






'''simple docstring'''







from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor

class _lowercase     :




							def __init__(							self:    Union[str, Any]      ,							UpperCamelCase__:    Tuple      ,							UpperCamelCase__:    str=13      ,							UpperCamelCase__:    Tuple=30      ,							UpperCamelCase__:    List[Any]=2      ,							UpperCamelCase__:    Any=3      ,							UpperCamelCase__:    int=True      ,							UpperCamelCase__:    List[Any]=True      ,							UpperCamelCase__:    int=32      ,							UpperCamelCase__:    int=2      ,							UpperCamelCase__:    int=4      ,							UpperCamelCase__:    List[str]=37      ,							UpperCamelCase__:    Optional[Any]="gelu"      ,							UpperCamelCase__:    List[str]=0.1      ,							UpperCamelCase__:    Tuple=0.1      ,							UpperCamelCase__:    Tuple=10      ,							UpperCamelCase__:    Optional[Any]=0.02      ,							UpperCamelCase__:    List[str]=3      ,							UpperCamelCase__:    Optional[Any]=0.6      ,							UpperCamelCase__:    Union[str, Any]=None      ,							):
														lowerCamelCase__ :     Optional[int]	  =	parent
														lowerCamelCase__ :     Any	  =	batch_size
														lowerCamelCase__ :     Any	  =	image_size
														lowerCamelCase__ :     Union[str, Any]	  =	patch_size
														lowerCamelCase__ :     Tuple	  =	num_channels
														lowerCamelCase__ :     Tuple	  =	is_training
														lowerCamelCase__ :     Union[str, Any]	  =	use_labels
														lowerCamelCase__ :     Optional[int]	  =	hidden_size
														lowerCamelCase__ :     Tuple	  =	num_hidden_layers
														lowerCamelCase__ :     str	  =	num_attention_heads
														lowerCamelCase__ :     Dict	  =	intermediate_size
														lowerCamelCase__ :     List[Any]	  =	hidden_act
														lowerCamelCase__ :     Union[str, Any]	  =	hidden_dropout_prob
														lowerCamelCase__ :     Any	  =	attention_probs_dropout_prob
														lowerCamelCase__ :     Tuple	  =	type_sequence_label_size
														lowerCamelCase__ :     Optional[int]	  =	initializer_range
														lowerCamelCase__ :     List[Any]	  =	mask_ratio
														lowerCamelCase__ :     Dict	  =	scope

														# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
														# (we add 1 for the [CLS] token)
														lowerCamelCase__ :     List[str]	  =	(image_size // patch_size) ** 2
														lowerCamelCase__ :     str	  =	int(math.ceil((1 - mask_ratio) * (num_patches + 1)					)					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     str	  =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

														lowerCamelCase__ :     List[Any]	  =	None
														if self.use_labels:
																					lowerCamelCase__ :     Dict	  =	ids_tensor([self.batch_size]      ,							self.type_sequence_label_size					)

														lowerCamelCase__ :     Union[str, Any]	  =	self.get_config()

														return config, pixel_values, labels




							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return ViTMAEConfig(
														    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      ,							decoder_hidden_size=self.hidden_size      ,							decoder_num_hidden_layers=self.num_hidden_layers      ,							decoder_num_attention_heads=self.num_attention_heads      ,							decoder_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=_lowerCamelCase      ,							initializer_range=self.initializer_range      ,							mask_ratio=self.mask_ratio      ,							)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Tuple					):
														lowerCamelCase__ :     Union[str, Any]	  =	TFViTMAEModel(config=_lowerCamelCase					)
														lowerCamelCase__ :     List[Any]	  =	model(_lowerCamelCase      ,							training=_lowerCamelCase					)
														self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.seq_length, self.hidden_size)					)




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[int]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Any	  =	TFViTMAEForPreTraining(_lowerCamelCase					)
														lowerCamelCase__ :     List[str]	  =	model(_lowerCamelCase      ,							training=_lowerCamelCase					)
														# expected sequence length = num_patches
														lowerCamelCase__ :     int	  =	(self.image_size // self.patch_size) ** 2
														lowerCamelCase__ :     int	  =	self.patch_size**2 * self.num_channels
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, num_patches, expected_num_channels)					)

														# test greyscale images
														lowerCamelCase__ :     str	  =	1
														lowerCamelCase__ :     Dict	  =	TFViTMAEForPreTraining(_lowerCamelCase					)

														lowerCamelCase__ :     Tuple	  =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
														lowerCamelCase__ :     Optional[Any]	  =	model(_lowerCamelCase      ,							training=_lowerCamelCase					)
														lowerCamelCase__ :     Tuple	  =	self.patch_size**2
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, num_patches, expected_num_channels)					)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Tuple	  =	self.prepare_config_and_inputs()
														(lowerCamelCase__) :     List[str]	  =	config_and_inputs
														lowerCamelCase__ :     Tuple	  =	{'''pixel_values''': pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase     (    a__    ,				a__    ,				unittest.TestCase		):
							a    				=	(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
							a    				=	{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

							a    				=	False
							a    				=	False
							a    				=	False
							a    				=	False




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     List[str]	  =	TFViTMAEModelTester(self					)
														lowerCamelCase__ :     Optional[Any]	  =	ConfigTester(self      ,							config_class=_lowerCamelCase      ,							has_text_modality=_lowerCamelCase      ,							hidden_size=37					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""ViTMAE does not use inputs_embeds"""					)
							def 		lowerCamelCase_    (							self:    Any					):
														pass




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     List[str]	  =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     Tuple	  =	model_class(_lowerCamelCase					)
																					self.assertIsInstance(model.get_input_embeddings()      ,							(tf.keras.layers.Layer)					)
																					lowerCamelCase__ :     Union[str, Any]	  =	model.get_output_embeddings()
																					self.assertTrue(x is None or isinstance(_lowerCamelCase      ,							tf.keras.layers.Layer					)					)




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     str	  =	self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     int	  =	model_class(_lowerCamelCase					)
																					lowerCamelCase__ :     Any	  =	inspect.signature(model.call					)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__ :     List[Any]	  =	[*signature.parameters.keys()]

																					lowerCamelCase__ :     int	  =	['''pixel_values''']
																					self.assertListEqual(arg_names[:1]      ,							_lowerCamelCase					)




							def 		lowerCamelCase_    (							self:    str					):
														lowerCamelCase__ :     Dict	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*_lowerCamelCase					)




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Any	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_pretraining(*_lowerCamelCase					)




							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														np.random.seed(2					)

														lowerCamelCase__ :     str	  =	self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__ :     Any	  =	int((config.image_size // config.patch_size) ** 2					)
														lowerCamelCase__ :     Any	  =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     List[str]	  =	model_class(_lowerCamelCase					)
																					lowerCamelCase__ :     Tuple	  =	self._prepare_for_class(_lowerCamelCase      ,							_lowerCamelCase					)

																					lowerCamelCase__ :     Tuple	  =	model(_lowerCamelCase      ,							noise=_lowerCamelCase					)

																					lowerCamelCase__ :     List[str]	  =	copy.deepcopy(self._prepare_for_class(_lowerCamelCase      ,							_lowerCamelCase					)					)
																					lowerCamelCase__ :     Any	  =	model(**_lowerCamelCase      ,							noise=_lowerCamelCase					)
																					lowerCamelCase__ :     Optional[Any]	  =	outputs_dict[0].numpy()
																					lowerCamelCase__ :     Optional[Any]	  =	outputs_keywords[0].numpy()

																					self.assertLess(np.sum(np.abs(output_dict - output_keywords					)					)      ,							1e-6					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														np.random.seed(2					)

														lowerCamelCase__ :     str	  =	self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__ :     str	  =	int((config.image_size // config.patch_size) ** 2					)
														lowerCamelCase__ :     Dict	  =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

														def prepare_numpy_arrays(UpperCamelCase__:    Optional[Any]					):
																					lowerCamelCase__ :     Any	  =	{}
																					for k, v in inputs_dict.items():
																												if tf.is_tensor(_lowerCamelCase					):
																																			lowerCamelCase__ :     Dict	  =	v.numpy()
																												else:
																																			lowerCamelCase__ :     Union[str, Any]	  =	np.array(_lowerCamelCase					)

																					return inputs_np_dict

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     List[str]	  =	model_class(_lowerCamelCase					)

																					lowerCamelCase__ :     List[Any]	  =	self._prepare_for_class(_lowerCamelCase      ,							_lowerCamelCase					)
																					lowerCamelCase__ :     int	  =	prepare_numpy_arrays(_lowerCamelCase					)

																					lowerCamelCase__ :     str	  =	model(_lowerCamelCase      ,							noise=_lowerCamelCase					)
																					lowerCamelCase__ :     Union[str, Any]	  =	model(**_lowerCamelCase      ,							noise=_lowerCamelCase					)
																					self.assert_outputs_same(_lowerCamelCase      ,							_lowerCamelCase					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    Optional[Any]					):
														np.random.seed(2					)

														lowerCamelCase__ :     List[Any]	  =	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2					)
														lowerCamelCase__ :     int	  =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
														lowerCamelCase__ :     List[Any]	  =	tf.constant(_lowerCamelCase					)

														# Add `noise` argument.
														# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
														lowerCamelCase__ :     str	  =	tf_noise

														super().check_pt_tf_models(_lowerCamelCase      ,							_lowerCamelCase      ,							_lowerCamelCase					)




							def 		lowerCamelCase_    (							self:    Dict					):
														np.random.seed(2					)

														lowerCamelCase__ :     int	  =	self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__ :     Dict	  =	{
														    module_member
														    for model_class in self.all_model_classes
														    for module in (import_module(model_class.__module__					),)
														    for module_member_name in dir(_lowerCamelCase					)
														    if module_member_name.endswith("""MainLayer"""					)
														    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
														    and module_member_name[: -len("""MainLayer"""					)] == model_class.__name__[: -len("""Model"""					)]
														    for module_member in (getattr(_lowerCamelCase      ,							_lowerCamelCase					),)
														    if isinstance(_lowerCamelCase      ,							_lowerCamelCase					)
														    and tf.keras.layers.Layer in module_member.__bases__
														    and getattr(_lowerCamelCase      ,							"""_keras_serializable"""      ,							_lowerCamelCase					)
														}

														lowerCamelCase__ :     List[Any]	  =	int((config.image_size // config.patch_size) ** 2					)
														lowerCamelCase__ :     Union[str, Any]	  =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
														lowerCamelCase__ :     Any	  =	tf.convert_to_tensor(_lowerCamelCase					)
														inputs_dict.update({"""noise""": noise}					)

														for main_layer_class in tf_main_layer_classes:
																					lowerCamelCase__ :     Optional[Any]	  =	main_layer_class(_lowerCamelCase					)

																					lowerCamelCase__ :     List[Any]	  =	{
																					    name: tf.keras.Input(tensor.shape[1:]      ,							dtype=tensor.dtype					) for name, tensor in inputs_dict.items()
																					}

																					lowerCamelCase__ :     Tuple	  =	tf.keras.Model(_lowerCamelCase      ,							outputs=main_layer(_lowerCamelCase					)					)
																					lowerCamelCase__ :     Dict	  =	model(_lowerCamelCase					)

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												lowerCamelCase__ :     List[Any]	  =	os.path.join(_lowerCamelCase      ,							"""keras_model.h5"""					)
																												model.save(_lowerCamelCase					)
																												lowerCamelCase__ :     Any	  =	tf.keras.models.load_model(
																												    _lowerCamelCase      ,							custom_objects={main_layer_class.__name__: main_layer_class}					)
																												assert isinstance(_lowerCamelCase      ,							tf.keras.Model					)
																												lowerCamelCase__ :     Optional[int]	  =	model(_lowerCamelCase					)
																												self.assert_outputs_same(_lowerCamelCase      ,							_lowerCamelCase					)




							@slow
							def 		lowerCamelCase_    (							self:    str					):
														np.random.seed(2					)

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

														lowerCamelCase__ :     Union[str, Any]	  =	int((config.image_size // config.patch_size) ** 2					)
														lowerCamelCase__ :     int	  =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     Any	  =	model_class(_lowerCamelCase					)
																					lowerCamelCase__ :     List[Any]	  =	self._prepare_for_class(_lowerCamelCase      ,							_lowerCamelCase					)
																					lowerCamelCase__ :     Optional[Any]	  =	model(_lowerCamelCase      ,							noise=_lowerCamelCase					)

																					if model_class.__name__ == "TFViTMAEModel":
																												lowerCamelCase__ :     Dict	  =	outputs.last_hidden_state.numpy()
																												lowerCamelCase__ :     List[Any]	  =	0
																					else:
																												lowerCamelCase__ :     Any	  =	outputs.logits.numpy()
																												lowerCamelCase__ :     List[Any]	  =	0

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												model.save_pretrained(_lowerCamelCase      ,							saved_model=_lowerCamelCase					)
																												lowerCamelCase__ :     List[Any]	  =	model_class.from_pretrained(_lowerCamelCase					)
																												lowerCamelCase__ :     Any	  =	model(_lowerCamelCase      ,							noise=_lowerCamelCase					)

																												if model_class.__name__ == "TFViTMAEModel":
																																			lowerCamelCase__ :     List[Any]	  =	after_outputs['''last_hidden_state'''].numpy()
																																			lowerCamelCase__ :     Optional[Any]	  =	0
																												else:
																																			lowerCamelCase__ :     Dict	  =	after_outputs['''logits'''].numpy()
																																			lowerCamelCase__ :     Dict	  =	0

																												lowerCamelCase__ :     List[Any]	  =	np.amax(np.abs(out_a - out_a					)					)
																												self.assertLessEqual(_lowerCamelCase      ,							1e-5					)




							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														np.random.seed(2					)

														lowerCamelCase__ :     List[str]	  =	self.model_tester.prepare_config_and_inputs_for_common()

														lowerCamelCase__ :     int	  =	int((config.image_size // config.patch_size) ** 2					)
														lowerCamelCase__ :     Dict	  =	np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     List[Any]	  =	model_class(_lowerCamelCase					)
																					lowerCamelCase__ :     str	  =	self._prepare_for_class(_lowerCamelCase      ,							_lowerCamelCase					)

																					lowerCamelCase__ :     str	  =	model(_lowerCamelCase      ,							noise=_lowerCamelCase					)
																					lowerCamelCase__ :     Optional[Any]	  =	model.get_config()
																					# make sure that returned config is jsonifiable, which is required by keras
																					json.dumps(_lowerCamelCase					)
																					lowerCamelCase__ :     Optional[int]	  =	model_class.from_config(model.get_config()					)
																					# make sure it also accepts a normal config
																					lowerCamelCase__ :     Any	  =	model_class.from_config(model.config					)
																					lowerCamelCase__ :     int	  =	new_model(_lowerCamelCase					)  # Build model
																					new_model.set_weights(model.get_weights()					)
																					lowerCamelCase__ :     Dict	  =	new_model(_lowerCamelCase      ,							noise=_lowerCamelCase					)

																					self.assert_outputs_same(_lowerCamelCase      ,							_lowerCamelCase					)




							@unittest.skip(
							    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""					)
							def 		lowerCamelCase_    (							self:    List[str]					):
														pass




							@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""					)
							def 		lowerCamelCase_    (							self:    List[Any]					):
														pass




							@slow
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														lowerCamelCase__ :     Tuple	  =	TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""					)
														self.assertIsNotNone(_lowerCamelCase					)





def 			SCREAMING_SNAKE_CASE_	()				->	List[str]:
							lowerCamelCase__ :     Optional[int]	  =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
							return image



@require_tf
@require_vision
class _lowercase     (    unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_    (							self:    int					):
														return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""					) if is_vision_available() else None




							@slow
							def 		lowerCamelCase_    (							self:    Optional[Any]					):
														np.random.seed(2					)

														lowerCamelCase__ :     List[str]	  =	TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""					)

														lowerCamelCase__ :     str	  =	self.default_image_processor
														lowerCamelCase__ :     Dict	  =	prepare_img()
														lowerCamelCase__ :     Union[str, Any]	  =	image_processor(images=_lowerCamelCase      ,							return_tensors="""tf"""					)

														# prepare a noise vector that will be also used for testing the TF model
														# (this way we can ensure that the PT and TF models operate on the same inputs)
														lowerCamelCase__ :     Tuple	  =	ViTMAEConfig()
														lowerCamelCase__ :     List[str]	  =	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2					)
														lowerCamelCase__ :     Optional[Any]	  =	np.random.uniform(size=(1, num_patches)					)

														# forward pass
														lowerCamelCase__ :     Union[str, Any]	  =	model(**_lowerCamelCase      ,							noise=_lowerCamelCase					)

														# verify the logits
														lowerCamelCase__ :     Dict	  =	tf.convert_to_tensor([1, 196, 768]					)
														self.assertEqual(outputs.logits.shape      ,							_lowerCamelCase					)

														lowerCamelCase__ :     Dict	  =	tf.convert_to_tensor(
														    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]					)

														tf.debugging.assert_near(outputs.logits[0, :3, :3]      ,							_lowerCamelCase      ,							atol=1e-4					)









'''simple docstring'''







from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_A     :      Union[str, Any]   					=8





