


'''simple docstring'''







import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
 import torch

 from transformers import AutoModelForImageClassification

if is_vision_available():
 from transformers import AutoImageProcessor


@require_torch
@require_vision
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""						)
       __a       =   AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""						)
       model.to(A__						)

       from datasets import load_dataset

       __a       =   load_dataset("""nielsr/rvlcdip-demo"""						)

       __a       =   dataset["""train"""][0]["""image"""].convert("""RGB"""						)

       __a       =   image_processor(A__				,							return_tensors="""pt"""						).to(A__						)

       # forward pass
       with torch.no_grad():
            __a       =   model(**A__						)
            __a       =   outputs.logits

       __a       =   torch.Size((1, 1_6)						)
       self.assertEqual(logits.shape				,							A__						)

       __a       =   torch.tensor(
           [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]				,							device=A__				,							dtype=torch.float				,							)
       self.assertTrue(torch.allclose(logits[0, :3]				,							A__				,							atol=1E-4						)						)









'''simple docstring'''







import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[int]												=ConsistencyModelPipeline
  a_       :List[Any]												=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
  a_       :Optional[int]												=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

  # Override required_optional_params to remove num_images_per_prompt
  a_       :Optional[Any]												=frozenset(
      [
          """num_inference_steps""",
          """generator""",
          """latents""",
          """output_type""",
          """return_dict""",
          """callback""",
          """callback_steps""",
      ]      )

  @property
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained(
           """diffusers/consistency-models-test"""				,							subfolder="""test_unet"""				,							)
       return unet

  @property
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained(
           """diffusers/consistency-models-test"""				,							subfolder="""test_unet_class_cond"""				,							)
       return unet

  def __a						(					self     :							str				,							SCREAMING_SNAKE_CASE__     :							int=False						):





       '''simple docstring'''

       if class_cond:
            __a       =   self.dummy_cond_unet
       else:
            __a       =   self.dummy_uncond_unet

       # Default to CM multistep sampler
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)

       __a       =   {
           """unet""": unet,
           """scheduler""": scheduler,
       }

       return components

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Dict				,							SCREAMING_SNAKE_CASE__     :							int=0						):





       '''simple docstring'''

       if str(SCREAMING_SNAKE_CASE__						).startswith("""mps"""						):
            __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)
       else:
            __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)

       __a       =   {
           """batch_size""": 1,
           """num_inference_steps""": None,
           """timesteps""": [2_2, 0],
           """generator""": generator,
           """output_type""": """np""",
       }

       return inputs

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components()
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3

  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__						)
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   0
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.3_5_7_2, 0.6_2_7_3, 0.4_0_3_1, 0.3_9_6_1, 0.4_3_2_1, 0.5_7_3_0, 0.5_2_6_6, 0.4_7_8_0, 0.5_0_0_4]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components()
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   1
       __a       =   None
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
       __a       =   self.get_dummy_components(class_cond=SCREAMING_SNAKE_CASE__						)
       __a       =   ConsistencyModelPipeline(**SCREAMING_SNAKE_CASE__						)
       __a       =   pipe.to(SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_dummy_inputs(SCREAMING_SNAKE_CASE__						)
       __a       =   1
       __a       =   None
       __a       =   0
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 3_2, 3_2, 3)

       __a       =   image[0, -3:, -3:, -1]
       __a       =   np.array([0.5_0_0_4, 0.5_0_0_4, 0.4_9_9_4, 0.5_0_0_8, 0.4_9_7_6, 0.5_0_1_8, 0.4_9_9_0, 0.4_9_8_2, 0.4_9_8_7]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


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





  """simple docstring"""



  def __a						(					self     :							int						):





       '''simple docstring'''

       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							Dict=0				,							SCREAMING_SNAKE_CASE__     :							str=False				,							SCREAMING_SNAKE_CASE__     :							int="cpu"				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=torch.floataa				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=(1, 3, 6_4, 6_4)						):





       '''simple docstring'''

