






'''simple docstring'''







import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :      Optional[Any]   					=logging.get_logger(__name__)

_A     :      List[str]   					={
    '''nvidia/segformer-b0-finetuned-ade-512-512''': (
        '''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class _lowercase     (    lowerCamelCase__		):
							a    				=	"""segformer"""




							def __init__(							self:    int      ,							UpperCamelCase__:    str=3      ,							UpperCamelCase__:    int=4      ,							UpperCamelCase__:    Optional[Any]=[2, 2, 2, 2]      ,							UpperCamelCase__:    Union[str, Any]=[8, 4, 2, 1]      ,							UpperCamelCase__:    List[Any]=[32, 64, 160, 256]      ,							UpperCamelCase__:    Union[str, Any]=[7, 3, 3, 3]      ,							UpperCamelCase__:    int=[4, 2, 2, 2]      ,							UpperCamelCase__:    Any=[1, 2, 5, 8]      ,							UpperCamelCase__:    List[str]=[4, 4, 4, 4]      ,							UpperCamelCase__:    Tuple="gelu"      ,							UpperCamelCase__:    List[Any]=0.0      ,							UpperCamelCase__:    int=0.0      ,							UpperCamelCase__:    Optional[int]=0.1      ,							UpperCamelCase__:    List[Any]=0.02      ,							UpperCamelCase__:    Dict=0.1      ,							UpperCamelCase__:    Any=1e-6      ,							UpperCamelCase__:    Union[str, Any]=256      ,							UpperCamelCase__:    Union[str, Any]=255      ,							**UpperCamelCase__:    List[Any]      ,							):
														super().__init__(**UpperCamelCase__					)

														if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
																					warnings.warn(
																					    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
																					    """ removed, as the behaviour will default to that of reshape_last_stage = True."""      ,							UpperCamelCase__      ,							)

														lowerCamelCase__ :     Any	  =	num_channels
														lowerCamelCase__ :     Tuple	  =	num_encoder_blocks
														lowerCamelCase__ :     int	  =	depths
														lowerCamelCase__ :     int	  =	sr_ratios
														lowerCamelCase__ :     Dict	  =	hidden_sizes
														lowerCamelCase__ :     Tuple	  =	patch_sizes
														lowerCamelCase__ :     Optional[int]	  =	strides
														lowerCamelCase__ :     str	  =	mlp_ratios
														lowerCamelCase__ :     Dict	  =	num_attention_heads
														lowerCamelCase__ :     Optional[int]	  =	hidden_act
														lowerCamelCase__ :     Optional[Any]	  =	hidden_dropout_prob
														lowerCamelCase__ :     str	  =	attention_probs_dropout_prob
														lowerCamelCase__ :     Tuple	  =	classifier_dropout_prob
														lowerCamelCase__ :     Tuple	  =	initializer_range
														lowerCamelCase__ :     List[Any]	  =	drop_path_rate
														lowerCamelCase__ :     Optional[Any]	  =	layer_norm_eps
														lowerCamelCase__ :     str	  =	decoder_hidden_size
														lowerCamelCase__ :     Union[str, Any]	  =	kwargs.get("""reshape_last_stage"""      ,							UpperCamelCase__					)
														lowerCamelCase__ :     Tuple	  =	semantic_loss_ignore_index



class _lowercase     (    lowerCamelCase__		):
							a    				=	version.parse("""1.11"""		)




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return OrderedDict(
														    [
														        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
														    ]					)




							@property
							def 		lowerCamelCase_    (							self:    Any					):
														return 1e-4




							@property
							def 		lowerCamelCase_    (							self:    Optional[int]					):
														return 12









'''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__					)

