






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
		import transformers




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					random.seed(__snake_case  )
					np.random.seed(__snake_case  )
					torch.manual_seed(__snake_case  )
					torch.cuda.manual_seed_all(__snake_case  )
					# ^^ safe to call this function even if cuda is not available






class   lowerCamelCase				:







		'''simple docstring'''



		def __init__(							self    ,		_UpperCamelCase    ,		_UpperCamelCase = 0.99_99    ,		_UpperCamelCase = 0.0    ,		_UpperCamelCase = 0    ,		_UpperCamelCase = False    ,		_UpperCamelCase = 1.0    ,		_UpperCamelCase = 2 / 3    ,		_UpperCamelCase = None    ,		_UpperCamelCase = None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:

							if isinstance(_UpperCamelCase    ,		torch.nn.Module       ):
												UpperCAmelCase_   :				Optional[Any]						=						(
												    'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
												    'Please pass the parameters of the module instead.'
												)
												deprecate(
												    'passing a `torch.nn.Module` to `ExponentialMovingAverage`'    ,		'1.0.0'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase    ,		)
												UpperCAmelCase_   :				Optional[Any]						=						parameters.parameters()

												# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
												UpperCAmelCase_   :				Any						=						True

							if kwargs.get('max_value'    ,		_UpperCamelCase       ) is not None:
												UpperCAmelCase_   :				Any						=						'The `max_value` argument is deprecated. Please use `decay` instead.'
												deprecate('max_value'    ,		'1.0.0'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase       )
												UpperCAmelCase_   :				List[str]						=						kwargs['max_value']

							if kwargs.get('min_value'    ,		_UpperCamelCase       ) is not None:
												UpperCAmelCase_   :				Dict						=						'The `min_value` argument is deprecated. Please use `min_decay` instead.'
												deprecate('min_value'    ,		'1.0.0'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase       )
												UpperCAmelCase_   :				List[str]						=						kwargs['min_value']

							UpperCAmelCase_   :				Tuple						=						list(_UpperCamelCase       )
							UpperCAmelCase_   :				str						=						[p.clone().detach() for p in parameters]

							if kwargs.get('device'    ,		_UpperCamelCase       ) is not None:
												UpperCAmelCase_   :				str						=						'The `device` argument is deprecated. Please use `to` instead.'
												deprecate('device'    ,		'1.0.0'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase       )
												self.to(device=kwargs['device']       )

							UpperCAmelCase_   :				Optional[Any]						=						None

							UpperCAmelCase_   :				Any						=						decay
							UpperCAmelCase_   :				Tuple						=						min_decay
							UpperCAmelCase_   :				Tuple						=						update_after_step
							UpperCAmelCase_   :				Any						=						use_ema_warmup
							UpperCAmelCase_   :				int						=						inv_gamma
							UpperCAmelCase_   :				Optional[Any]						=						power
							UpperCAmelCase_   :				List[Any]						=						0
							UpperCAmelCase_   :				Any						=						None  # set in `step()`

							UpperCAmelCase_   :				Tuple						=						model_cls
							UpperCAmelCase_   :				List[Any]						=						model_config



		@classmethod
		def  __UpperCAmelCase      (							cls    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							"EMAModel":
							UpperCAmelCase_	,  UpperCAmelCase_   :				Tuple						=						model_cls.load_config(_UpperCamelCase    ,		return_unused_kwargs=_UpperCamelCase       )
							UpperCAmelCase_   :				int						=						model_cls.from_pretrained(_UpperCamelCase       )

							UpperCAmelCase_   :				Any						=						cls(model.parameters()    ,		model_cls=_UpperCamelCase    ,		model_config=model.config       )

							ema_model.load_state_dict(_UpperCamelCase       )
							return ema_model



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							int:
							if self.model_cls is None:
												raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.'       )

							if self.model_config is None:
												raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.'       )

							UpperCAmelCase_   :				Union[str, Any]						=						self.model_cls.from_config(self.model_config       )
							UpperCAmelCase_   :				List[Any]						=						self.state_dict()
							state_dict.pop('shadow_params'    ,		_UpperCamelCase       )

							model.register_to_config(**_UpperCamelCase       )
							self.copy_to(model.parameters()       )
							model.save_pretrained(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							float:
							UpperCAmelCase_   :				Tuple						=						max(0    ,		optimization_step - self.update_after_step - 1       )

							if step <= 0:
												return 0.0

							if self.use_ema_warmup:
												UpperCAmelCase_   :				Union[str, Any]						=						1 - (1 + step / self.inv_gamma) ** -self.power
							else:
												UpperCAmelCase_   :				Optional[Any]						=						(1 + step) / (1_0 + step)

							UpperCAmelCase_   :				Tuple						=						min(_UpperCamelCase    ,		self.decay       )
							# make sure decay is not smaller than min_decay
							UpperCAmelCase_   :				Optional[Any]						=						max(_UpperCamelCase    ,		self.min_decay       )
							return cur_decay_value



		@torch.no_grad()
		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							List[Any]:
							if isinstance(_UpperCamelCase    ,		torch.nn.Module       ):
												UpperCAmelCase_   :				Dict						=						(
												    'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
												    'Please pass the parameters of the module instead.'
												)
												deprecate(
												    'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'    ,		'1.0.0'    ,		_UpperCamelCase    ,		standard_warn=_UpperCamelCase    ,		)
												UpperCAmelCase_   :				Any						=						parameters.parameters()

							UpperCAmelCase_   :				Optional[Any]						=						list(_UpperCamelCase       )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							UpperCAmelCase_   :				Dict						=						self.get_decay(self.optimization_step       )
							UpperCAmelCase_   :				str						=						decay
							UpperCAmelCase_   :				Any						=						1 - decay

							UpperCAmelCase_   :				Tuple						=						contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
												import deepspeed

							for s_param, param in zip(self.shadow_params    ,		_UpperCamelCase       ):
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																	UpperCAmelCase_   :				List[Any]						=						deepspeed.zero.GatheredParameters(_UpperCamelCase    ,		modifier_rank=_UpperCamelCase       )

												with context_manager():
																	if param.requires_grad:
																						s_param.sub_(one_minus_decay * (s_param - param)       )
																	else:
																						s_param.copy_(_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Dict						=						list(_UpperCamelCase       )
							for s_param, param in zip(self.shadow_params    ,		_UpperCamelCase       ):
												param.data.copy_(s_param.to(param.device       ).data       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None       )     ->							None:
							UpperCAmelCase_   :				int						=						[
							    p.to(device=_UpperCamelCase    ,		dtype=_UpperCamelCase       ) if p.is_floating_point() else p.to(device=_UpperCamelCase       )
							    for p in self.shadow_params
							]



		def  __UpperCAmelCase      (							self       )     ->							dict:
							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				Tuple						=						[param.detach().cpu().clone() for param in parameters]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							if self.temp_stored_params is None:
												raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`'       )
							for c_param, param in zip(self.temp_stored_params    ,		_UpperCamelCase       ):
												param.data.copy_(c_param.data       )

							# Better memory-wise.
							UpperCAmelCase_   :				List[str]						=						None



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase       )     ->							None:
							UpperCAmelCase_   :				List[Any]						=						copy.deepcopy(_UpperCamelCase       )

							UpperCAmelCase_   :				Optional[int]						=						state_dict.get('decay'    ,		self.decay       )
							if self.decay < 0.0 or self.decay > 1.0:
												raise ValueError('Decay must be between 0 and 1'       )

							UpperCAmelCase_   :				Tuple						=						state_dict.get('min_decay'    ,		self.min_decay       )
							if not isinstance(self.min_decay    ,		_UpperCamelCase       ):
												raise ValueError('Invalid min_decay'       )

							UpperCAmelCase_   :				Optional[int]						=						state_dict.get('optimization_step'    ,		self.optimization_step       )
							if not isinstance(self.optimization_step    ,		_UpperCamelCase       ):
												raise ValueError('Invalid optimization_step'       )

							UpperCAmelCase_   :				List[Any]						=						state_dict.get('update_after_step'    ,		self.update_after_step       )
							if not isinstance(self.update_after_step    ,		_UpperCamelCase       ):
												raise ValueError('Invalid update_after_step'       )

							UpperCAmelCase_   :				Union[str, Any]						=						state_dict.get('use_ema_warmup'    ,		self.use_ema_warmup       )
							if not isinstance(self.use_ema_warmup    ,		_UpperCamelCase       ):
												raise ValueError('Invalid use_ema_warmup'       )

							UpperCAmelCase_   :				List[str]						=						state_dict.get('inv_gamma'    ,		self.inv_gamma       )
							if not isinstance(self.inv_gamma    ,		(float, int)       ):
												raise ValueError('Invalid inv_gamma'       )

							UpperCAmelCase_   :				Optional[int]						=						state_dict.get('power'    ,		self.power       )
							if not isinstance(self.power    ,		(float, int)       ):
												raise ValueError('Invalid power'       )

							UpperCAmelCase_   :				Optional[int]						=						state_dict.get('shadow_params'    ,		_UpperCamelCase       )
							if shadow_params is not None:
												UpperCAmelCase_   :				Dict						=						shadow_params
												if not isinstance(self.shadow_params    ,		_UpperCamelCase       ):
																	raise ValueError('shadow_params must be a list'       )
												if not all(isinstance(_UpperCamelCase    ,		torch.Tensor       ) for p in self.shadow_params       ):
																	raise ValueError('shadow_params must all be Tensors'       )














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


__UpperCAmelCase     							=						logging.get_logger(__name__)

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



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  '''layoutlmv3'''



		def __init__(							self    ,		_UpperCamelCase=5_0_2_6_5    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=True    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(
							    vocab_size=_UpperCamelCase    ,		hidden_size=_UpperCamelCase    ,		num_hidden_layers=_UpperCamelCase    ,		num_attention_heads=_UpperCamelCase    ,		intermediate_size=_UpperCamelCase    ,		hidden_act=_UpperCamelCase    ,		hidden_dropout_prob=_UpperCamelCase    ,		attention_probs_dropout_prob=_UpperCamelCase    ,		max_position_embeddings=_UpperCamelCase    ,		type_vocab_size=_UpperCamelCase    ,		initializer_range=_UpperCamelCase    ,		layer_norm_eps=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		bos_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				str						=						max_ad_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						coordinate_size
							UpperCAmelCase_   :				Union[str, Any]						=						shape_size
							UpperCAmelCase_   :				str						=						has_relative_attention_bias
							UpperCAmelCase_   :				Tuple						=						rel_pos_bins
							UpperCAmelCase_   :				Dict						=						max_rel_pos
							UpperCAmelCase_   :				Any						=						has_spatial_attention_bias
							UpperCAmelCase_   :				Optional[Any]						=						rel_ad_pos_bins
							UpperCAmelCase_   :				List[str]						=						max_rel_ad_pos
							UpperCAmelCase_   :				List[str]						=						text_embed
							UpperCAmelCase_   :				Dict						=						visual_embed
							UpperCAmelCase_   :				Optional[int]						=						input_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						patch_size
							UpperCAmelCase_   :				Union[str, Any]						=						classifier_dropout





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  version.parse('''1.12'''      )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							# 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  __UpperCAmelCase      (							self       )     ->							float:
							return 1E-5



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_2



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3    ,		_UpperCamelCase = 4_0    ,		_UpperCamelCase = 4_0    ,		)     ->							Mapping[str, Any]:
							setattr(processor.image_processor    ,		'apply_ocr'    ,		_UpperCamelCase       )

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

							# Generate dummy bounding boxes
							UpperCAmelCase_   :				List[str]						=						[[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
							UpperCAmelCase_   :				str						=						self._generate_dummy_images(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						dict(
							    processor(
							        _UpperCamelCase    ,		text=_UpperCamelCase    ,		boxes=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		)       )

							return inputs