       __a       =   torch.manual_seed(SCREAMING_SNAKE_CASE__						)

       __a       =   {
           """num_inference_steps""": None,
           """timesteps""": [2_2, 0],
           """class_labels""": 0,
           """generator""": generator,
           """output_type""": """np""",
       }

       if get_fixed_latents:
            __a       =   self.get_fixed_latents(seed=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__				,							shape=SCREAMING_SNAKE_CASE__						)
            __a       =   latents

       return inputs

  def __a						(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							str=0				,							SCREAMING_SNAKE_CASE__     :							List[str]="cpu"				,							SCREAMING_SNAKE_CASE__     :							List[str]=torch.floataa				,							SCREAMING_SNAKE_CASE__     :							List[str]=(1, 3, 6_4, 6_4)						):





       '''simple docstring'''

       if type(SCREAMING_SNAKE_CASE__						) == str:
            __a       =   torch.device(SCREAMING_SNAKE_CASE__						)
       __a       =   torch.Generator(device=SCREAMING_SNAKE_CASE__						).manual_seed(SCREAMING_SNAKE_CASE__						)
       __a       =   randn_tensor(SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__				,							dtype=SCREAMING_SNAKE_CASE__						)
       return latents

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs()
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

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

       __a       =   np.array([0.0_8_8_8, 0.0_8_8_1, 0.0_6_6_6, 0.0_4_7_9, 0.0_2_9_2, 0.0_1_9_5, 0.0_2_0_1, 0.0_1_6_3, 0.0_2_5_4]						)

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

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs()
       __a       =   1
       __a       =   None
       __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

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

       __a       =   np.array([0.0_3_4_0, 0.0_1_5_2, 0.0_0_6_3, 0.0_2_6_7, 0.0_2_2_1, 0.0_1_0_7, 0.0_4_1_6, 0.0_1_8_6, 0.0_2_1_7]						)

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

  @require_torch_a
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__				,							torch_dtype=torch.floataa						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__						)
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__				,							enable_math=SCREAMING_SNAKE_CASE__				,							enable_mem_efficient=SCREAMING_SNAKE_CASE__						):
            __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

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

       __a       =   np.array([0.1_8_7_5, 0.1_4_2_8, 0.1_2_8_9, 0.2_1_5_1, 0.2_0_9_2, 0.1_4_7_7, 0.1_8_7_7, 0.1_6_4_1, 0.1_3_5_3]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


  @require_torch_a
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   UNetaDModel.from_pretrained("""diffusers/consistency_models"""				,							subfolder="""diffusers_cd_imagenet64_l2"""						)
       __a       =   CMStochasticIterativeScheduler(
           num_train_timesteps=4_0				,							sigma_min=0.0_0_2				,							sigma_max=8_0.0				,							)
       __a       =   ConsistencyModelPipeline(unet=SCREAMING_SNAKE_CASE__				,							scheduler=SCREAMING_SNAKE_CASE__						)
       pipe.to(torch_device=SCREAMING_SNAKE_CASE__				,							torch_dtype=torch.floataa						)
       pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__						)

       __a       =   self.get_inputs(get_fixed_latents=SCREAMING_SNAKE_CASE__				,							device=SCREAMING_SNAKE_CASE__						)
       __a       =   1
       __a       =   None
       # Ensure usage of flash attention in torch 2.0
       with sdp_kernel(enable_flash=SCREAMING_SNAKE_CASE__				,							enable_math=SCREAMING_SNAKE_CASE__				,							enable_mem_efficient=SCREAMING_SNAKE_CASE__						):
            __a       =   pipe(**SCREAMING_SNAKE_CASE__						).images
       assert image.shape == (1, 6_4, 6_4, 3)

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

       __a       =   np.array([0.1_6_6_3, 0.1_9_4_8, 0.2_2_7_5, 0.1_6_8_0, 0.1_2_0_4, 0.1_2_4_5, 0.1_8_5_8, 0.1_3_3_8, 0.2_0_9_5]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






