
"""simple docstring"""


import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest







class 				lowerCAmelCase_				(      UpperCamelCase_    ):



						'''simple docstring'''


						_lowerCamelCase:    str      =    (DEISMultistepScheduler,)
						_lowerCamelCase:    Tuple      =    (('''num_inference_steps''', 25),)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,**A_     :      Optional[Any]      )     ->   List[str]:
							A						   =   {
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.00_01,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''solver_order''': 2,
							}

							config.update(**A_      )
							return config


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      str=0  ,**A_     :      int      )     ->   Optional[Any]:
							A						   =   dict(self.forward_default_kwargs      )
							A						   =   kwargs.pop('num_inference_steps'  ,A_      )
							A						   =   self.dummy_sample
							A						   =   0.1 * sample
							A						   =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								A						   =   self.get_scheduler_config(**A_      )
								A						   =   scheduler_class(**A_      )
								scheduler.set_timesteps(A_      )
								# copy over dummy past residuals
								A						   =   dummy_past_residuals[: scheduler.config.solver_order]

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

								A						   =   sample, sample
								for t in range(A_  ,time_step + scheduler.config.solver_order + 1      ):
									A						   =   scheduler.step(A_  ,A_  ,A_  ,**A_      ).prev_sample
									A						   =   new_scheduler.step(A_  ,A_  ,A_  ,**A_      ).prev_sample

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							pass


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      str=0  ,**A_     :      List[str]      )     ->   List[str]:
							A						   =   dict(self.forward_default_kwargs      )
							A						   =   kwargs.pop('num_inference_steps'  ,A_      )
							A						   =   self.dummy_sample
							A						   =   0.1 * sample
							A						   =   [residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
								A						   =   self.get_scheduler_config()
								A						   =   scheduler_class(**A_      )
								scheduler.set_timesteps(A_      )

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

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

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

								A						   =   scheduler.step(A_  ,A_  ,A_  ,**A_      ).prev_sample
								A						   =   new_scheduler.step(A_  ,A_  ,A_  ,**A_      ).prev_sample

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      List[str]=None  ,**A_     :      List[str]      )     ->   Any:
							if scheduler is None:
								A						   =   self.scheduler_classes[0]
								A						   =   self.get_scheduler_config(**A_      )
								A						   =   scheduler_class(**A_      )

							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config(**A_      )
							A						   =   scheduler_class(**A_      )

							A						   =   10
							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter
							scheduler.set_timesteps(A_      )

							for i, t in enumerate(scheduler.timesteps      ):
								A						   =   model(A_  ,A_      )
								A						   =   scheduler.step(A_  ,A_  ,A_      ).prev_sample

							return sample


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[str]:
							A						   =   dict(self.forward_default_kwargs      )

							A						   =   kwargs.pop('num_inference_steps'  ,A_      )

							for scheduler_class in self.scheduler_classes:
								A						   =   self.get_scheduler_config()
								A						   =   scheduler_class(**A_      )

								A						   =   self.dummy_sample
								A						   =   0.1 * sample

								if num_inference_steps is not None and hasattr(A_  ,'set_timesteps'      ):
									scheduler.set_timesteps(A_      )
								elif num_inference_steps is not None and not hasattr(A_  ,'set_timesteps'      ):
									A						   =   num_inference_steps

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

								A						   =   scheduler.timesteps[5]
								A						   =   scheduler.timesteps[6]

								A						   =   scheduler.step(A_  ,A_  ,A_  ,**A_      ).prev_sample
								A						   =   scheduler.step(A_  ,A_  ,A_  ,**A_      ).prev_sample

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[Any]:
							# make sure that iterating over schedulers with same config names gives same results
							# for defaults
							A						   =   DEISMultistepScheduler(**self.get_scheduler_config()      )
							A						   =   self.full_loop(scheduler=A_      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_mean.item() - 0.2_39_16      ) < 1e-3

							A						   =   DPMSolverSinglestepScheduler.from_config(scheduler.config      )
							A						   =   DPMSolverMultistepScheduler.from_config(scheduler.config      )
							A						   =   UniPCMultistepScheduler.from_config(scheduler.config      )
							A						   =   DEISMultistepScheduler.from_config(scheduler.config      )

							A						   =   self.full_loop(scheduler=A_      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_mean.item() - 0.2_39_16      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Union[str, Any]:
							for timesteps in [25, 50, 100, 999, 1000]:
								self.check_over_configs(num_train_timesteps=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[str]:
							self.check_over_configs(thresholding=A_      )
							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=A_  ,prediction_type=A_  ,sample_max_value=A_  ,algorithm_type='deis'  ,solver_order=A_  ,solver_type=A_  ,)


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[int]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(prediction_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[Any]:
							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=A_  ,solver_type=A_  ,prediction_type=A_  ,algorithm_type=A_  ,)
											A						   =   self.full_loop(
											    solver_order=A_  ,solver_type=A_  ,prediction_type=A_  ,algorithm_type=A_  ,)
											assert not torch.isnan(A_      ).any(), "Samples have nan numbers"


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Dict:
							self.check_over_configs(lower_order_final=A_      )
							self.check_over_configs(lower_order_final=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
								self.check_over_forward(num_inference_steps=A_  ,time_step=0      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Tuple:
							A						   =   self.full_loop()
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_mean.item() - 0.2_39_16      ) < 1e-3


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.full_loop(prediction_type='v_prediction'      )
							A						   =   torch.mean(torch.abs(A_      )      )

							assert abs(result_mean.item() - 0.0_91      ) < 1e-3




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A						   =   self.scheduler_classes[0]
							A						   =   self.get_scheduler_config(thresholding=A_  ,dynamic_thresholding_ratio=0      )
							A						   =   scheduler_class(**A_      )

							A						   =   10
							A						   =   self.dummy_model()
							A						   =   self.dummy_sample_deter.half()
							scheduler.set_timesteps(A_      )

							for i, t in enumerate(scheduler.timesteps      ):
								A						   =   model(A_  ,A_      )
								A						   =   scheduler.step(A_  ,A_  ,A_      ).prev_sample

							assert sample.dtype == torch.floataa




"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







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



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







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



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )