

'''simple docstring'''
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	int        =(DEISMultistepScheduler,)
			__A  :	List[str]        =(("num_inference_steps", 25),)






			def 		UpperCamelCase__							(  self			,**_snake_case     ):
				UpperCAmelCase_     :      Optional[Any]						=  {
				    "num_train_timesteps": 10_00,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "solver_order": 2,
				}

				config.update(**_snake_case     )
				return config






			def 		UpperCamelCase__							(  self			,_snake_case=0			,**_snake_case     ):
				UpperCAmelCase_     :      int						=  dict(self.forward_default_kwargs     )
				UpperCAmelCase_     :      Tuple						=  kwargs.pop("num_inference_steps"			,_snake_case     )
				UpperCAmelCase_     :      List[str]						=  self.dummy_sample
				UpperCAmelCase_     :      Optional[int]						=  0.1 * sample
				UpperCAmelCase_     :      Optional[Any]						=  [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Dict						=  self.get_scheduler_config(**_snake_case     )
					UpperCAmelCase_     :      List[str]						=  scheduler_class(**_snake_case     )
					scheduler.set_timesteps(_snake_case     )
					# copy over dummy past residuals
					UpperCAmelCase_     :      int						=  dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(_snake_case     )
						UpperCAmelCase_     :      Any						=  scheduler_class.from_pretrained(_snake_case     )
						new_scheduler.set_timesteps(_snake_case     )
						# copy over dummy past residuals
						UpperCAmelCase_     :      Any						=  dummy_past_residuals[: new_scheduler.config.solver_order]

					UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  sample, sample
					for t in range(_snake_case			,time_step + scheduler.config.solver_order + 1     ):
						UpperCAmelCase_     :      Tuple						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample
						UpperCAmelCase_     :      Tuple						=  new_scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample

						assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"






			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self			,_snake_case=0			,**_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  dict(self.forward_default_kwargs     )
				UpperCAmelCase_     :      List[Any]						=  kwargs.pop("num_inference_steps"			,_snake_case     )
				UpperCAmelCase_     :      Tuple						=  self.dummy_sample
				UpperCAmelCase_     :      Optional[Any]						=  0.1 * sample
				UpperCAmelCase_     :      List[Any]						=  [residual + 0.2, residual + 0.15, residual + 0.10]

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Optional[Any]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      Dict						=  scheduler_class(**_snake_case     )
					scheduler.set_timesteps(_snake_case     )

					# copy over dummy past residuals (must be after setting timesteps)
					UpperCAmelCase_     :      List[str]						=  dummy_past_residuals[: scheduler.config.solver_order]

					with tempfile.TemporaryDirectory() as tmpdirname:
						scheduler.save_config(_snake_case     )
						UpperCAmelCase_     :      int						=  scheduler_class.from_pretrained(_snake_case     )
						# copy over dummy past residuals
						new_scheduler.set_timesteps(_snake_case     )

						# copy over dummy past residual (must be after setting timesteps)
						UpperCAmelCase_     :      int						=  dummy_past_residuals[: new_scheduler.config.solver_order]

					UpperCAmelCase_     :      Any						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample
					UpperCAmelCase_     :      List[str]						=  new_scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample

					assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"






			def 		UpperCamelCase__							(  self			,_snake_case=None			,**_snake_case     ):
				if scheduler is None:
					UpperCAmelCase_     :      int						=  self.scheduler_classes[0]
					UpperCAmelCase_     :      str						=  self.get_scheduler_config(**_snake_case     )
					UpperCAmelCase_     :      Union[str, Any]						=  scheduler_class(**_snake_case     )

				UpperCAmelCase_     :      Tuple						=  self.scheduler_classes[0]
				UpperCAmelCase_     :      Union[str, Any]						=  self.get_scheduler_config(**_snake_case     )
				UpperCAmelCase_     :      Any						=  scheduler_class(**_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  10
				UpperCAmelCase_     :      List[Any]						=  self.dummy_model()
				UpperCAmelCase_     :      Optional[int]						=  self.dummy_sample_deter
				scheduler.set_timesteps(_snake_case     )

				for i, t in enumerate(scheduler.timesteps     ):
					UpperCAmelCase_     :      Tuple						=  model(_snake_case			,_snake_case     )
					UpperCAmelCase_     :      Optional[Any]						=  scheduler.step(_snake_case			,_snake_case			,_snake_case     ).prev_sample

				return sample






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  dict(self.forward_default_kwargs     )

				UpperCAmelCase_     :      List[Any]						=  kwargs.pop("num_inference_steps"			,_snake_case     )

				for scheduler_class in self.scheduler_classes:
					UpperCAmelCase_     :      Optional[Any]						=  self.get_scheduler_config()
					UpperCAmelCase_     :      Any						=  scheduler_class(**_snake_case     )

