

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  (IPNDMScheduler,)
		a__					       =  (("num_inference_steps", 5_0),)


		def 		A__					(				self	,       **__snake_case):
			_UpperCamelCase   :		Optional[int]			=						{'num_train_timesteps': 10_00}
			config.update(**__snake_case)
			return config


		def 		A__					(				self	,       __snake_case=0	,       **__snake_case):
			_UpperCamelCase   :		Optional[int]			=						dict(self.forward_default_kwargs)
			_UpperCamelCase   :		Any			=						kwargs.pop('num_inference_steps'	,       __snake_case)
			_UpperCamelCase   :		Union[str, Any]			=						self.dummy_sample
			_UpperCamelCase   :		List[Any]			=						0.1 * sample
			_UpperCamelCase   :		Union[str, Any]			=						[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase   :		Any			=						self.get_scheduler_config(**__snake_case)
				_UpperCamelCase   :		Optional[int]			=						scheduler_class(**__snake_case)
				scheduler.set_timesteps(__snake_case)
				# copy over dummy past residuals
				_UpperCamelCase   :		List[Any]			=						dummy_past_residuals[:]

				if time_step is None:
					_UpperCamelCase   :		Tuple			=						scheduler.timesteps[len(scheduler.timesteps) // 2]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(__snake_case)
					_UpperCamelCase   :		List[Any]			=						scheduler_class.from_pretrained(__snake_case)
					new_scheduler.set_timesteps(__snake_case)
					# copy over dummy past residuals
					_UpperCamelCase   :		Any			=						dummy_past_residuals[:]

				_UpperCamelCase   :		Any			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       **__snake_case).prev_sample
				_UpperCamelCase   :		Any			=						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"

				_UpperCamelCase   :		List[Any]			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       **__snake_case).prev_sample
				_UpperCamelCase   :		Optional[int]			=						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 		A__					(				self):
			pass


		def 		A__					(				self	,       __snake_case=0	,       **__snake_case):
			_UpperCamelCase   :		Optional[int]			=						dict(self.forward_default_kwargs)
			_UpperCamelCase   :		int			=						kwargs.pop('num_inference_steps'	,       __snake_case)
			_UpperCamelCase   :		List[Any]			=						self.dummy_sample
			_UpperCamelCase   :		Union[str, Any]			=						0.1 * sample
			_UpperCamelCase   :		Any			=						[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase   :		Any			=						self.get_scheduler_config()
				_UpperCamelCase   :		str			=						scheduler_class(**__snake_case)
				scheduler.set_timesteps(__snake_case)

				# copy over dummy past residuals (must be after setting timesteps)
				_UpperCamelCase   :		int			=						dummy_past_residuals[:]

				if time_step is None:
					_UpperCamelCase   :		str			=						scheduler.timesteps[len(scheduler.timesteps) // 2]

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(__snake_case)
					_UpperCamelCase   :		Any			=						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   :		Tuple			=						dummy_past_residuals[:]

				_UpperCamelCase   :		Optional[Any]			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       **__snake_case).prev_sample
				_UpperCamelCase   :		Dict			=						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"

				_UpperCamelCase   :		Dict			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       **__snake_case).prev_sample
				_UpperCamelCase   :		Any			=						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 		A__					(				self	,       **__snake_case):
			_UpperCamelCase   :		List[str]			=						self.scheduler_classes[0]
			_UpperCamelCase   :		Dict			=						self.get_scheduler_config(**__snake_case)
			_UpperCamelCase   :		str			=						scheduler_class(**__snake_case)

			_UpperCamelCase   :		Optional[int]			=						10
			_UpperCamelCase   :		List[str]			=						self.dummy_model()
			_UpperCamelCase   :		Tuple			=						self.dummy_sample_deter
			scheduler.set_timesteps(__snake_case)

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

			for i, t in enumerate(scheduler.timesteps):
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       __snake_case)
				_UpperCamelCase   :		List[Any]			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case).prev_sample

			return sample


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						dict(self.forward_default_kwargs)

			_UpperCamelCase   :		Tuple			=						kwargs.pop('num_inference_steps'	,       __snake_case)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase   :		List[Any]			=						self.get_scheduler_config()
				_UpperCamelCase   :		List[Any]			=						scheduler_class(**__snake_case)

				_UpperCamelCase   :		Any			=						self.dummy_sample
				_UpperCamelCase   :		Dict			=						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   :		Optional[Any]			=						num_inference_steps

				# copy over dummy past residuals (must be done after set_timesteps)
				_UpperCamelCase   :		List[str]			=						[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
				_UpperCamelCase   :		List[Any]			=						dummy_past_residuals[:]

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

				_UpperCamelCase   :		int			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       **__snake_case).prev_sample
				_UpperCamelCase   :		List[Any]			=						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)

				_UpperCamelCase   :		Optional[Any]			=						scheduler.step(__snake_case	,       __snake_case	,       __snake_case	,       **__snake_case).prev_sample
				_UpperCamelCase   :		List[Any]			=						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 		A__					(				self):
			for timesteps in [1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=__snake_case	,       time_step=__snake_case)


		def 		A__					(				self):
			for t, num_inference_steps in zip([1, 5, 10]	,       [10, 50, 1_00]):
				self.check_over_forward(num_inference_steps=__snake_case	,       time_step=__snake_case)






		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						self.full_loop()
			_UpperCamelCase   :		List[Any]			=						torch.mean(torch.abs(__snake_case))

			assert abs(result_mean.item() - 2_54_05_29) < 10







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__								=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__								=		[
							    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """CanineForMultipleChoice""",
							    """CanineForQuestionAnswering""",
							    """CanineForSequenceClassification""",
							    """CanineForTokenClassification""",
							    """CanineLayer""",
							    """CanineModel""",
							    """CaninePreTrainedModel""",
							    """load_tf_weights_in_canine""",
							]


if TYPE_CHECKING:
							from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
							from .tokenization_canine import CanineTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_canine import (
														    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CanineForMultipleChoice,
														    CanineForQuestionAnswering,
														    CanineForSequenceClassification,
														    CanineForTokenClassification,
														    CanineLayer,
														    CanineModel,
														    CaninePreTrainedModel,
														    load_tf_weights_in_canine,
														)


else:
							import sys

							lowerCAmelCase__								=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




