


"""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


UpperCAmelCase__  							=8


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: int=BITS ):






 """simple docstring"""







 __lowercase									=	x.device

 __lowercase									=	(x * 255).int().clamp(0				,   255 )

 __lowercase									=	2 ** torch.arange(bits - 1				,   -1				,   -1				,   device=__UpperCAmelCase )
 __lowercase									=	rearrange(__UpperCAmelCase				,   """d -> d 1 1""" )
 __lowercase									=	rearrange(__UpperCAmelCase				,   """b c h w -> b c 1 h w""" )

 __lowercase									=	((x & mask) != 0).float()
 __lowercase									=	rearrange(__UpperCAmelCase				,   """b c d h w -> b (c d) h w""" )
 __lowercase									=	bits * 2 - 1
 return bits


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: int=BITS ):






 """simple docstring"""







 __lowercase									=	x.device

 __lowercase									=	(x > 0).int()
 __lowercase									=	2 ** torch.arange(bits - 1				,   -1				,   -1				,   device=__UpperCAmelCase				,   dtype=torch.intaa )

 __lowercase									=	rearrange(__UpperCAmelCase				,   """d -> d 1 1""" )
 __lowercase									=	rearrange(__UpperCAmelCase				,   """b (c d) h w -> b c d h w"""				,   d=8 )
 __lowercase									=	reduce(x * mask				,   """b c d h w -> b c h w"""				,   """sum""" )
 return (dec / 255).clamp(0.0				,   1.0 )


def lowerCAmelCase_      (				self	: str				,   UpperCamelCase__	: List[str]				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any = 0.0				,   UpperCamelCase__	: Dict = True				,   UpperCamelCase__	: Optional[Any]=None				,   UpperCamelCase__	: Any = True				,   ):






 """simple docstring"""







 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)
 __lowercase									=	timestep - self.config.num_train_timesteps // self.num_inference_steps

 # 2. compute alphas, betas
 __lowercase									=	self.alphas_cumprod[timestep]
 __lowercase									=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

 __lowercase									=	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
 __lowercase									=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

 # 4. Clip "predicted x_0"
 __lowercase									=	self.bit_scale
 if self.config.clip_sample:
  __lowercase									=	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)
 __lowercase									=	self._get_variance(__UpperCAmelCase				,   __UpperCAmelCase )
 __lowercase									=	eta * variance ** 0.5

 if use_clipped_model_output:
  # the model_output is always re-derived from the clipped x_0 in Glide
  __lowercase									=	(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
 __lowercase									=	(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
 __lowercase									=	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
  __lowercase									=	model_output.device if torch.is_tensor(__UpperCAmelCase ) else """cpu"""
  __lowercase									=	torch.randn(model_output.shape				,   dtype=model_output.dtype				,   generator=__UpperCAmelCase ).to(__UpperCAmelCase )
  __lowercase									=	self._get_variance(__UpperCAmelCase				,   __UpperCAmelCase ) ** 0.5 * eta * noise

  __lowercase									=	prev_sample + variance

 if not return_dict:
  return (prev_sample,)

 return DDIMSchedulerOutput(prev_sample=__UpperCAmelCase				,   pred_original_sample=__UpperCAmelCase )


def lowerCAmelCase_      (				self	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: Any				,   UpperCamelCase__	: str="epsilon"				,   UpperCamelCase__	: Tuple=None				,   UpperCamelCase__	: List[Any] = True				,   ):






 """simple docstring"""







 __lowercase									=	timestep

 if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
  __lowercase							,		__lowercase									=	torch.split(__UpperCAmelCase				,   sample.shape[1]				,   dim=1 )
 else:
  __lowercase									=	None

 # 1. compute alphas, betas
 __lowercase									=	self.alphas_cumprod[t]
 __lowercase									=	self.alphas_cumprod[t - 1] if t > 0 else self.one
 __lowercase									=	1 - alpha_prod_t
 __lowercase									=	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":
  __lowercase									=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
 elif prediction_type == "sample":
  __lowercase									=	model_output
 else:
  raise ValueError(f'''Unsupported prediction_type {prediction_type}.''' )

 # 3. Clip "predicted x_0"
 __lowercase									=	self.bit_scale
 if self.config.clip_sample:
  __lowercase									=	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
 __lowercase									=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
 __lowercase									=	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
 __lowercase									=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

 # 6. Add noise
 __lowercase									=	0
 if t > 0:
  __lowercase									=	torch.randn(
      model_output.size()				,   dtype=model_output.dtype				,   layout=model_output.layout				,   generator=__UpperCAmelCase ).to(model_output.device )
  __lowercase									=	(self._get_variance(__UpperCAmelCase				,   predicted_variance=__UpperCAmelCase ) ** 0.5) * noise

 __lowercase									=	pred_prev_sample + variance

 if not return_dict:
  return (pred_prev_sample,)

 return DDPMSchedulerOutput(prev_sample=__UpperCAmelCase				,   pred_original_sample=__UpperCAmelCase )





class    lowerCamelCase__						(		_snake_case    ):
      def __init__(		self	:	Optional[Any]      ,       A_	:	UNetaDConditionModel      ,       A_	:	Union[DDIMScheduler, DDPMScheduler]      ,       A_	:	Optional[float] = 1.0      ,       ):






       '''simple docstring'''


       super().__init__()
       __lowercase									=	bit_scale
       __lowercase									=	(
           ddim_bit_scheduler_step if isinstance(A_      ,       A_							) else ddpm_bit_scheduler_step
       )

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






      @torch.no_grad()
      def __call__(		self	:	Optional[Any]      ,       A_	:	Optional[int] = 2_5_6      ,       A_	:	Optional[int] = 2_5_6      ,       A_	:	Optional[int] = 5_0      ,       A_	:	Optional[torch.Generator] = None      ,       A_	:	Optional[int] = 1      ,       A_	:	Optional[str] = "pil"      ,       A_	:	bool = True      ,       **A_	:	List[Any]      ,       ):






       '''simple docstring'''


       __lowercase									=	torch.randn(
           (batch_size, self.unet.config.in_channels, height, width)      ,       generator=A_      ,       )
       __lowercase									=	decimal_to_bits(A_							) * self.bit_scale
       __lowercase									=	latents.to(self.device							)

       self.scheduler.set_timesteps(A_							)

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

        # compute the previous noisy sample x_t -> x_t-1
        __lowercase									=	self.scheduler.step(A_      ,       A_      ,       A_							).prev_sample

       __lowercase									=	bits_to_decimal(A_							)

       if output_type == "pil":
        __lowercase									=	self.numpy_to_pil(A_							)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=A_							)




"""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


UpperCAmelCase__  							=logging.get_logger(__name__)

UpperCAmelCase__  							={
    "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    lowerCamelCase__						(		_a    ):
      a :     Optional[int]											=		"""segformer"""
      def __init__(		self	:	str      ,       A_	:	Optional[int]=3      ,       A_	:	Any=4      ,       A_	:	Optional[int]=[2, 2, 2, 2]      ,       A_	:	str=[8, 4, 2, 1]      ,       A_	:	Any=[3_2, 6_4, 1_6_0, 2_5_6]      ,       A_	:	Union[str, Any]=[7, 3, 3, 3]      ,       A_	:	Any=[4, 2, 2, 2]      ,       A_	:	int=[1, 2, 5, 8]      ,       A_	:	Union[str, Any]=[4, 4, 4, 4]      ,       A_	:	Optional[Any]="gelu"      ,       A_	:	List[str]=0.0      ,       A_	:	Any=0.0      ,       A_	:	Optional[Any]=0.1      ,       A_	:	List[str]=0.02      ,       A_	:	Tuple=0.1      ,       A_	:	Optional[Any]=1e-6      ,       A_	:	Optional[int]=2_5_6      ,       A_	:	Optional[Any]=2_5_5      ,       **A_	:	Union[str, Any]      ,       ):






       '''simple docstring'''


       super().__init__(**A_							)

       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."""      ,       A_      ,       )

       __lowercase									=	num_channels
       __lowercase									=	num_encoder_blocks
       __lowercase									=	depths
       __lowercase									=	sr_ratios
       __lowercase									=	hidden_sizes
       __lowercase									=	patch_sizes
       __lowercase									=	strides
       __lowercase									=	mlp_ratios
       __lowercase									=	num_attention_heads
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	classifier_dropout_prob
       __lowercase									=	initializer_range
       __lowercase									=	drop_path_rate
       __lowercase									=	layer_norm_eps
       __lowercase									=	decoder_hidden_size
       __lowercase									=	kwargs.get("""reshape_last_stage"""      ,       A_							)
       __lowercase									=	semantic_loss_ignore_index






class    lowerCamelCase__						(		_a    ):
      a :     List[str]											=		version.parse("""1.11"""    )
      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


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






       '''simple docstring'''


       return 1e-4






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       return 1_2