					UpperCAmelCase_     :      Optional[Any]						=  self.dummy_sample
					UpperCAmelCase_     :      Union[str, Any]						=  0.1 * sample

					if num_inference_steps is not None and hasattr(_snake_case			,"set_timesteps"     ):
						scheduler.set_timesteps(_snake_case     )
					elif num_inference_steps is not None and not hasattr(_snake_case			,"set_timesteps"     ):
						UpperCAmelCase_     :      Tuple						=  num_inference_steps

					# copy over dummy past residuals (must be done after set_timesteps)
					UpperCAmelCase_     :      Optional[int]						=  [residual + 0.2, residual + 0.15, residual + 0.10]
					UpperCAmelCase_     :      Optional[Any]						=  dummy_past_residuals[: scheduler.config.solver_order]

					UpperCAmelCase_     :      Optional[int]						=  scheduler.timesteps[5]
					UpperCAmelCase_     :      Optional[int]						=  scheduler.timesteps[6]

					UpperCAmelCase_     :      Tuple						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample
					UpperCAmelCase_     :      int						=  scheduler.step(_snake_case			,_snake_case			,_snake_case			,**_snake_case     ).prev_sample

					self.assertEqual(output_a.shape			,sample.shape     )
					self.assertEqual(output_a.shape			,output_a.shape     )






			def 		UpperCamelCase__							(  self     ):
				# make sure that iterating over schedulers with same config names gives same results
				# for defaults
				UpperCAmelCase_     :      Tuple						=  DEISMultistepScheduler(**self.get_scheduler_config()     )
				UpperCAmelCase_     :      Union[str, Any]						=  self.full_loop(scheduler=_snake_case     )
				UpperCAmelCase_     :      List[str]						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.23916     ) < 1E-3

				UpperCAmelCase_     :      int						=  DPMSolverSinglestepScheduler.from_config(scheduler.config     )
				UpperCAmelCase_     :      int						=  DPMSolverMultistepScheduler.from_config(scheduler.config     )
				UpperCAmelCase_     :      str						=  UniPCMultistepScheduler.from_config(scheduler.config     )
				UpperCAmelCase_     :      int						=  DEISMultistepScheduler.from_config(scheduler.config     )

				UpperCAmelCase_     :      Union[str, Any]						=  self.full_loop(scheduler=_snake_case     )
				UpperCAmelCase_     :      int						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.23916     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				for timesteps in [25, 50, 1_00, 9_99, 10_00]:
					self.check_over_configs(num_train_timesteps=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				self.check_over_configs(thresholding=_snake_case     )
				for order in [1, 2, 3]:
					for solver_type in ["logrho"]:
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    thresholding=_snake_case			,prediction_type=_snake_case			,sample_max_value=_snake_case			,algorithm_type="deis"			,solver_order=_snake_case			,solver_type=_snake_case			,)






			def 		UpperCamelCase__							(  self     ):
				for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				for algorithm_type in ["deis"]:
					for solver_type in ["logrho"]:
						for order in [1, 2, 3]:
							for prediction_type in ["epsilon", "sample"]:
								self.check_over_configs(
								    solver_order=_snake_case			,solver_type=_snake_case			,prediction_type=_snake_case			,algorithm_type=_snake_case			,)
								UpperCAmelCase_     :      Tuple						=  self.full_loop(
								    solver_order=_snake_case			,solver_type=_snake_case			,prediction_type=_snake_case			,algorithm_type=_snake_case			,)
								assert not torch.isnan(_snake_case     ).any(), "Samples have nan numbers"






			def 		UpperCamelCase__							(  self     ):
				self.check_over_configs(lower_order_final=_snake_case     )
				self.check_over_configs(lower_order_final=_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
					self.check_over_forward(num_inference_steps=_snake_case			,time_step=0     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.full_loop()
				UpperCAmelCase_     :      Union[str, Any]						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.23916     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.full_loop(prediction_type="v_prediction"     )
				UpperCAmelCase_     :      Tuple						=  torch.mean(torch.abs(_snake_case     )     )

				assert abs(result_mean.item() - 0.091     ) < 1E-3






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[Any]						=  self.scheduler_classes[0]
				UpperCAmelCase_     :      Union[str, Any]						=  self.get_scheduler_config(thresholding=_snake_case			,dynamic_thresholding_ratio=0     )
				UpperCAmelCase_     :      Union[str, Any]						=  scheduler_class(**_snake_case     )

				UpperCAmelCase_     :      Optional[Any]						=  10
				UpperCAmelCase_     :      Dict						=  self.dummy_model()
				UpperCAmelCase_     :      str						=  self.dummy_sample_deter.half()
				scheduler.set_timesteps(_snake_case     )

				for i, t in enumerate(scheduler.timesteps     ):
					UpperCAmelCase_     :      Optional[int]						=  model(_snake_case			,_snake_case     )
					UpperCAmelCase_     :      List[str]						=  scheduler.step(_snake_case			,_snake_case			,_snake_case     ).prev_sample

				assert sample.dtype == torch.floataa








"""simple docstring"""


import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


__snake_case    					=					get_tests_dir('fixtures')

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							str:
    # A mock response for an HTTP head request to emulate server down
    lowercase__       :       List[str]				=						mock.Mock()
    lowercase__       :       Dict				=						500
    lowercase__       :       Any				=						{}
    lowercase__       :       int				=						HTTPError
    lowercase__       :       Optional[int]				=						{}

    # Download this model to make sure it's in the cache.
    lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""						)
    # Under the mock environment we get a 500 error when trying to reach the model.
    with mock.patch("""requests.Session.request""" ,     return_value=lowerCamelCase__						) as mock_head:
     lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit"""						)
     # This check we did call the fake head request
     mock_head.assert_called()


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    # This test is for deprecated behavior and can be removed in v5
    lowercase__       :       Dict				=						ViTImageProcessor.from_pretrained(
        """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json"""						)







   def 					UpperCAmelCase__(						self						)      ->							Tuple:
    with self.assertRaises(lowerCamelCase__						):
     # config is in subfolder, the following should not work without specifying the subfolder
     lowercase__       :       List[str]				=						AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants"""						)

    lowercase__       :       Any				=						AutoImageProcessor.from_pretrained(
        """hf-internal-testing/stable-diffusion-all-variants""" ,     subfolder="""feature_extractor"""						)

    self.assertIsNotNone(lowerCamelCase__						)

@is_staging_test
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							int:
    lowercase__       :       str				=						TOKEN
    HfFolder.save_token(lowerCamelCase__						)


   @classmethod
   def 					UpperCAmelCase__(						cls						)      ->							List[str]:
    try:
     delete_repo(token=cls._token ,     repo_id="""test-image-processor"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""valid_org/test-image-processor-org"""						)
    except HTTPError:
     pass

    try:
     delete_repo(token=cls._token ,     repo_id="""test-dynamic-image-processor"""						)
    except HTTPError:
     pass


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[str]				=						ViTImageProcessor.from_pretrained(lowerCamelCase__						)
    image_processor.push_to_hub("""test-image-processor""" ,     use_auth_token=self._token						)

    lowercase__       :       str				=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

    # Reset repo
    delete_repo(token=self._token ,     repo_id="""test-image-processor"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     image_processor.save_pretrained(
         lowerCamelCase__ ,     repo_id="""test-image-processor""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       Union[str, Any]				=						ViTImageProcessor.from_pretrained(F'''{USER}/test-image-processor'''						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       Tuple				=						ViTImageProcessor.from_pretrained(lowerCamelCase__						)
    image_processor.push_to_hub("""valid_org/test-image-processor""" ,     use_auth_token=self._token						)

    lowercase__       :       Any				=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor"""						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)

    # Reset repo
    delete_repo(token=self._token ,     repo_id="""valid_org/test-image-processor"""						)

    # Push to hub via save_pretrained
    with tempfile.TemporaryDirectory() as tmp_dir:
     image_processor.save_pretrained(
         lowerCamelCase__ ,     repo_id="""valid_org/test-image-processor-org""" ,     push_to_hub=lowerCamelCase__ ,     use_auth_token=self._token						)

    lowercase__       :       List[Any]				=						ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org"""						)
    for k, v in image_processor.__dict__.items():
     self.assertEqual(lowerCamelCase__ ,     getattr(lowerCamelCase__ ,     lowerCamelCase__						)						)







   def 					UpperCAmelCase__(						self						)      ->							Dict:
    CustomImageProcessor.register_for_auto_class()
    lowercase__       :       List[str]				=						CustomImageProcessor.from_pretrained(lowerCamelCase__						)

    image_processor.push_to_hub("""test-dynamic-image-processor""" ,     use_auth_token=self._token						)

    # This has added the proper auto_map field to the config
    self.assertDictEqual(
        image_processor.auto_map ,     {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""} ,     )

    lowercase__       :       Dict				=						AutoImageProcessor.from_pretrained(
        F'''{USER}/test-dynamic-image-processor''' ,     trust_remote_code=lowerCamelCase__						)
    # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
    self.assertEqual(new_image_processor.__class__.__name__ ,     """CustomImageProcessor"""						)