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=BITS				)				->	Tuple:
							lowerCamelCase__ :     List[str]	  =	x.device

							lowerCamelCase__ :     Any	  =	(x * 255).int().clamp(0    ,      255				)

							lowerCamelCase__ :     Optional[int]	  =	2 ** torch.arange(bits - 1    ,      -1    ,      -1    ,      device=UpperCamelCase				)
							lowerCamelCase__ :     int	  =	rearrange(UpperCamelCase    ,      """d -> d 1 1"""				)
							lowerCamelCase__ :     List[str]	  =	rearrange(UpperCamelCase    ,      """b c h w -> b c 1 h w"""				)

							lowerCamelCase__ :     Tuple	  =	((x & mask) != 0).float()
							lowerCamelCase__ :     List[Any]	  =	rearrange(UpperCamelCase    ,      """b c d h w -> b (c d) h w"""				)
							lowerCamelCase__ :     Optional[int]	  =	bits * 2 - 1
							return bits





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase=BITS				)				->	List[Any]:
							lowerCamelCase__ :     List[Any]	  =	x.device

							lowerCamelCase__ :     Dict	  =	(x > 0).int()
							lowerCamelCase__ :     Optional[Any]	  =	2 ** torch.arange(bits - 1    ,      -1    ,      -1    ,      device=UpperCamelCase    ,      dtype=torch.intaa				)

							lowerCamelCase__ :     List[Any]	  =	rearrange(UpperCamelCase    ,      """d -> d 1 1"""				)
							lowerCamelCase__ :     List[str]	  =	rearrange(UpperCamelCase    ,      """b (c d) h w -> b c d h w"""    ,      d=8				)
							lowerCamelCase__ :     List[Any]	  =	reduce(x * mask    ,      """b c d h w -> b c h w"""    ,      """sum"""				)
							return (dec / 255).clamp(0.0    ,      1.0				)





def 			SCREAMING_SNAKE_CASE_	(self    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase = 0.0    ,      UpperCamelCase = True    ,      UpperCamelCase=None    ,      UpperCamelCase = True    ,      )				->	Union[DDIMSchedulerOutput, Tuple]:
							if self.num_inference_steps is None:
														raise ValueError(
														    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""				)

							# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
							# Ideally, read DDIM paper in-detail understanding

							# Notation (<variable name> -> <name in paper>
							# - pred_noise_t -> e_theta(x_t, t)
							# - pred_original_sample -> f_theta(x_t, t) or x_0
							# - std_dev_t -> sigma_t
							# - eta -> η
							# - pred_sample_direction -> "direction pointing to x_t"
							# - pred_prev_sample -> "x_t-1"

							# 1. get previous step value (=t-1)
							lowerCamelCase__ :     Optional[int]	  =	timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							lowerCamelCase__ :     str	  =	self.alphas_cumprod[timestep]
							lowerCamelCase__ :     List[str]	  =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

							lowerCamelCase__ :     Optional[int]	  =	1 - alpha_prod_t

							# 3. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCamelCase__ :     Optional[int]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

							# 4. Clip "predicted x_0"
							lowerCamelCase__ :     Dict	  =	self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase__ :     Optional[Any]	  =	torch.clamp(UpperCamelCase    ,      -scale    ,      UpperCamelCase				)

							# 5. compute variance: "sigma_t(η)" -> see formula (16)
							# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
							lowerCamelCase__ :     Tuple	  =	self._get_variance(UpperCamelCase    ,      UpperCamelCase				)
							lowerCamelCase__ :     Optional[int]	  =	eta * variance ** 0.5

							if use_clipped_model_output:
														# the model_output is always re-derived from the clipped x_0 in Glide
														lowerCamelCase__ :     int	  =	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

							# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCamelCase__ :     Optional[Any]	  =	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							lowerCamelCase__ :     Tuple	  =	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if eta > 0:
														# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
														lowerCamelCase__ :     Dict	  =	model_output.device if torch.is_tensor(UpperCamelCase				) else """cpu"""
														lowerCamelCase__ :     str	  =	torch.randn(model_output.shape    ,      dtype=model_output.dtype    ,      generator=UpperCamelCase				).to(UpperCamelCase				)
														lowerCamelCase__ :     Optional[Any]	  =	self._get_variance(UpperCamelCase    ,      UpperCamelCase				) ** 0.5 * eta * noise

														lowerCamelCase__ :     int	  =	prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=UpperCamelCase    ,      pred_original_sample=UpperCamelCase				)





def 			SCREAMING_SNAKE_CASE_	(self    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase="epsilon"    ,      UpperCamelCase=None    ,      UpperCamelCase = True    ,      )				->	Union[DDPMSchedulerOutput, Tuple]:
							lowerCamelCase__ :     List[Any]	  =	timestep

							if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	torch.split(UpperCamelCase    ,      sample.shape[1]    ,      dim=1				)
							else:
														lowerCamelCase__ :     List[str]	  =	None

