






'''simple docstring'''







from __future__ import annotations

import inspect
import unittest
from math import floor

import numpy as np

from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 TFCvtForImageClassification, TFCvtModel
			from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor

class _lowercase     (    _lowercase		):




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     List[str]	  =	self.config_class(**self.inputs_dict					)
														self.parent.assertTrue(hasattr(UpperCamelCase__      ,							"""embed_dim"""					)					)
														self.parent.assertTrue(hasattr(UpperCamelCase__      ,							"""num_heads"""					)					)



class _lowercase     :




							def __init__(							self:    Optional[int]      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Optional[int]=13      ,							UpperCamelCase__:    Tuple=64      ,							UpperCamelCase__:    Union[str, Any]=3      ,							UpperCamelCase__:    Optional[int]=[16, 48, 96]      ,							UpperCamelCase__:    int=[1, 3, 6]      ,							UpperCamelCase__:    Union[str, Any]=[1, 2, 10]      ,							UpperCamelCase__:    List[Any]=[7, 3, 3]      ,							UpperCamelCase__:    Union[str, Any]=[4, 2, 2]      ,							UpperCamelCase__:    Any=[2, 1, 1]      ,							UpperCamelCase__:    Optional[int]=[2, 2, 2]      ,							UpperCamelCase__:    Dict=[False, False, True]      ,							UpperCamelCase__:    int=[0.0, 0.0, 0.0]      ,							UpperCamelCase__:    Optional[int]=0.02      ,							UpperCamelCase__:    Optional[int]=1e-12      ,							UpperCamelCase__:    Any=True      ,							UpperCamelCase__:    Optional[int]=True      ,							UpperCamelCase__:    List[str]=2      ,							):
														lowerCamelCase__ :     int	  =	parent
														lowerCamelCase__ :     Optional[Any]	  =	batch_size
														lowerCamelCase__ :     str	  =	image_size
														lowerCamelCase__ :     Union[str, Any]	  =	patch_sizes
														lowerCamelCase__ :     str	  =	patch_stride
														lowerCamelCase__ :     str	  =	patch_padding
														lowerCamelCase__ :     Union[str, Any]	  =	is_training
														lowerCamelCase__ :     str	  =	use_labels
														lowerCamelCase__ :     List[Any]	  =	num_labels
														lowerCamelCase__ :     Dict	  =	num_channels
														lowerCamelCase__ :     Optional[Any]	  =	embed_dim
														lowerCamelCase__ :     int	  =	num_heads
														lowerCamelCase__ :     Optional[int]	  =	stride_kv
														lowerCamelCase__ :     Any	  =	depth
														lowerCamelCase__ :     List[str]	  =	cls_token
														lowerCamelCase__ :     str	  =	attention_drop_rate
														lowerCamelCase__ :     Union[str, Any]	  =	initializer_range
														lowerCamelCase__ :     Union[str, Any]	  =	layer_norm_eps




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

														lowerCamelCase__ :     Optional[Any]	  =	None
														if self.use_labels:
																					# create a random int32 tensor of given shape
																					lowerCamelCase__ :     Union[str, Any]	  =	ids_tensor([self.batch_size]      ,							self.num_labels					)

														lowerCamelCase__ :     Union[str, Any]	  =	self.get_config()
														return config, pixel_values, labels




							def 		lowerCamelCase_    (							self:    Tuple					):
														return CvtConfig(
														    image_size=self.image_size      ,							num_labels=self.num_labels      ,							num_channels=self.num_channels      ,							embed_dim=self.embed_dim      ,							num_heads=self.num_heads      ,							patch_sizes=self.patch_sizes      ,							patch_padding=self.patch_padding      ,							patch_stride=self.patch_stride      ,							stride_kv=self.stride_kv      ,							depth=self.depth      ,							cls_token=self.cls_token      ,							attention_drop_rate=self.attention_drop_rate      ,							initializer_range=self.initializer_range      ,							)




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    Dict      ,							UpperCamelCase__:    Union[str, Any]      ,							UpperCamelCase__:    Union[str, Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	TFCvtModel(config=UpperCamelCase__					)
														lowerCamelCase__ :     int	  =	model(UpperCamelCase__      ,							training=UpperCamelCase__					)
														lowerCamelCase__ :     Dict	  =	(self.image_size, self.image_size)
														lowerCamelCase__  ,		lowerCamelCase__ :     List[str]	  =	image_size[0], image_size[1]
														for i in range(len(self.depth					)					):
																					lowerCamelCase__ :     Tuple	  =	floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1					)
																					lowerCamelCase__ :     List[Any]	  =	floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1					)
														self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, self.embed_dim[-1], height, width)					)




