






"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				str    =				(UniPCMultistepScheduler,)
	_A       :				Any    =				(("""num_inference_steps""", 25),)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		A_		:						Tuple	    =   {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """solver_order""": 2,
		    """solver_type""": """bh2""",
		}

		config.update(**lowerCAmelCase_							)
		return config





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0     ,     **lowerCAmelCase_							):
		A_		:						Dict	    =   dict(self.forward_default_kwargs							)
		A_		:						int	    =   kwargs.pop("""num_inference_steps"""     ,     lowerCAmelCase_							)
		A_		:						Tuple	    =   self.dummy_sample
		A_		:						Optional[Any]	    =   0.1 * sample
		A_		:						Optional[Any]	    =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

			A_		:						Union[str, Any]	    =   sample, sample
			for t in range(lowerCAmelCase_     ,     time_step + scheduler.config.solver_order + 1							):
				A_		:						Any	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
				A_		:						List[Any]	    =   new_scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_=0     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   dict(self.forward_default_kwargs							)
		A_		:						int	    =   kwargs.pop("""num_inference_steps"""     ,     lowerCAmelCase_							)
		A_		:						Any	    =   self.dummy_sample
		A_		:						int	    =   0.1 * sample
		A_		:						Tuple	    =   [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:						Tuple	    =   self.get_scheduler_config()
			A_		:						List[str]	    =   scheduler_class(**lowerCAmelCase_							)
			scheduler.set_timesteps(lowerCAmelCase_							)

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

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

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

			A_		:						Tuple	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						List[Any]	    =   new_scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample

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





	def    lowerCamelCase(self     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if scheduler is None:
			A_		:						Any	    =   self.scheduler_classes[0]
			A_		:						Dict	    =   self.get_scheduler_config(**lowerCAmelCase_							)
			A_		:						List[Any]	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						Any	    =   self.scheduler_classes[0]
		A_		:						Any	    =   self.get_scheduler_config(**lowerCAmelCase_							)
		A_		:						Any	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						int	    =   10
		A_		:						Tuple	    =   self.dummy_model()
		A_		:						Tuple	    =   self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_							)

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

		return sample





	def    lowerCamelCase(self							):
		A_		:						Any	    =   dict(self.forward_default_kwargs							)

		A_		:						Dict	    =   kwargs.pop("""num_inference_steps"""     ,     lowerCAmelCase_							)

		for scheduler_class in self.scheduler_classes:
			A_		:						int	    =   self.get_scheduler_config()
			A_		:						Dict	    =   scheduler_class(**lowerCAmelCase_							)

			A_		:						Dict	    =   self.dummy_sample
			A_		:						Dict	    =   0.1 * sample

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

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

			A_		:						str	    =   scheduler.timesteps[5]
			A_		:						List[Any]	    =   scheduler.timesteps[6]

			A_		:						List[Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample
			A_		:						Dict	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     **lowerCAmelCase_							).prev_sample

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





	def    lowerCamelCase(self							):
		# make sure that iterating over schedulers with same config names gives same results
		# for defaults
		A_		:						Optional[Any]	    =   UniPCMultistepScheduler(**self.get_scheduler_config()							)
		A_		:						List[str]	    =   self.full_loop(scheduler=lowerCAmelCase_							)
		A_		:						Tuple	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_mean.item() - 0.2464							) < 1e-3

		A_		:						Dict	    =   DPMSolverSinglestepScheduler.from_config(scheduler.config							)
		A_		:						Dict	    =   DEISMultistepScheduler.from_config(scheduler.config							)
		A_		:						List[str]	    =   DPMSolverMultistepScheduler.from_config(scheduler.config							)
		A_		:						Dict	    =   UniPCMultistepScheduler.from_config(scheduler.config							)

		A_		:						Union[str, Any]	    =   self.full_loop(scheduler=lowerCAmelCase_							)
		A_		:						Optional[int]	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_mean.item() - 0.2464							) < 1e-3





	def    lowerCamelCase(self							):
		for timesteps in [25, 50, 100, 999, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		self.check_over_configs(thresholding=lowerCAmelCase_							)
		for order in [1, 2, 3]:
			for solver_type in ["bh1", "bh2"]:
				for threshold in [0.5, 1.0, 2.0]:
					for prediction_type in ["epsilon", "sample"]:
						self.check_over_configs(
						    thresholding=lowerCAmelCase_     ,     prediction_type=lowerCAmelCase_     ,     sample_max_value=lowerCAmelCase_     ,     solver_order=lowerCAmelCase_     ,     solver_type=lowerCAmelCase_     ,     )





