





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Dict       =       (DDPMScheduler,)






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   **_A      :      Optional[Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			List[str]											=	{
							    'num_train_timesteps': 1000,
							    'beta_start': 0.0_0_0_1,
							    'beta_end': 0.0_2,
							    'beta_schedule': 'linear',
							    'variance_type': 'fixed_small',
							    'clip_sample': True,
							}

							config.update(**_A	)
							return config






						def UpperCAmelCase_				(      self      :      Dict	)							->    Any:







							"""simple docstring"""


							for timesteps in [1, 5, 100, 1000]:
								self.check_over_configs(num_train_timesteps=_A	)






						def UpperCAmelCase_				(      self      :      int	)							->    Union[str, Any]:







							"""simple docstring"""


							for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]		,   [0.0_0_2, 0.0_2, 0.2, 2]	):
								self.check_over_configs(beta_start=_A		,   beta_end=_A	)






						def UpperCAmelCase_				(      self      :      str	)							->    List[Any]:







							"""simple docstring"""


							for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=_A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Union[str, Any]:







							"""simple docstring"""


							for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=_A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    List[Any]:







							"""simple docstring"""


							for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=_A	)






						def UpperCAmelCase_				(      self      :      Any	)							->    List[Any]:







							"""simple docstring"""


							self.check_over_configs(thresholding=_A	)
							for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(
									    thresholding=_A		,   prediction_type=_A		,   sample_max_value=_A		,   )






						def UpperCAmelCase_				(      self      :      List[str]	)							->    List[str]:







							"""simple docstring"""


							for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(prediction_type=_A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    Any:







							"""simple docstring"""


							for t in [0, 500, 999]:
								self.check_over_forward(time_step=_A	)






						def UpperCAmelCase_				(      self      :      str	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Optional[int]											=	self.scheduler_classes[0]
							snake_case_		:			Tuple											=	self.get_scheduler_config()
							snake_case_		:			Dict											=	scheduler_class(**_A	)

							assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.0_0_9_7_9	)	) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.0_2	)	) < 1E-5






						def UpperCAmelCase_				(      self      :      Any	)							->    str:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.scheduler_classes[0]
							snake_case_		:			List[Any]											=	self.get_scheduler_config()
							snake_case_		:			Any											=	scheduler_class(**_A	)

							snake_case_		:			int											=	len(_A	)

							snake_case_		:			Dict											=	self.dummy_model()
							snake_case_		:			Optional[Any]											=	self.dummy_sample_deter
							snake_case_		:			Union[str, Any]											=	torch.manual_seed(0	)

							for t in reversed(range(_A	)	):
								# 1. predict noise residual
								snake_case_		:			Optional[int]											=	model(_A		,   _A	)

								# 2. predict previous mean of sample x_t-1
								snake_case_		:			Tuple											=	scheduler.step(_A		,   _A		,   _A		,   generator=_A	).prev_sample

								# if t > 0:
								#     noise = self.dummy_sample_deter
								#     variance = scheduler.get_variance(t) ** (0.5) * noise
								#
								# sample = pred_prev_sample + variance
								snake_case_		:			List[str]											=	pred_prev_sample

							snake_case_		:			int											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			Tuple											=	torch.mean(torch.abs(_A	)	)

							assert abs(result_sum.item() - 258.9606	) < 1E-2
							assert abs(result_mean.item() - 0.3_3_7_2	) < 1E-3






						def UpperCAmelCase_				(      self      :      int	)							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			str											=	self.scheduler_classes[0]
							snake_case_		:			int											=	self.get_scheduler_config(prediction_type='v_prediction'	)
							snake_case_		:			Tuple											=	scheduler_class(**_A	)

							snake_case_		:			Dict											=	len(_A	)

							snake_case_		:			Dict											=	self.dummy_model()
							snake_case_		:			Dict											=	self.dummy_sample_deter
							snake_case_		:			List[str]											=	torch.manual_seed(0	)

							for t in reversed(range(_A	)	):
								# 1. predict noise residual
								snake_case_		:			List[str]											=	model(_A		,   _A	)

								# 2. predict previous mean of sample x_t-1
								snake_case_		:			List[Any]											=	scheduler.step(_A		,   _A		,   _A		,   generator=_A	).prev_sample

