'''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 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


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




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			str:



         '''simple docstring'''



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




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  '''cpu'''

         UpperCAmelCase__							:				int   				=  self.get_dummy_components()
         UpperCAmelCase__							:				str   				=  self.pipeline_class(**lowerCamelCase_							)
         pipe.to(lowerCamelCase_							)
         pipe.set_progress_bar_config(disable=lowerCamelCase_							)

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

         self.assertEqual(image.shape					,(1, 32, 32, 3)							)
         UpperCAmelCase__							:				List[str]   				=  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__							:				Union[str, Any]   				=  np.abs(image_slice.flatten() - expected_slice							).max()
         self.assertLessEqual(lowerCamelCase_					,1e-3							)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



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




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



         '''simple docstring'''



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




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



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



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




@slow
@require_torch_gpu
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  '''google/ddpm-cifar10-32'''

         UpperCAmelCase__							:				str   				=  UNetaDModel.from_pretrained(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  DDIMScheduler()

         UpperCAmelCase__							:				Optional[Any]   				=  DDIMPipeline(unet=lowerCamelCase_					,scheduler=lowerCamelCase_							)
         ddim.to(lowerCamelCase_							)
         ddim.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  torch.manual_seed(0							)
         UpperCAmelCase__							:				List[Any]   				=  ddim(generator=lowerCamelCase_					,eta=0.0					,output_type='''numpy'''							).images

         UpperCAmelCase__							:				str   				=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         UpperCAmelCase__							:				Optional[int]   				=  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 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  '''google/ddpm-ema-bedroom-256'''

         UpperCAmelCase__							:				List[Any]   				=  UNetaDModel.from_pretrained(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  DDIMScheduler.from_pretrained(lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[int]   				=  DDIMPipeline(unet=lowerCamelCase_					,scheduler=lowerCamelCase_							)
         ddpm.to(lowerCamelCase_							)
         ddpm.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[Any]   				=  torch.manual_seed(0							)
         UpperCAmelCase__							:				Tuple   				=  ddpm(generator=lowerCamelCase_					,output_type='''numpy'''							).images

         UpperCAmelCase__							:				List[str]   				=  image[0, -3:, -3:, -1]

         assert image.shape == (1, 256, 256, 3)
         UpperCAmelCase__							:				Optional[int]   				=  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 _LazyModule


UpperCamelCase__     :	Union[str, Any]				=			{'tokenization_byt5': ['ByT5Tokenizer']}


if TYPE_CHECKING:
  from .tokenization_byta import ByTaTokenizer
else:
  import sys

  UpperCamelCase__     :	List[str]				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




