




"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     42
			lowerCamelCase__ =     None







def 	_snake_case     (							lowercase__				,       lowercase__=0.9_9_9				,       lowercase__="cosine"				,       ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowercase__						):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowercase__						):
												return math.exp(t * -1_2.0						)

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

				_lowerCamelCase	:     Optional[Any]       =       []
				for i in range(lowercase__						):
								_lowerCamelCase	:     Optional[Any]       =       i / num_diffusion_timesteps
								_lowerCamelCase	:     List[str]       =       (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowercase__						) / alpha_bar_fn(lowercase__						)				,       lowercase__						)						)
				return torch.tensor(lowercase__				,       dtype=torch.floataa						)






class lowerCAmelCase__      (	lowercase,      lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     1
			@register_to_config
			def __init__(    self    , lowercase = 1000    , lowercase = 0.00_01    , lowercase = 0.02    , lowercase = "linear"    , lowercase = None    , lowercase = True    , lowercase = True    , lowercase = 0    , lowercase = "epsilon"    , lowercase = 1.0    , **lowercase    , ):
							if kwargs.get('set_alpha_to_one'    , lowercase    ) is not None:
											_lowerCamelCase	:     List[str]       =       (
											    'The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'
											)
											deprecate('set_alpha_to_one'    , '1.0.0'    , lowercase    , standard_warn=lowercase    )
											_lowerCamelCase	:     Union[str, Any]       =       kwargs['set_alpha_to_one']
							if trained_betas is not None:
											_lowerCamelCase	:     Tuple       =       torch.tensor(lowercase    , dtype=torch.floataa    )
							elif beta_schedule == "linear":
											_lowerCamelCase	:     List[str]       =       torch.linspace(lowercase    , lowercase    , lowercase    , dtype=torch.floataa    )
							elif beta_schedule == "scaled_linear":
											# this schedule is very specific to the latent diffusion model.
											_lowerCamelCase	:     Any       =       (
											    torch.linspace(beta_start**0.5    , beta_end**0.5    , lowercase    , dtype=torch.floataa    ) ** 2
											)
							elif beta_schedule == "squaredcos_cap_v2":
											# Glide cosine schedule
											_lowerCamelCase	:     Tuple       =       betas_for_alpha_bar(lowercase    )
							else:
											raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''    )

							_lowerCamelCase	:     str       =       1.0 - self.betas
							_lowerCamelCase	:     str       =       torch.cumprod(self.alphas    , dim=0    )

							# At every step in inverted ddim, we are looking into the next alphas_cumprod
							# For the final step, there is no next alphas_cumprod, and the index is out of bounds
							# `set_alpha_to_zero` decides whether we set this parameter simply to zero
							# in this case, self.step() just output the predicted noise
							# or whether we use the final alpha of the "non-previous" one.
							_lowerCamelCase	:     int       =       torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

							# standard deviation of the initial noise distribution
							_lowerCamelCase	:     Optional[Any]       =       1.0

							# setable values
							_lowerCamelCase	:     Dict       =       None
							_lowerCamelCase	:     int       =       torch.from_numpy(np.arange(0    , lowercase    ).copy().astype(np.intaa    )    )
			def    A_       (    self    , lowercase    , lowercase = None    ):
							return sample
			def    A_       (    self    , lowercase    , lowercase = None    ):

							if num_inference_steps > self.config.num_train_timesteps:
											raise ValueError(
											    F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
											    F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
											    F''' maximal {self.config.num_train_timesteps} timesteps.'''    )

							_lowerCamelCase	:     int       =       num_inference_steps
							_lowerCamelCase	:     Optional[int]       =       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
							_lowerCamelCase	:     Any       =       (np.arange(0    , lowercase    ) * step_ratio).round().copy().astype(np.intaa    )
							_lowerCamelCase	:     Any       =       torch.from_numpy(lowercase    ).to(lowercase    )
							self.timesteps += self.config.steps_offset
			def    A_       (    self    , lowercase    , lowercase    , lowercase    , lowercase = 0.0    , lowercase = False    , lowercase = None    , lowercase = True    , ):
							# 1. get previous step value (=t+1)
							_lowerCamelCase	:     int       =       timestep + self.config.num_train_timesteps // self.num_inference_steps

							# 2. compute alphas, betas
							# change original implementation to exactly match noise levels for analogous forward process
							_lowerCamelCase	:     Dict       =       self.alphas_cumprod[timestep]
							_lowerCamelCase	:     str       =       (
							    self.alphas_cumprod[prev_timestep]
							    if prev_timestep < self.config.num_train_timesteps
							    else self.final_alpha_cumprod
							)

