


'''simple docstring'''







import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[int]												=inspect.getfile(accelerate.test_utils      )
  a_       :int												=os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["""scripts""", """test_cli.py"""]      )

  a_       :str												=["""accelerate""", """launch"""]
  a_       :Tuple												=Path.home() / """.cache/huggingface/accelerate"""
  a_       :List[str]												="""default_config.yaml"""
  a_       :Union[str, Any]												=config_folder / config_file
  a_       :Dict												=config_folder / """_default_config.yaml"""

  a_       :Any												=Path("""tests/test_configs"""      )

  @classmethod
  def __a						(					cls     :							List[Any]						):





       '''simple docstring'''

       if cls.config_path.is_file():
            cls.config_path.rename(cls.changed_path						)

  @classmethod
  def __a						(					cls     :							Dict						):





       '''simple docstring'''

       if cls.changed_path.is_file():
            cls.changed_path.rename(cls.config_path						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   self.base_cmd
       if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
            cmd += ["--multi_gpu"]
       execute_subprocess_async(cmd + [self.test_file_path]				,							env=os.environ.copy()						)

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       for config in sorted(self.test_config_path.glob("""**/*.yaml"""						)						):
            with self.subTest(config_file=SCREAMING_SNAKE_CASE__						):
                 execute_subprocess_async(
                     self.base_cmd + ["""--config_file""", str(SCREAMING_SNAKE_CASE__						), self.test_file_path]				,							env=os.environ.copy()						)


  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       execute_subprocess_async(["""accelerate""", """test"""]				,							env=os.environ.copy()						)


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  a_       :Any												="""test-tpu"""
  a_       :str												="""us-central1-a"""
  a_       :str												="""ls"""
  a_       :Any												=["""accelerate""", """tpu-config"""]
  a_       :str												="""cd /usr/share"""
  a_       :str												="""tests/test_samples/test_command_file.sh"""
  a_       :Optional[Any]												="""Running gcloud compute tpus tpu-vm ssh"""

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd
           + ["""--command""", self.command, """--tpu_zone""", self.tpu_zone, """--tpu_name""", self.tpu_name, """--debug"""]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/0_12_0.yaml""",
               """--command""",
               self.command,
               """--tpu_zone""",
               self.tpu_zone,
               """--tpu_name""",
               self.tpu_name,
               """--debug""",
           ]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--debug"""]				,							return_stdout=SCREAMING_SNAKE_CASE__						)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Union[str, Any]						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command""", self.command, """--debug"""]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/latest.yaml""",
               """--command""",
               self.command,
               """--command""",
               """echo \"Hello World\"""",
               """--debug""",
           ]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Dict						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd
           + ["""--config_file""", """tests/test_configs/latest.yaml""", """--command_file""", self.command_file, """--debug"""]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/0_12_0.yaml""",
               """--command_file""",
               self.command_file,
               """--tpu_zone""",
               self.tpu_zone,
               """--tpu_name""",
               self.tpu_name,
               """--debug""",
           ]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)

  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd + ["""--config_file""", """tests/test_configs/latest.yaml""", """--install_accelerate""", """--debug"""]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)


  def __a						(					self     :							Optional[int]						):





       '''simple docstring'''

       __a       =   run_command(
           self.cmd
           + [
               """--config_file""",
               """tests/test_configs/latest.yaml""",
               """--install_accelerate""",
               """--accelerate_version""",
               """12.0.0""",
               """--debug""",
           ]				,							return_stdout=SCREAMING_SNAKE_CASE__				,							)
       self.assertIn(
           f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all'''				,							SCREAMING_SNAKE_CASE__				,							)









'''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






