


"""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__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					DDIMPipeline
   __UpperCAmelCase							       =					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   __UpperCAmelCase							       =					PipelineTesterMixin.required_optional_params - {
       '''num_images_per_prompt''',
       '''latents''',
       '''callback''',
       '''callback_steps''',
   }
   __UpperCAmelCase							       =					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      torch.manual_seed(0)
      _lowerCamelCase	:					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""")      ,   )
      _lowerCamelCase	:					List[Any]   =		DDIMScheduler()
      _lowerCamelCase	:					List[str]   =		{"""unet""": unet, """scheduler""": scheduler}
      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  str:
      if str(SCREAMING_SNAKE_CASE).startswith("""mps"""):
         _lowerCamelCase	:					List[Any]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					Optional[Any]   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		{
          """batch_size""": 1,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }
      return inputs





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Optional[Any]   =		"""cpu"""

      _lowerCamelCase	:					Any   =		self.get_dummy_components()
      _lowerCamelCase	:					List[Any]   =		self.pipeline_class(**SCREAMING_SNAKE_CASE)
      pipe.to(SCREAMING_SNAKE_CASE)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		pipe(**SCREAMING_SNAKE_CASE).images
      _lowerCamelCase	:					Dict   =		image[0, -3:, -3:, -1]

      self.assertEqual(image.shape      ,   (1, 32, 32, 3))
      _lowerCamelCase	:					int   =		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])
      _lowerCamelCase	:					List[str]   =		np.abs(image_slice.flatten() - expected_slice).max()
      self.assertLessEqual(SCREAMING_SNAKE_CASE      ,   1e-3)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      super().test_save_load_local(expected_max_difference=3e-3)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      super().test_save_load_optional_components(expected_max_difference=3e-3)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3e-3)







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





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Any   =		"""google/ddpm-cifar10-32"""

      _lowerCamelCase	:					int   =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[int]   =		DDIMScheduler()

      _lowerCamelCase	:					List[str]   =		DDIMPipeline(unet=SCREAMING_SNAKE_CASE      ,   scheduler=SCREAMING_SNAKE_CASE)
      ddim.to(SCREAMING_SNAKE_CASE)
      ddim.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

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

      _lowerCamelCase	:					Dict   =		image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      _lowerCamelCase	:					int   =		np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

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





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Union[str, Any]   =		"""google/ddpm-ema-bedroom-256"""

      _lowerCamelCase	:					Tuple   =		UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					List[str]   =		DDIMScheduler.from_pretrained(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					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)

      _lowerCamelCase	:					Any   =		torch.manual_seed(0)
      _lowerCamelCase	:					int   =		ddpm(generator=SCREAMING_SNAKE_CASE      ,   output_type="""numpy""").images

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

      assert image.shape == (1, 256, 256, 3)
      _lowerCamelCase	:					str   =		np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

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







'''simple docstring'''





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[str] = None       )		->					str:

     """simple docstring"""




     if version.parse(hfh.__version__       ).release < version.parse("0.11.0"       ).release:
          # old versions of hfh don't url-encode the file path
          _snake_case				= quote(_UpperCamelCase       )
     return hfh.hf_hub_url(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"   ,		revision=_UpperCamelCase       )


