
import math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class     __A(				a    ):
				snake_case_		     =					42
				snake_case_		     =					None



def 					__lowerCAmelCase    (    a__		,					a__=0.999		,					a__="cosine"		,					) ->						str:






				if alpha_transform_type == "cosine":

								def alpha_bar_fn(a__						):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(a__						):
												return math.exp(t * -12.0						)

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""						)

				__a    							=					[]
				for i in range(_UpperCAmelCase						):
								__a    							=					i / num_diffusion_timesteps
								__a    							=					(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase						) / alpha_bar_fn(_UpperCAmelCase						)		,					_UpperCAmelCase						)						)
				return torch.tensor(_UpperCAmelCase		,					dtype=torch.floataa						)







class     __A(				a						,     a    ):
				snake_case_		     =					[e.name for e in KarrasDiffusionSchedulers]
				snake_case_		     =					1




				@register_to_config
				def __init__(  self	,  _snake_case = 1_000	,  _snake_case = 0.0001	,  _snake_case = 0.02	,  _snake_case = "linear"	,  _snake_case = None	,  _snake_case = False	,  _snake_case = "epsilon"	,  _snake_case = "linspace"	,  _snake_case = 0	,  )					->   List[Any]:


								'''simple docstring'''




								if trained_betas is not None:
												__a    							=					torch.tensor(UpperCAmelCase_	,  dtype=torch.floataa       )
								elif beta_schedule == "linear":
												__a    							=					torch.linspace(UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_	,  dtype=torch.floataa       )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												__a    							=					(
												    torch.linspace(beta_start**0.5	,  beta_end**0.5	,  UpperCAmelCase_	,  dtype=torch.floataa       ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												__a    							=					betas_for_alpha_bar(UpperCAmelCase_       )
								else:
												raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

								__a    							=					1.0 - self.betas
								__a    							=					torch.cumprod(self.alphas	,  dim=0       )

								__a    							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
								__a    							=					np.concatenate([sigmas[::-1], [0.0]]       ).astype(np.floataa       )
								__a    							=					torch.from_numpy(UpperCAmelCase_       )

								# setable values
								__a    							=					None
								__a    							=					use_karras_sigmas
								self.set_timesteps(UpperCAmelCase_	,  UpperCAmelCase_       )
								__a    							=					[]
								__a    							=					False




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   List[Any]:


								'''simple docstring'''




								if isinstance(UpperCAmelCase_	,  torch.Tensor       ):
												__a    							=					timestep.to(self.timesteps.device       )
								__a    							=					(self.timesteps == timestep).nonzero().item()
								__a    							=					self.sigmas[step_index]
								__a    							=					sample / ((sigma**2 + 1) ** 0.5)
								__a    							=					True
								return sample




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case       )					->   int:


								'''simple docstring'''




								def lms_derivative(_snake_case       ):
												__a    							=					1.0
												for k in range(UpperCAmelCase_       ):
																if current_order == k:
																				continue
																prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
												return prod

								__a    							=					integrate.quad(UpperCAmelCase_	,  self.sigmas[t]	,  self.sigmas[t + 1]	,  epsrel=1E-4       )[0]

								return integrated_coeff




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case = None       )					->   List[str]:


								'''simple docstring'''




								__a    							=					num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												__a    							=					np.linspace(0	,  self.config.num_train_timesteps - 1	,  UpperCAmelCase_	,  dtype=UpperCAmelCase_       )[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												__a    							=					self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												__a    							=					(np.arange(0	,  UpperCAmelCase_       ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_       )
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												__a    							=					self.config.num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												__a    							=					(np.arange(self.config.num_train_timesteps	,  0	,  -step_ratio       )).round().copy().astype(UpperCAmelCase_       )
												timesteps -= 1
								else:
												raise ValueError(
												    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""       )