							_lowerCamelCase	:     Any       =       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
							if self.config.prediction_type == "epsilon":
											_lowerCamelCase	:     Optional[int]       =       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
											_lowerCamelCase	:     Any       =       model_output
							elif self.config.prediction_type == "sample":
											_lowerCamelCase	:     Optional[Any]       =       model_output
											_lowerCamelCase	:     Tuple       =       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
							elif self.config.prediction_type == "v_prediction":
											_lowerCamelCase	:     Any       =       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
											_lowerCamelCase	:     Tuple       =       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
							else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
											    ' `v_prediction`'    )

							# 4. Clip or threshold "predicted x_0"
							if self.config.clip_sample:
											_lowerCamelCase	:     Tuple       =       pred_original_sample.clamp(
											    -self.config.clip_sample_range    , self.config.clip_sample_range    )

							# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_lowerCamelCase	:     Optional[int]       =       (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

							# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_lowerCamelCase	:     Dict       =       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

							if not return_dict:
											return (prev_sample, pred_original_sample)
							return DDIMSchedulerOutput(prev_sample=lowercase    , pred_original_sample=lowercase    )






			def __len__(    self    ):
							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


lowercase__			=		logging.get_logger(__name__)

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






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     """layoutlmv3"""
			def __init__(    self    , lowercase=50265    , lowercase=768    , lowercase=12    , lowercase=12    , lowercase=3072    , lowercase="gelu"    , lowercase=0.1    , lowercase=0.1    , lowercase=512    , lowercase=2    , lowercase=0.02    , lowercase=1E-5    , lowercase=1    , lowercase=0    , lowercase=2    , lowercase=1024    , lowercase=128    , lowercase=128    , lowercase=True    , lowercase=32    , lowercase=128    , lowercase=64    , lowercase=256    , lowercase=True    , lowercase=True    , lowercase=True    , lowercase=224    , lowercase=3    , lowercase=16    , lowercase=None    , **lowercase    , ):
							super().__init__(
							    vocab_size=lowercase    , hidden_size=lowercase    , num_hidden_layers=lowercase    , num_attention_heads=lowercase    , intermediate_size=lowercase    , hidden_act=lowercase    , hidden_dropout_prob=lowercase    , attention_probs_dropout_prob=lowercase    , max_position_embeddings=lowercase    , type_vocab_size=lowercase    , initializer_range=lowercase    , layer_norm_eps=lowercase    , pad_token_id=lowercase    , bos_token_id=lowercase    , eos_token_id=lowercase    , **lowercase    , )
							_lowerCamelCase	:     List[str]       =       max_ad_position_embeddings
							_lowerCamelCase	:     Optional[Any]       =       coordinate_size
							_lowerCamelCase	:     int       =       shape_size
							_lowerCamelCase	:     Optional[Any]       =       has_relative_attention_bias
							_lowerCamelCase	:     Dict       =       rel_pos_bins
							_lowerCamelCase	:     Tuple       =       max_rel_pos
							_lowerCamelCase	:     int       =       has_spatial_attention_bias
							_lowerCamelCase	:     Optional[int]       =       rel_ad_pos_bins
							_lowerCamelCase	:     List[Any]       =       max_rel_ad_pos
							_lowerCamelCase	:     Any       =       text_embed
							_lowerCamelCase	:     List[Any]       =       visual_embed
							_lowerCamelCase	:     str       =       input_size
							_lowerCamelCase	:     Optional[Any]       =       num_channels
							_lowerCamelCase	:     Tuple       =       patch_size
							_lowerCamelCase	:     Optional[int]       =       classifier_dropout






class lowerCAmelCase__      (	lowercase		):






			'''simple docstring'''



			lowerCamelCase__ =     version.parse("""1.12"""		)
			@property
			def    A_       (    self    ):
							# The order of inputs is different for question answering and sequence classification
							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    A_       (    self    ):
							return 1E-5
			@property
			def    A_       (    self    ):
							return 12






			def    A_       (    self    , lowercase    , lowercase = -1    , lowercase = -1    , lowercase = False    , lowercase = None    , lowercase = 3    , lowercase = 40    , lowercase = 40    , ):
							setattr(processor.image_processor    , 'apply_ocr'    , lowercase    )

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

							# Generate dummy bounding boxes
							_lowerCamelCase	:     List[str]       =       [[[48, 84, 73, 128]]] * 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)
							_lowerCamelCase	:     Tuple       =       self._generate_dummy_images(lowercase    , lowercase    , lowercase    , lowercase    )

							_lowerCamelCase	:     str       =       dict(
							    processor(
							        lowercase    , text=lowercase    , boxes=lowercase    , return_tensors=lowercase    , )    )

							return inputs