								# if t > 0:
								#     noise = self.dummy_sample_deter
								#     variance = scheduler.get_variance(t) ** (0.5) * noise
								#
								# sample = pred_prev_sample + variance
								snake_case_		:			Optional[Any]											=	pred_prev_sample

							snake_case_		:			Any											=	torch.sum(torch.abs(_A	)	)
							snake_case_		:			List[Any]											=	torch.mean(torch.abs(_A	)	)

							assert abs(result_sum.item() - 202.0296	) < 1E-2
							assert abs(result_mean.item() - 0.2_6_3_1	) < 1E-3






						def UpperCAmelCase_				(      self      :      List[str]	)							->    int:







							"""simple docstring"""


							snake_case_		:			str											=	self.scheduler_classes[0]
							snake_case_		:			Union[str, Any]											=	self.get_scheduler_config()
							snake_case_		:			Any											=	scheduler_class(**_A	)

							snake_case_		:			str											=	[100, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=_A	)

							snake_case_		:			Tuple											=	scheduler.timesteps

							for i, timestep in enumerate(_A	):
								if i == len(_A	) - 1:
									snake_case_		:			Tuple											=	-1
								else:
									snake_case_		:			Union[str, Any]											=	timesteps[i + 1]

								snake_case_		:			str											=	scheduler.previous_timestep(_A	)
								snake_case_		:			Optional[int]											=	prev_t.item()

								self.assertEqual(_A		,   _A	)






						def UpperCAmelCase_				(      self      :      Tuple	)							->    Tuple:







							"""simple docstring"""


							snake_case_		:			List[Any]											=	self.scheduler_classes[0]
							snake_case_		:			Union[str, Any]											=	self.get_scheduler_config()
							snake_case_		:			List[str]											=	scheduler_class(**_A	)

							snake_case_		:			Tuple											=	[100, 87, 50, 51, 0]

							with self.assertRaises(_A		,   msg='`custom_timesteps` must be in descending order.'	):
								scheduler.set_timesteps(timesteps=_A	)






						def UpperCAmelCase_				(      self      :      Dict	)							->    int:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	self.scheduler_classes[0]
							snake_case_		:			int											=	self.get_scheduler_config()
							snake_case_		:			List[str]											=	scheduler_class(**_A	)

							snake_case_		:			int											=	[100, 87, 50, 1, 0]
							snake_case_		:			Any											=	len(_A	)

							with self.assertRaises(_A		,   msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
								scheduler.set_timesteps(num_inference_steps=_A		,   timesteps=_A	)






						def UpperCAmelCase_				(      self      :      List[str]	)							->    List[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	self.scheduler_classes[0]
							snake_case_		:			List[str]											=	self.get_scheduler_config()
							snake_case_		:			Optional[Any]											=	scheduler_class(**_A	)

							snake_case_		:			Union[str, Any]											=	[scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    _A		,   msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,   ):
								scheduler.set_timesteps(timesteps=_A	)








import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
						import jax

						from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def __init__(      self      :      Dict		,   _A      :      List[str]		,   _A      :      Dict=13		,   _A      :      str=30		,   _A      :      Any=2		,   _A      :      Dict=3		,   _A      :      Optional[Any]=True		,   _A      :      Tuple=True		,   _A      :      List[str]=32		,   _A      :      int=5		,   _A      :      Optional[int]=4		,   _A      :      Dict=37		,   _A      :      Tuple="gelu"		,   _A      :      Dict=0.1		,   _A      :      str=0.1		,   _A      :      int=10		,   _A      :      Union[str, Any]=0.0_2		,   )							->    int:







							"""simple docstring"""


							snake_case_		:			int											=	parent
							snake_case_		:			str											=	batch_size
							snake_case_		:			List[str]											=	image_size
							snake_case_		:			Tuple											=	patch_size
							snake_case_		:			str											=	num_channels
							snake_case_		:			List[str]											=	is_training
							snake_case_		:			List[str]											=	use_labels
							snake_case_		:			str											=	hidden_size
							snake_case_		:			Union[str, Any]											=	num_hidden_layers
							snake_case_		:			Any											=	num_attention_heads
							snake_case_		:			List[Any]											=	intermediate_size
							snake_case_		:			Any											=	hidden_act
							snake_case_		:			List[Any]											=	hidden_dropout_prob
							snake_case_		:			List[Any]											=	attention_probs_dropout_prob
							snake_case_		:			Tuple											=	type_sequence_label_size
							snake_case_		:			Any											=	initializer_range

							# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							snake_case_		:			Tuple											=	(image_size // patch_size) ** 2
							snake_case_		:			List[Any]											=	num_patches + 1






						def UpperCAmelCase_				(      self      :      Dict	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			int											=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

							snake_case_		:			Optional[Any]											=	ViTConfig(
							    image_size=self.image_size		,   patch_size=self.patch_size		,   num_channels=self.num_channels		,   hidden_size=self.hidden_size		,   num_hidden_layers=self.num_hidden_layers		,   num_attention_heads=self.num_attention_heads		,   intermediate_size=self.intermediate_size		,   hidden_act=self.hidden_act		,   hidden_dropout_prob=self.hidden_dropout_prob		,   attention_probs_dropout_prob=self.attention_probs_dropout_prob		,   is_decoder=_A		,   initializer_range=self.initializer_range		,   )

							return config, pixel_values






						def UpperCAmelCase_				(      self      :      int		,   _A      :      Union[str, Any]		,   _A      :      List[Any]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	FlaxViTModel(config=_A	)
							snake_case_		:			Optional[Any]											=	model(_A	)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
							snake_case_		:			Union[str, Any]											=	(self.image_size, self.image_size)
							snake_case_		:			int											=	(self.patch_size, self.patch_size)
							snake_case_		:			Optional[Any]											=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
							self.parent.assertEqual(result.last_hidden_state.shape		,   (self.batch_size, num_patches + 1, self.hidden_size)	)






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Any		,   _A      :      List[str]	)							->    Dict:







							"""simple docstring"""


							snake_case_		:			Dict											=	self.type_sequence_label_size
							snake_case_		:			Any											=	FlaxViTForImageClassification(config=_A	)
							snake_case_		:			Optional[int]											=	model(_A	)
							self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.type_sequence_label_size)	)

							# test greyscale images
							snake_case_		:			List[str]											=	1
							snake_case_		:			Dict											=	FlaxViTForImageClassification(_A	)

							snake_case_		:			Optional[int]											=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
							snake_case_		:			Any											=	model(_A	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Any:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)						,(
							    snake_case_
							)						,
							)		:			Any											=	config_and_inputs
							snake_case_		:			Tuple											=	{'pixel_values': pixel_values}
							return config, inputs_dict
@require_flax
class SCREAMING_SNAKE_CASE_						(					snake_case_       ,				unittest.TestCase					):
						__magic_name__:			Tuple       =       (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






						def UpperCAmelCase_				(      self      :      List[str]	)							->    None:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	FlaxViTModelTester(self	)
							snake_case_		:			Any											=	ConfigTester(self		,   config_class=_A		,   has_text_modality=_A		,   hidden_size=37	)






						def UpperCAmelCase_				(      self      :      Any	)							->    Dict:







							"""simple docstring"""


							self.config_tester.run_common_tests()






						def UpperCAmelCase_				(      self      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			str											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_A	)






						def UpperCAmelCase_				(      self      :      List[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_A	)






						def UpperCAmelCase_				(      self      :      int	)							->    List[str]:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Tuple											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		:			str											=	model_class(_A	)
								snake_case_		:			Dict											=	inspect.signature(model.__call__	)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_		:			Optional[Any]											=	[*signature.parameters.keys()]

								snake_case_		:			Dict											=	['pixel_values']
								self.assertListEqual(arg_names[:1]		,   _A	)






						def UpperCAmelCase_				(      self      :      Optional[Any]	)							->    str:







							"""simple docstring"""


							snake_case_						,snake_case_		:			Optional[int]											=	self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__	):
									snake_case_		:			Dict											=	self._prepare_for_class(_A		,   _A	)
									snake_case_		:			Tuple											=	model_class(_A	)

									@jax.jit
									def model_jitted(_A      :      Optional[int]		,   **_A      :      Any	):
										return model(pixel_values=_A		,   **_A	)

									with self.subTest('JIT Enabled'	):
										snake_case_		:			Optional[Any]											=	model_jitted(**_A	).to_tuple()

									with self.subTest('JIT Disabled'	):
										with jax.disable_jit():
											snake_case_		:			Optional[Any]											=	model_jitted(**_A	).to_tuple()

									self.assertEqual(len(_A	)		,   len(_A	)	)
									for jitted_output, output in zip(_A		,   _A	):
										self.assertEqual(jitted_output.shape		,   output.shape	)






						@slow
						def UpperCAmelCase_				(      self      :      List[str]	)							->    Any:







							"""simple docstring"""


							for model_class_name in self.all_model_classes:
								snake_case_		:			List[Any]											=	model_class_name.from_pretrained('google/vit-base-patch16-224'	)
								snake_case_		:			Optional[Any]											=	model(np.ones((1, 3, 224, 224)	)	)
								self.assertIsNotNone(_A	)