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





	def    lowerCamelCase(self							):
		for solver_type in ["bh1", "bh2"]:
			for order in [1, 2, 3]:
				for prediction_type in ["epsilon", "sample"]:
					self.check_over_configs(
					    solver_order=lowerCAmelCase_     ,     solver_type=lowerCAmelCase_     ,     prediction_type=lowerCAmelCase_     ,     )
					A_		:						Optional[Any]	    =   self.full_loop(
					    solver_order=lowerCAmelCase_     ,     solver_type=lowerCAmelCase_     ,     prediction_type=lowerCAmelCase_     ,     )
					assert not torch.isnan(lowerCAmelCase_							).any(), "Samples have nan numbers"





	def    lowerCamelCase(self							):
		self.check_over_configs(lower_order_final=lowerCAmelCase_							)
		self.check_over_configs(lower_order_final=lowerCAmelCase_							)





	def    lowerCamelCase(self							):
		for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
			self.check_over_forward(num_inference_steps=lowerCAmelCase_     ,     time_step=0							)





	def    lowerCamelCase(self							):
		A_		:						str	    =   self.full_loop()
		A_		:						Optional[Any]	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_mean.item() - 0.2464							) < 1e-3





	def    lowerCamelCase(self							):
		A_		:						Union[str, Any]	    =   self.full_loop(prediction_type="""v_prediction"""							)
		A_		:						Tuple	    =   torch.mean(torch.abs(lowerCAmelCase_							)							)

		assert abs(result_mean.item() - 0.1014							) < 1e-3





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   self.scheduler_classes[0]
		A_		:						Tuple	    =   self.get_scheduler_config(thresholding=lowerCAmelCase_     ,     dynamic_thresholding_ratio=0							)
		A_		:						List[str]	    =   scheduler_class(**lowerCAmelCase_							)

		A_		:						Any	    =   10
		A_		:						str	    =   self.dummy_model()
		A_		:						Any	    =   self.dummy_sample_deter.half()
		scheduler.set_timesteps(lowerCAmelCase_							)

		for i, t in enumerate(scheduler.timesteps							):
			A_		:						Any	    =   model(lowerCAmelCase_     ,     lowerCAmelCase_							)
			A_		:						Union[str, Any]	    =   scheduler.step(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							).prev_sample

		assert sample.dtype == torch.floataa





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		for scheduler_class in self.scheduler_classes:
			A_		:						List[Any]	    =   self.get_scheduler_config(**lowerCAmelCase_							)
			A_		:						Union[str, Any]	    =   scheduler_class(**lowerCAmelCase_							)

			scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
			assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps









"""simple docstring"""
import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
							from PIL import Image
else:



							class      SCREAMING_SNAKE_CASE  :





								"""simple docstring"""




								@staticmethod
								def    lowerCamelCase(*lowerCAmelCase_     ,     **lowerCAmelCase_							):
									pass



@is_pipeline_test
@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	_A       :				List[Any]    =				MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Union[str, Any]	    =   [
		    {
		        """image""": Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							),
		        """question""": """How many cats are there?""",
		    },
		    {
		        """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
		        """question""": """How many cats are there?""",
		    },
		]
		return vqa_pipeline, examples





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						str	    =   vqa_pipeline(lowerCAmelCase_     ,     top_k=1							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		        [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}],
		    ]     ,     )





	@require_torch
	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   pipeline("""visual-question-answering"""     ,     model="""hf-internal-testing/tiny-vilt-random-vqa"""							)
		A_		:						Any	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						Any	    =   """How many cats are there?"""

		A_		:						Optional[int]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question="""How many cats are there?"""     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    lowerCAmelCase_     ,     [{"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}, {"""score""": ANY(lowerCAmelCase_							), """answer""": ANY(lowerCAmelCase_							)}]							)





	@slow
	@require_torch
	def    lowerCamelCase(self							):
		A_		:						int	    =   pipeline("""visual-question-answering"""     ,     model="""dandelin/vilt-b32-finetuned-vqa"""							)
		A_		:						Tuple	    =   """./tests/fixtures/tests_samples/COCO/000000039769.png"""
		A_		:						int	    =   """How many cats are there?"""

		A_		:						List[Any]	    =   vqa_pipeline(image=lowerCAmelCase_     ,     question=lowerCAmelCase_     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Any	    =   vqa_pipeline({"""image""": image, """question""": question}     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]							)

		A_		:						Tuple	    =   vqa_pipeline(
		    [{"""image""": image, """question""": question}, {"""image""": image, """question""": question}]     ,     top_k=2							)
		self.assertEqual(
		    nested_simplify(lowerCAmelCase_     ,     decimals=4							)     ,     [[{"""score""": 0.8799, """answer""": """2"""}, {"""score""": 0.296, """answer""": """1"""}]] * 2     ,     )





	@require_tf
	@unittest.skip("""Visual question answering not implemented in TF"""							)
	def    lowerCamelCase(self							):
		pass

