '''batch_size''': 1,
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''output_type''': '''numpy''',
    }
    return inputs

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	'''cpu'''

    UpperCAmelCase	:					Optional[int]	=	self.get_dummy_components()
    UpperCAmelCase	:					Union[str, Any]	=	self.pipeline_class(**__SCREAMING_SNAKE_CASE						)
    pipe.to(__SCREAMING_SNAKE_CASE						)
    pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	self.get_dummy_inputs(__SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Union[str, Any]	=	pipe(**__SCREAMING_SNAKE_CASE						).images
    UpperCAmelCase	:					int	=	image[0, -3:, -3:, -1]

    self.assertEqual(image.shape      ,     (1, 32, 32, 3)						)
    UpperCAmelCase	:					Union[str, 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]						)
    UpperCAmelCase	:					Tuple	=	np.abs(image_slice.flatten() - expected_slice						).max()
    self.assertLessEqual(__SCREAMING_SNAKE_CASE      ,     1E-3						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




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

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





    '''simple docstring'''




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




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





    '''simple docstring'''




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



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

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	'''google/ddpm-cifar10-32'''

    UpperCAmelCase	:					Union[str, Any]	=	UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	DDIMScheduler()

    UpperCAmelCase	:					Union[str, Any]	=	DDIMPipeline(unet=__SCREAMING_SNAKE_CASE      ,     scheduler=__SCREAMING_SNAKE_CASE						)
    ddim.to(__SCREAMING_SNAKE_CASE						)
    ddim.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					int	=	torch.manual_seed(0						)
    UpperCAmelCase	:					Tuple	=	ddim(generator=__SCREAMING_SNAKE_CASE      ,     eta=0.0      ,     output_type="""numpy"""						).images

    UpperCAmelCase	:					Union[str, Any]	=	image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    UpperCAmelCase	:					Union[str, Any]	=	np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]						)

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




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





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	'''google/ddpm-ema-bedroom-256'''

    UpperCAmelCase	:					Dict	=	UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Optional[Any]	=	DDIMScheduler.from_pretrained(__SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Union[str, Any]	=	DDIMPipeline(unet=__SCREAMING_SNAKE_CASE      ,     scheduler=__SCREAMING_SNAKE_CASE						)
    ddpm.to(__SCREAMING_SNAKE_CASE						)
    ddpm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					str	=	torch.manual_seed(0						)
    UpperCAmelCase	:					str	=	ddpm(generator=__SCREAMING_SNAKE_CASE      ,     output_type="""numpy"""						).images

    UpperCAmelCase	:					Optional[Any]	=	image[0, -3:, -3:, -1]

    assert image.shape == (1, 256, 256, 3)
    UpperCAmelCase	:					Tuple	=	np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]						)

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


'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Dict    							=       ["Pix2StructImageProcessor"]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  List[str]    							=       [
							    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Pix2StructPreTrainedModel",
							    "Pix2StructForConditionalGeneration",
							    "Pix2StructVisionModel",
							    "Pix2StructTextModel",
							]

if TYPE_CHECKING:
							from .configuration_pixastruct import (
							    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    PixaStructConfig,
							    PixaStructTextConfig,
							    PixaStructVisionConfig,
							)
							from .processing_pixastruct import PixaStructProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_pixastruct import PixaStructImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pixastruct import (
														    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PixaStructForConditionalGeneration,
														    PixaStructPreTrainedModel,
														    PixaStructTextModel,
														    PixaStructVisionModel,
														)

else:
							import sys

							_lowercase :  Optional[int]    							=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



