"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[Any] ,			_UpperCamelCase       :				List[Any]=0.9_99 ,			_UpperCamelCase       :				Tuple="cosine" ,			)					->				Any:





						"""simple docstring"""






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(_UpperCamelCase       :				Any					):
																		return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(_UpperCamelCase       :				Optional[int]					):
																		return math.exp(t * -12.0					)

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

						snake_case								=			[]
						for i in range(_UpperCamelCase					):
												snake_case								=			i / num_diffusion_timesteps
												snake_case								=			(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   lowerCAmelCase_    (			lowerCAmelCase     ,    lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						[e.name for e in KarrasDiffusionSchedulers]
				_lowerCAmelCase     :    Dict			  =						2

				@register_to_config
				def __init__(				self		,    lowerCAmelCase = 10_00		,    lowerCAmelCase = 0.0_00_85		,    lowerCAmelCase = 0.0_12		,    lowerCAmelCase = "linear"		,    lowerCAmelCase = None		,    lowerCAmelCase = "epsilon"		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = 1.0		,    lowerCAmelCase = "linspace"		,    lowerCAmelCase = 0		,    ):






										"""simple docstring"""

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

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

										#  set all values
										self.set_timesteps(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )
										snake_case								=			use_karras_sigmas

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase=None    ):






										"""simple docstring"""

										if schedule_timesteps is None:
																snake_case								=			self.timesteps

										snake_case								=			(schedule_timesteps == timestep).nonzero()

										# The sigma index that is taken for the **very** first `step`
										# is always the second index (or the last index if there is only 1)
										# This way we can ensure we don't accidentally skip a sigma in
										# case we start in the middle of the denoising schedule (e.g. for image-to-image)
										if len(self._index_counter    ) == 0:
																snake_case								=			1 if len(lowerCAmelCase    ) > 1 else 0
										else:
																snake_case								=			timestep.cpu().item() if torch.is_tensor(lowerCAmelCase    ) else timestep
																snake_case								=			self._index_counter[timestep_int]

										return indices[pos].item()

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

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

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			self.index_for_timestep(lowerCAmelCase    )

										snake_case								=			self.sigmas[step_index]
										snake_case								=			sample / ((sigma**2 + 1) ** 0.5)
										return sample

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    ):






										"""simple docstring"""

										snake_case								=			num_inference_steps

										snake_case								=			num_train_timesteps or self.config.num_train_timesteps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
																snake_case								=			np.linspace(0		,    num_train_timesteps - 1		,    lowerCAmelCase		,    dtype=lowerCAmelCase    )[::-1].copy()
										elif self.config.timestep_spacing == "leading":
																snake_case								=			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
																snake_case								=			(np.arange(0		,    lowerCAmelCase    ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase    )
																timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
																snake_case								=			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
																snake_case								=			(np.arange(lowerCAmelCase		,    0		,    -step_ratio    )).round().copy().astype(lowerCAmelCase    )
																timesteps -= 1
										else:
																raise ValueError(
																    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

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

										if self.config.use_karras_sigmas:
																snake_case								=			self._convert_to_karras(in_sigmas=lowerCAmelCase		,    num_inference_steps=self.num_inference_steps    )
																snake_case								=			np.array([self._sigma_to_t(lowerCAmelCase		,    lowerCAmelCase    ) for sigma in sigmas]    )

										snake_case								=			np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
										snake_case								=			torch.from_numpy(lowerCAmelCase    ).to(device=lowerCAmelCase    )
										snake_case								=			torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

										snake_case								=			torch.from_numpy(lowerCAmelCase    )
										snake_case								=			torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

										if str(lowerCAmelCase    ).startswith('mps'    ):
																# mps does not support float64
																snake_case								=			timesteps.to(lowerCAmelCase		,    dtype=torch.floataa    )
										else:
																snake_case								=			timesteps.to(device=lowerCAmelCase    )

										# empty dt and derivative
										snake_case								=			None
										snake_case								=			None

										# for exp beta schedules, such as the one for `pipeline_shap_e.py`
										# we need an index counter
										snake_case								=			defaultdict(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			np.log(lowerCAmelCase    )

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

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

										snake_case								=			log_sigmas[low_idx]
										snake_case								=			log_sigmas[high_idx]

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

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

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

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

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.dt is None

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = True		,    ):






										"""simple docstring"""

										snake_case								=			self.index_for_timestep(lowerCAmelCase    )

										# advance index counter by 1
										snake_case								=			timestep.cpu().item() if torch.is_tensor(lowerCAmelCase    ) else timestep
										self._index_counter[timestep_int] += 1

										if self.state_in_first_order:
																snake_case								=			self.sigmas[step_index]
																snake_case								=			self.sigmas[step_index + 1]
										else:
																# 2nd order / Heun's method
																snake_case								=			self.sigmas[step_index - 1]
																snake_case								=			self.sigmas[step_index]

										# currently only gamma=0 is supported. This usually works best anyways.
										# We can support gamma in the future but then need to scale the timestep before
										# passing it to the model which requires a change in API
										snake_case								=			0
										snake_case								=			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

										if self.config.clip_sample:
																snake_case								=			pred_original_sample.clamp(
																    -self.config.clip_sample_range		,    self.config.clip_sample_range    )

										if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																snake_case								=			(sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																snake_case								=			sigma_next - sigma_hat

																# store for 2nd order step
																snake_case								=			derivative
																snake_case								=			dt
																snake_case								=			sample
										else:
																# 2. 2nd order / Heun's method
																snake_case								=			(sample - pred_original_sample) / sigma_next
																snake_case								=			(self.prev_derivative + derivative) / 2

																# 3. take prev timestep & sample
																snake_case								=			self.dt
																snake_case								=			self.sample

																# free dt and derivative
																# Note, this puts the scheduler in "first order mode"
																snake_case								=			None
																snake_case								=			None
																snake_case								=			None

										snake_case								=			sample + derivative * dt

										if not return_dict:
																return (prev_sample,)

										return SchedulerOutput(prev_sample=lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    ):






										"""simple docstring"""

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

										snake_case								=			[self.index_for_timestep(lowerCAmelCase		,    lowerCAmelCase    ) for t in timesteps]

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

										snake_case								=			original_samples + noise * sigma
										return noisy_samples

				def __len__(				self    ):






										"""simple docstring"""

										return self.config.num_train_timesteps



"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    vocab_size=lowerCAmelCase		,    hidden_size=lowerCAmelCase		,    num_hidden_layers=lowerCAmelCase		,    num_attention_heads=lowerCAmelCase		,    intermediate_size=lowerCAmelCase		,    hidden_act=lowerCAmelCase		,    hidden_dropout_prob=lowerCAmelCase		,    attention_probs_dropout_prob=lowerCAmelCase		,    max_position_embeddings=lowerCAmelCase		,    type_vocab_size=lowerCAmelCase		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.task in ["question-answering", "sequence-classification"]:
																return OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'sequence'}),
																        ('bbox', {0: 'batch', 1: 'sequence'}),
																        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
																    ]    )
										else:
																return OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'sequence'}),
																        ('bbox', {0: 'batch', 1: 'sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'sequence'}),
																        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
																    ]    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										setattr(processor.image_processor		,    'apply_ocr'		,    lowerCAmelCase    )

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_batch		,    num_token_to_add=0    )
										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										snake_case								=			processor.tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=lowerCAmelCase    )
										# Generate dummy inputs according to compute batch and sequence
										snake_case								=			[[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * batch_size

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