								__a    							=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5       )
								__a    							=					np.log(UpperCAmelCase_       )
								__a    							=					np.interp(UpperCAmelCase_	,  np.arange(0	,  len(UpperCAmelCase_       )       )	,  UpperCAmelCase_       )

								if self.use_karras_sigmas:
												__a    							=					self._convert_to_karras(in_sigmas=UpperCAmelCase_       )
												__a    							=					np.array([self._sigma_to_t(UpperCAmelCase_	,  UpperCAmelCase_       ) for sigma in sigmas]       )

								__a    							=					np.concatenate([sigmas, [0.0]]       ).astype(np.floataa       )

								__a    							=					torch.from_numpy(UpperCAmelCase_       ).to(device=UpperCAmelCase_       )
								if str(UpperCAmelCase_       ).startswith('''mps'''       ):
												# mps does not support float64
												__a    							=					torch.from_numpy(UpperCAmelCase_       ).to(UpperCAmelCase_	,  dtype=torch.floataa       )
								else:
												__a    							=					torch.from_numpy(UpperCAmelCase_       ).to(device=UpperCAmelCase_       )

								__a    							=					[]




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case       )					->   Tuple:


								'''simple docstring'''




								__a    							=					np.log(UpperCAmelCase_       )

								# get distribution
								__a    							=					log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								__a    							=					np.cumsum((dists >= 0)	,  axis=0       ).argmax(axis=0       ).clip(max=log_sigmas.shape[0] - 2       )
								__a    							=					low_idx + 1

								__a    							=					log_sigmas[low_idx]
								__a    							=					log_sigmas[high_idx]

								# interpolate sigmas
								__a    							=					(low - log_sigma) / (low - high)
								__a    							=					np.clip(UpperCAmelCase_	,  0	,  1       )

								# transform interpolation to time range
								__a    							=					(1 - w) * low_idx + w * high_idx
								__a    							=					t.reshape(sigma.shape       )
								return t




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   int:


								'''simple docstring'''




								__a    							=					in_sigmas[-1].item()
								__a    							=					in_sigmas[0].item()

								__a    							=					7.0  # 7.0 is the value used in the paper
								__a    							=					np.linspace(0	,  1	,  self.num_inference_steps       )
								__a    							=					sigma_min ** (1 / rho)
								__a    							=					sigma_max ** (1 / rho)
								__a    							=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case = 4	,  _snake_case = True	,  )					->   Tuple:


								'''simple docstring'''




								if not self.is_scale_input_called:
												warnings.warn(
												    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
												    '''See `StableDiffusionPipeline` for a usage example.'''       )

								if isinstance(UpperCAmelCase_	,  torch.Tensor       ):
												__a    							=					timestep.to(self.timesteps.device       )
								__a    							=					(self.timesteps == timestep).nonzero().item()
								__a    							=					self.sigmas[step_index]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												__a    							=					sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												__a    							=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								elif self.config.prediction_type == "sample":
												__a    							=					model_output
								else:
												raise ValueError(
												    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""       )

								# 2. Convert to an ODE derivative
								__a    							=					(sample - pred_original_sample) / sigma
								self.derivatives.append(UpperCAmelCase_       )
								if len(self.derivatives       ) > order:
												self.derivatives.pop(0       )

								# 3. Compute linear multistep coefficients
								__a    							=					min(step_index + 1	,  UpperCAmelCase_       )
								__a    							=					[self.get_lms_coefficient(UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_       ) for curr_order in range(UpperCAmelCase_       )]

								# 4. Compute previous sample based on the derivatives path
								__a    							=					sample + sum(
								    coeff * derivative for coeff, derivative in zip(UpperCAmelCase_	,  reversed(self.derivatives       )       )       )

								if not return_dict:
												return (prev_sample,)

								return LMSDiscreteSchedulerOutput(prev_sample=UpperCAmelCase_	,  pred_original_sample=UpperCAmelCase_       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  )					->   int:


								'''simple docstring'''