							def 		lowerCamelCase_    (							self:    List[str]      ,							UpperCamelCase__:    List[str]      ,							UpperCamelCase__:    List[Any]      ,							UpperCamelCase__:    Any					):
														lowerCamelCase__ :     Optional[int]	  =	self.num_labels
														lowerCamelCase__ :     List[str]	  =	TFCvtForImageClassification(UpperCamelCase__					)
														lowerCamelCase__ :     List[Any]	  =	model(UpperCamelCase__      ,							labels=UpperCamelCase__      ,							training=UpperCamelCase__					)
														self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)					)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__ :     int	  =	config_and_inputs
														lowerCamelCase__ :     Optional[int]	  =	{"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_tf
class _lowercase     (    _lowercase    ,				_lowercase    ,				unittest.TestCase		):
							a    				=	(TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
							a    				=	(
							    {"""feature-extraction""": TFCvtModel, """image-classification""": TFCvtForImageClassification}
							    if is_tf_available()
							    else {}
							)
							a    				=	False
							a    				=	False
							a    				=	False
							a    				=	False
							a    				=	False




							def 		lowerCamelCase_    (							self:    Dict					):
														lowerCamelCase__ :     Dict	  =	TFCvtModelTester(self					)
														lowerCamelCase__ :     List[Any]	  =	TFCvtConfigTester(self      ,							config_class=UpperCamelCase__      ,							has_text_modality=UpperCamelCase__      ,							hidden_size=37					)




							def 		lowerCamelCase_    (							self:    List[str]					):
														self.config_tester.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()




							@unittest.skip(reason="""Cvt does not output attentions"""					)
							def 		lowerCamelCase_    (							self:    str					):
														pass




							@unittest.skip(reason="""Cvt does not use inputs_embeds"""					)
							def 		lowerCamelCase_    (							self:    Union[str, Any]					):
														pass




							@unittest.skip(reason="""Cvt does not support input and output embeddings"""					)
							def 		lowerCamelCase_    (							self:    int					):
														pass




							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0      ,							reason="""TF does not support backprop for grouped convolutions on CPU."""      ,							)
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														super().test_dataset_conversion()




							@unittest.skipIf(
							    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0      ,							reason="""TF does not support backprop for grouped convolutions on CPU."""      ,							)
							@slow
							def 		lowerCamelCase_    (							self:    str					):
														super().test_keras_fit()




							@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8"""					)
							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Dict	  =	tf.keras.mixed_precision.Policy("""mixed_float16"""					)
														tf.keras.mixed_precision.set_global_policy(UpperCamelCase__					)
														super().test_keras_fit()
														tf.keras.mixed_precision.set_global_policy("""float32"""					)




							def 		lowerCamelCase_    (							self:    int					):
														lowerCamelCase__  ,		lowerCamelCase__ :     Optional[Any]	  =	self.model_tester.prepare_config_and_inputs_for_common()

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

																					lowerCamelCase__ :     Tuple	  =	["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														def check_hidden_states_output(UpperCamelCase__:    int      ,							UpperCamelCase__:    Optional[Any]      ,							UpperCamelCase__:    Optional[int]					):
																					lowerCamelCase__ :     int	  =	model_class(UpperCamelCase__					)

																					lowerCamelCase__ :     Dict	  =	model(**self._prepare_for_class(UpperCamelCase__      ,							UpperCamelCase__					)					)
																					lowerCamelCase__ :     Any	  =	outputs.hidden_states

																					lowerCamelCase__ :     Union[str, Any]	  =	len(self.model_tester.depth					)
																					self.assertEqual(len(UpperCamelCase__					)      ,							UpperCamelCase__					)

																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-3:]					)      ,							[
																					        self.model_tester.embed_dim[0],
																					        self.model_tester.image_size // 4,
																					        self.model_tester.image_size // 4,
																					    ]      ,							)

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

														for model_class in self.all_model_classes:
																					lowerCamelCase__ :     Any	  =	True
																					check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__ :     Any	  =	True

																					check_hidden_states_output(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]					):
														lowerCamelCase__ :     Union[str, Any]	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    Any					):
														lowerCamelCase__ :     Tuple	  =	self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__					)




							@slow
							def 		lowerCamelCase_    (							self:    int					):
														for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__ :     List[str]	  =	TFCvtModel.from_pretrained(UpperCamelCase__					)
																					self.assertIsNotNone(UpperCamelCase__					)





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



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




							@cached_property
							def 		lowerCamelCase_    (							self:    str					):
														return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)




							@slow
							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Tuple	  =	TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)

														lowerCamelCase__ :     Tuple	  =	self.default_image_processor
														lowerCamelCase__ :     int	  =	prepare_img()
														lowerCamelCase__ :     Any	  =	image_processor(images=UpperCamelCase__      ,							return_tensors="""tf"""					)

														# forward pass
														lowerCamelCase__ :     Tuple	  =	model(**UpperCamelCase__					)

														# verify the logits
														lowerCamelCase__ :     List[str]	  =	tf.TensorShape((1, 1_000)					)
														self.assertEqual(outputs.logits.shape      ,							UpperCamelCase__					)

														lowerCamelCase__ :     Optional[int]	  =	tf.constant([0.9_285, 0.9_015, -0.3_150]					)
														self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()      ,							UpperCamelCase__      ,							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__					)

