

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   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			int      		=       DDIMPipeline
      _lowerCamelCase       :			Union[str, Any]      		=       UNCONDITIONAL_IMAGE_GENERATION_PARAMS
      _lowerCamelCase       :			Any      		=       PipelineTesterMixin.required_optional_params - {
          'num_images_per_prompt',
          'latents',
          'callback',
          'callback_steps',
      }
      _lowerCamelCase       :			List[Any]      		=       UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
      _lowerCamelCase       :			Optional[int]      		=       False





      def 		__A	(   self							:				Any	):
            torch.manual_seed(0	)
            A_						  =				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_						  =				DDIMScheduler()
            A_						  =				{"unet": unet, "scheduler": scheduler}
            return components





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Optional[int]=0	):
            if str(UpperCAmelCase	).startswith("mps"	):
                  A_						  =				torch.manual_seed(UpperCAmelCase	)
            else:
                  A_						  =				torch.Generator(device=UpperCAmelCase	).manual_seed(UpperCAmelCase	)
            A_						  =				{
                "batch_size": 1,
                "generator": generator,
                "num_inference_steps": 2,
                "output_type": "numpy",
            }
            return inputs





      def 		__A	(   self							:				str	):
            A_						  =				"cpu"

            A_						  =				self.get_dummy_components()
            A_						  =				self.pipeline_class(**UpperCAmelCase	)
            pipe.to(UpperCAmelCase	)
            pipe.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				self.get_dummy_inputs(UpperCAmelCase	)
            A_						  =				pipe(**UpperCAmelCase	).images
            A_						  =				image[0, -3:, -3:, -1]

            self.assertEqual(image.shape						,			(1, 32, 32, 3)	)
            A_						  =				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_						  =				np.abs(image_slice.flatten() - expected_slice	).max()
            self.assertLessEqual(UpperCAmelCase						,			1E-3	)





      def 		__A	(   self							:				Any	):
            super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3	)





      def 		__A	(   self							:				Optional[int]	):
            super().test_save_load_local(expected_max_difference=3E-3	)





      def 		__A	(   self							:				Optional[int]	):
            super().test_save_load_optional_components(expected_max_difference=3E-3	)







      def 		__A	(   self							:				Any	):
            super().test_inference_batch_single_identical(expected_max_diff=3E-3	)




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




      """simple docstring"""

      def 		__A	(   self							:				Any	):
            A_						  =				"google/ddpm-cifar10-32"

            A_						  =				UNetaDModel.from_pretrained(UpperCAmelCase	)
            A_						  =				DDIMScheduler()

            A_						  =				DDIMPipeline(unet=UpperCAmelCase						,			scheduler=UpperCAmelCase	)
            ddim.to(UpperCAmelCase	)
            ddim.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				torch.manual_seed(0	)
            A_						  =				ddim(generator=UpperCAmelCase						,			eta=0.0						,			output_type="numpy"	).images

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

            assert image.shape == (1, 32, 32, 3)
            A_						  =				np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]	)

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







      def 		__A	(   self							:				Optional[Any]	):
            A_						  =				"google/ddpm-ema-bedroom-256"

            A_						  =				UNetaDModel.from_pretrained(UpperCAmelCase	)
            A_						  =				DDIMScheduler.from_pretrained(UpperCAmelCase	)

            A_						  =				DDIMPipeline(unet=UpperCAmelCase						,			scheduler=UpperCAmelCase	)
            ddpm.to(UpperCAmelCase	)
            ddpm.set_progress_bar_config(disable=UpperCAmelCase	)

            A_						  =				torch.manual_seed(0	)
            A_						  =				ddpm(generator=UpperCAmelCase						,			output_type="numpy"	).images

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

            assert image.shape == (1, 256, 256, 3)
            A_						  =				np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]	)

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_instructblip': [
        'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'InstructBlipConfig',
        'InstructBlipQFormerConfig',
        'InstructBlipVisionConfig',
    ],
    'processing_instructblip': ['InstructBlipProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'InstructBlipQFormerModel',
			    'InstructBlipPreTrainedModel',
			    'InstructBlipForConditionalGeneration',
			    'InstructBlipVisionModel',
			]

if TYPE_CHECKING:
			from .configuration_instructblip import (
			    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    InstructBlipConfig,
			    InstructBlipQFormerConfig,
			    InstructBlipVisionConfig,
			)
			from .processing_instructblip import InstructBlipProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
						    InstructBlipForConditionalGeneration,
						    InstructBlipPreTrainedModel,
						    InstructBlipQFormerModel,
						    InstructBlipVisionModel,
						)

else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)