


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			:  Dict     =logging.get_logger(__name__)

_UpperCAmelCase			:  Optional[int]     ={
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Any					=							"""layoutlmv3"""




						def __init__(				self   ,						__lowercase=5_0_2_6_5   ,						__lowercase=7_6_8   ,						__lowercase=1_2   ,						__lowercase=1_2   ,						__lowercase=3_0_7_2   ,						__lowercase="gelu"   ,						__lowercase=0.1   ,						__lowercase=0.1   ,						__lowercase=5_1_2   ,						__lowercase=2   ,						__lowercase=0.02   ,						__lowercase=1e-5   ,						__lowercase=1   ,						__lowercase=0   ,						__lowercase=2   ,						__lowercase=1_0_2_4   ,						__lowercase=1_2_8   ,						__lowercase=1_2_8   ,						__lowercase=True   ,						__lowercase=3_2   ,						__lowercase=1_2_8   ,						__lowercase=6_4   ,						__lowercase=2_5_6   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=2_2_4   ,						__lowercase=3   ,						__lowercase=1_6   ,						__lowercase=None   ,						**__lowercase   ,						)					->    Dict:
										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_ :      Tuple    				=				max_ad_position_embeddings
										lowerCAmelCase_ :      List[str]    				=				coordinate_size
										lowerCAmelCase_ :      Optional[Any]    				=				shape_size
										lowerCAmelCase_ :      Optional[Any]    				=				has_relative_attention_bias
										lowerCAmelCase_ :      List[str]    				=				rel_pos_bins
										lowerCAmelCase_ :      str    				=				max_rel_pos
										lowerCAmelCase_ :      Optional[Any]    				=				has_spatial_attention_bias
										lowerCAmelCase_ :      Optional[int]    				=				rel_ad_pos_bins
										lowerCAmelCase_ :      List[str]    				=				max_rel_ad_pos
										lowerCAmelCase_ :      List[Any]    				=				text_embed
										lowerCAmelCase_ :      Tuple    				=				visual_embed
										lowerCAmelCase_ :      List[str]    				=				input_size
										lowerCAmelCase_ :      int    				=				num_channels
										lowerCAmelCase_ :      Optional[int]    				=				patch_size
										lowerCAmelCase_ :      Any    				=				classifier_dropout







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							version.parse("""1.12"""							)




						@property
						def 						lowercase_ (				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 						lowercase_ (				self							)					->    float:
										return 1e-5




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




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = -1   ,						__lowercase = -1   ,						__lowercase = False   ,						__lowercase = None   ,						__lowercase = 3   ,						__lowercase = 4_0   ,						__lowercase = 4_0   ,						)					->    Mapping[str, Any]:
										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_ :      Optional[Any]    				=				processor.tokenizer.num_special_tokens_to_add(__lowercase							)
										lowerCAmelCase_ :      Union[str, 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_ :      Any    				=				[[''' '''.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										lowerCAmelCase_ :      List[Any]    				=				[[[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)
										lowerCAmelCase_ :      Optional[Any]    				=				self._generate_dummy_images(__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)

										lowerCAmelCase_ :      str    				=				dict(
										    processor(
										        __lowercase   ,						text=__lowercase   ,						boxes=__lowercase   ,						return_tensors=__lowercase   ,						)							)

										return inputs








# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    Any:
										if not self.step_with_optimizer:
														# No link between scheduler and optimizer -> just step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
														return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
														if self.gradient_state.adjust_scheduler:
																		self.scheduler._step_count += 1
														return

										for opt in self.optimizers:
														if opt.step_was_skipped:
																		return
										if self.split_batches:
														# Split batches -> the training dataloader batch size is not changed so one step per training step
														self.scheduler.step(*__lowercase   ,						**__lowercase							)
										else:
														# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
														# num_processes steps per training step
														lowerCAmelCase_ :      Optional[Any]    				=				AcceleratorState().num_processes
														for _ in range(__lowercase							):
																		# Special case when using OneCycle and `drop_last` was not used
																		if hasattr(self.scheduler   ,						'''total_steps'''							):
																						if self.scheduler._step_count <= self.scheduler.total_steps:
																										self.scheduler.step(*__lowercase   ,						**__lowercase							)
																		else:
																						self.scheduler.step(*__lowercase   ,						**__lowercase							)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)