								__a    							=					self.sigmas.to(device=original_samples.device	,  dtype=original_samples.dtype       )
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_       ):
												# mps does not support float64
												__a    							=					self.timesteps.to(original_samples.device	,  dtype=torch.floataa       )
												__a    							=					timesteps.to(original_samples.device	,  dtype=torch.floataa       )
								else:
												__a    							=					self.timesteps.to(original_samples.device       )
												__a    							=					timesteps.to(original_samples.device       )

								__a    							=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								__a    							=					sigmas[step_indices].flatten()
								while len(sigma.shape       ) < len(original_samples.shape       ):
												__a    							=					sigma.unsqueeze(-1       )

								__a    							=					original_samples + noise * sigma
								return noisy_samples




				def __len__(  self       )					->   Tuple:


								'''simple docstring'''




								return self.config.num_train_timesteps

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


A  :			int     				=				logging.get_logger(__name__)

A  :			Dict     				=				{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
					import detectrona







class     __A(				a    ):
				snake_case_		     =					'''layoutlmv2'''




				def __init__(  self	,  _snake_case=30_522	,  _snake_case=768	,  _snake_case=12	,  _snake_case=12	,  _snake_case=3_072	,  _snake_case="gelu"	,  _snake_case=0.1	,  _snake_case=0.1	,  _snake_case=512	,  _snake_case=2	,  _snake_case=0.02	,  _snake_case=1E-12	,  _snake_case=0	,  _snake_case=1_024	,  _snake_case=128	,  _snake_case=32	,  _snake_case=True	,  _snake_case=256	,  _snake_case=64	,  _snake_case=True	,  _snake_case=[7, 7, 256]	,  _snake_case=128	,  _snake_case=128	,  _snake_case=True	,  _snake_case=True	,  _snake_case=False	,  _snake_case=None	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




								super().__init__(
								    vocab_size=_snake_case	,  hidden_size=_snake_case	,  num_hidden_layers=_snake_case	,  num_attention_heads=_snake_case	,  intermediate_size=_snake_case	,  hidden_act=_snake_case	,  hidden_dropout_prob=_snake_case	,  attention_probs_dropout_prob=_snake_case	,  max_position_embeddings=_snake_case	,  type_vocab_size=_snake_case	,  initializer_range=_snake_case	,  layer_norm_eps=_snake_case	,  pad_token_id=_snake_case	,  **_snake_case	,  )
								__a    							=					max_ad_position_embeddings
								__a    							=					max_rel_pos
								__a    							=					rel_pos_bins
								__a    							=					fast_qkv
								__a    							=					max_rel_ad_pos
								__a    							=					rel_ad_pos_bins
								__a    							=					convert_sync_batchnorm
								__a    							=					image_feature_pool_shape
								__a    							=					coordinate_size
								__a    							=					shape_size
								__a    							=					has_relative_attention_bias
								__a    							=					has_spatial_attention_bias
								__a    							=					has_visual_segment_embedding
								__a    							=					(
								    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
								)




				@classmethod
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Dict:


								'''simple docstring'''




								return {
								    "MODEL.MASK_ON": True,
								    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
								    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
								    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
								    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
								    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
								    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
								    "MODEL.POST_NMS_TOPK_TEST": 1_000,
								    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
								    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
								    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
								    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
								    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
								    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
								    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
								    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
								    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
								    "MODEL.RESNETS.DEPTH": 101,
								    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
								    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
								    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
								    "MODEL.RESNETS.NUM_GROUPS": 32,
								    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
								    "MODEL.RESNETS.STRIDE_IN_1X1": False,
								}




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


								'''simple docstring'''




								__a    							=					detectrona.config.get_cfg()
								for k, v in self.detectrona_config_args.items():
												__a    							=					k.split('''.'''       )
												__a    							=					detectrona_config
												for attribute in attributes[:-1]:
																__a    							=					getattr(_snake_case	,  _snake_case       )
												setattr(_snake_case	,  attributes[-1]	,  _snake_case       )

								return detectrona_config