






"""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


_UpperCamelCase       :					Any									=		logging.get_logger(__name__)

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






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  '''layoutlmv3'''





    def __init__(							self			:      Optional[Any]       ,	A			:      Optional[int]=5_0_2_6_5       ,	A			:      str=7_6_8       ,	A			:      int=1_2       ,	A			:      Union[str, Any]=1_2       ,	A			:      Tuple=3_0_7_2       ,	A			:      List[str]="gelu"       ,	A			:      Union[str, Any]=0.1       ,	A			:      Any=0.1       ,	A			:      Union[str, Any]=5_1_2       ,	A			:      Any=2       ,	A			:      List[str]=0.02       ,	A			:      Dict=1E-5       ,	A			:      Optional[int]=1       ,	A			:      Optional[int]=0       ,	A			:      Optional[int]=2       ,	A			:      str=1_0_2_4       ,	A			:      str=1_2_8       ,	A			:      Dict=1_2_8       ,	A			:      Union[str, Any]=True       ,	A			:      List[str]=3_2       ,	A			:      Union[str, Any]=1_2_8       ,	A			:      int=6_4       ,	A			:      Tuple=2_5_6       ,	A			:      Any=True       ,	A			:      int=True       ,	A			:      Union[str, Any]=True       ,	A			:      Tuple=2_2_4       ,	A			:      Optional[Any]=3       ,	A			:      Optional[int]=1_6       ,	A			:      int=None       ,	**A			:      Any       ,	):


      '''simple docstring'''




      super().__init__(
          vocab_size=A       ,	hidden_size=A       ,	num_hidden_layers=A       ,	num_attention_heads=A       ,	intermediate_size=A       ,	hidden_act=A       ,	hidden_dropout_prob=A       ,	attention_probs_dropout_prob=A       ,	max_position_embeddings=A       ,	type_vocab_size=A       ,	initializer_range=A       ,	layer_norm_eps=A       ,	pad_token_id=A       ,	bos_token_id=A       ,	eos_token_id=A       ,	**A       ,	)
      a  :    List[Any]       =     max_ad_position_embeddings
      a  :    Dict       =     coordinate_size
      a  :    Union[str, Any]       =     shape_size
      a  :    List[str]       =     has_relative_attention_bias
      a  :    int       =     rel_pos_bins
      a  :    Dict       =     max_rel_pos
      a  :    List[Any]       =     has_spatial_attention_bias
      a  :    List[Any]       =     rel_ad_pos_bins
      a  :    int       =     max_rel_ad_pos
      a  :    Dict       =     text_embed
      a  :    Optional[int]       =     visual_embed
      a  :    str       =     input_size
      a  :    List[str]       =     num_channels
      a  :    int       =     patch_size
      a  :    Any       =     classifier_dropout






class 		snake_case						(						UpperCAmelCase ):
    __magic_name__					  =  version.parse('''1.12''' )





    @property
    def        lowerCamelCase__   (							self			:      List[str]    ):


      '''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        lowerCamelCase__   (							self			:      str    ):


      '''simple docstring'''




      return 1E-5





    @property
    def        lowerCamelCase__   (							self			:      Union[str, Any]    ):


      '''simple docstring'''




      return 1_2





    def        lowerCamelCase__   (							self			:      int       ,	A			:      "ProcessorMixin"       ,	A			:      int = -1       ,	A			:      int = -1       ,	A			:      bool = False       ,	A			:      Optional["TensorType"] = None       ,	A			:      int = 3       ,	A			:      int = 4_0       ,	A			:      int = 4_0       ,	):


      '''simple docstring'''




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

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

      # Generate dummy bounding boxes
      a  :    int       =     [[[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)
      a  :    List[str]       =     self._generate_dummy_images(A       ,	A       ,	A       ,	A    )

      a  :    List[Any]       =     dict(
          processor(
              A       ,	text=A       ,	boxes=A       ,	return_tensors=A       ,	)    )

      return inputs












"""simple docstring"""


from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler




