






"""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       __a  (   __snake_case	,       unittest.TestCase	):
 _lowerCAmelCase	:	Dict				=			DDIMPipeline
 _lowerCAmelCase	:	Optional[Any]				=			UNCONDITIONAL_IMAGE_GENERATION_PARAMS
 _lowerCAmelCase	:	Optional[int]				=			PipelineTesterMixin.required_optional_params - {
     'num_images_per_prompt',
     'latents',
     'callback',
     'callback_steps',
 }
 _lowerCAmelCase	:	Optional[Any]				=			UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
 _lowerCAmelCase	:	int				=			False







 def  __lowercase (		self :       Optional[Any]						):


      '''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__      :       Dict	    =  DDIMScheduler()
      UpperCamelCase__      :       Optional[Any]	    =  {"unet": unet, "scheduler": scheduler}
      return components







 def  __lowercase (		self :       Optional[Any]							,       SCREAMING_SNAKE_CASE :       Optional[int]							,       SCREAMING_SNAKE_CASE :       Tuple=0						):


      '''simple docstring'''






      if str(SCREAMING_SNAKE_CASE						).startswith("mps"						):
           UpperCamelCase__      :       int	    =  torch.manual_seed(SCREAMING_SNAKE_CASE						)
      else:
           UpperCamelCase__      :       Union[str, Any]	    =  torch.Generator(device=SCREAMING_SNAKE_CASE						).manual_seed(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       str	    =  {
          "batch_size": 1,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       Optional[Any]	    =  "cpu"

      UpperCamelCase__      :       Any	    =  self.get_dummy_components()
      UpperCamelCase__      :       Dict	    =  self.pipeline_class(**SCREAMING_SNAKE_CASE						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[int]	    =  self.get_dummy_inputs(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Any	    =  pipe(**SCREAMING_SNAKE_CASE						).images
      UpperCamelCase__      :       Union[str, Any]	    =  image[0, -3:, -3:, -1]

      self.assertEqual(image.shape							,       (1, 32, 32, 3)						)
      UpperCamelCase__      :       List[Any]	    =  np.array(
          [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]						)
      UpperCamelCase__      :       Optional[int]	    =  np.abs(image_slice.flatten() - expected_slice						).max()
      self.assertLessEqual(SCREAMING_SNAKE_CASE							,       1e-3						)







 def  __lowercase (		self :       Tuple						):


      '''simple docstring'''






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







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






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







 def  __lowercase (		self :       List[str]						):


      '''simple docstring'''






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







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







 def  __lowercase (		self :       Any						):


      '''simple docstring'''






      UpperCamelCase__      :       str	    =  "google/ddpm-cifar10-32"

      UpperCamelCase__      :       List[Any]	    =  UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[Any]	    =  DDIMScheduler()

      UpperCamelCase__      :       Tuple	    =  DDIMPipeline(unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE						)
      ddim.to(SCREAMING_SNAKE_CASE						)
      ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Optional[Any]	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Any	    =  ddim(generator=SCREAMING_SNAKE_CASE							,       eta=0.0							,       output_type="numpy"						).images

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

      assert image.shape == (1, 32, 32, 3)
      UpperCamelCase__      :       Tuple	    =  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  __lowercase (		self :       Tuple						):


      '''simple docstring'''






      UpperCamelCase__      :       int	    =  "google/ddpm-ema-bedroom-256"

      UpperCamelCase__      :       List[Any]	    =  UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Optional[Any]	    =  DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       Dict	    =  DDIMPipeline(unet=SCREAMING_SNAKE_CASE							,       scheduler=SCREAMING_SNAKE_CASE						)
      ddpm.to(SCREAMING_SNAKE_CASE						)
      ddpm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__      :       List[str]	    =  torch.manual_seed(0						)
      UpperCamelCase__      :       Dict	    =  ddpm(generator=SCREAMING_SNAKE_CASE							,       output_type="numpy"						).images

      UpperCamelCase__      :       Tuple	    =  image[0, -3:, -3:, -1]

      assert image.shape == (1, 2_56, 2_56, 3)
      UpperCamelCase__      :       Optional[int]	    =  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







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate





@dataclass(frozen=A__	)
class       __a  (   A__	):
 _lowerCAmelCase	:	str				=			field(default='''language-modeling'''	,       metadata={'''include_in_asdict_even_if_is_default''': True}	)

 _lowerCAmelCase	:	ClassVar[Features]				=			Features({'''text''': Value('''string'''	)}	)
 _lowerCAmelCase	:	ClassVar[Features]				=			Features({}	)
 _lowerCAmelCase	:	str				=			"text"







 @property
 def  __lowercase (		self :       str						):


      '''simple docstring'''






      return {self.text_column: "text"}