							# 1. compute alphas, betas
							lowerCamelCase__ :     str	  =	self.alphas_cumprod[t]
							lowerCamelCase__ :     List[str]	  =	self.alphas_cumprod[t - 1] if t > 0 else self.one
							lowerCamelCase__ :     str	  =	1 - alpha_prod_t
							lowerCamelCase__ :     List[Any]	  =	1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if prediction_type == "epsilon":
														lowerCamelCase__ :     Union[str, Any]	  =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif prediction_type == "sample":
														lowerCamelCase__ :     Optional[Any]	  =	model_output
							else:
														raise ValueError(f'''Unsupported prediction_type {prediction_type}.'''				)

							# 3. Clip "predicted x_0"
							lowerCamelCase__ :     str	  =	self.bit_scale
							if self.config.clip_sample:
														lowerCamelCase__ :     List[Any]	  =	torch.clamp(UpperCamelCase    ,      -scale    ,      UpperCamelCase				)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCamelCase__ :     Tuple	  =	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
							lowerCamelCase__ :     Tuple	  =	self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							lowerCamelCase__ :     int	  =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							lowerCamelCase__ :     Optional[Any]	  =	0
							if t > 0:
														lowerCamelCase__ :     Optional[Any]	  =	torch.randn(
														    model_output.size()    ,      dtype=model_output.dtype    ,      layout=model_output.layout    ,      generator=UpperCamelCase				).to(model_output.device				)
														lowerCamelCase__ :     str	  =	(self._get_variance(UpperCamelCase    ,      predicted_variance=UpperCamelCase				) ** 0.5) * noise

							lowerCamelCase__ :     Optional[int]	  =	pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=UpperCamelCase    ,      pred_original_sample=UpperCamelCase				)



class _lowercase     (    _lowercase		):




							def __init__(							self:    List[str]      ,							UpperCamelCase__:    UNetaDConditionModel      ,							UpperCamelCase__:    Union[DDIMScheduler, DDPMScheduler]      ,							UpperCamelCase__:    Optional[float] = 1.0      ,							):
														super().__init__()
														lowerCamelCase__ :     Optional[int]	  =	bit_scale
														lowerCamelCase__ :     List[Any]	  =	(
														    ddim_bit_scheduler_step if isinstance(UpperCamelCase__      ,							UpperCamelCase__					) else ddpm_bit_scheduler_step
														)

														self.register_modules(unet=UpperCamelCase__      ,							scheduler=UpperCamelCase__					)




							@torch.no_grad()
							def __call__(							self:    Union[str, Any]      ,							UpperCamelCase__:    Optional[int] = 256      ,							UpperCamelCase__:    Optional[int] = 256      ,							UpperCamelCase__:    Optional[int] = 50      ,							UpperCamelCase__:    Optional[torch.Generator] = None      ,							UpperCamelCase__:    Optional[int] = 1      ,							UpperCamelCase__:    Optional[str] = "pil"      ,							UpperCamelCase__:    bool = True      ,							**UpperCamelCase__:    int      ,							):
														lowerCamelCase__ :     List[Any]	  =	torch.randn(
														    (batch_size, self.unet.config.in_channels, height, width)      ,							generator=UpperCamelCase__      ,							)
														lowerCamelCase__ :     Union[str, Any]	  =	decimal_to_bits(UpperCamelCase__					) * self.bit_scale
														lowerCamelCase__ :     Union[str, Any]	  =	latents.to(self.device					)

														self.scheduler.set_timesteps(UpperCamelCase__					)

														for t in self.progress_bar(self.scheduler.timesteps					):
																					# predict the noise residual
																					lowerCamelCase__ :     Tuple	  =	self.unet(UpperCamelCase__      ,							UpperCamelCase__					).sample

																					# compute the previous noisy sample x_t -> x_t-1
																					lowerCamelCase__ :     Any	  =	self.scheduler.step(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					).prev_sample

														lowerCamelCase__ :     Dict	  =	bits_to_decimal(UpperCamelCase__					)

														if output_type == "pil":
																					lowerCamelCase__ :     int	  =	self.numpy_to_pil(UpperCamelCase__					)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=UpperCamelCase__					)

