




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





"""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     =							logging.get_logger(__name__)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50265						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1024						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=128						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=224						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Any:
				super().__init__(
				    vocab_size=SCREAMING_SNAKE_CASE__						,	hidden_size=SCREAMING_SNAKE_CASE__						,	num_hidden_layers=SCREAMING_SNAKE_CASE__						,	num_attention_heads=SCREAMING_SNAKE_CASE__						,	intermediate_size=SCREAMING_SNAKE_CASE__						,	hidden_act=SCREAMING_SNAKE_CASE__						,	hidden_dropout_prob=SCREAMING_SNAKE_CASE__						,	attention_probs_dropout_prob=SCREAMING_SNAKE_CASE__						,	max_position_embeddings=SCREAMING_SNAKE_CASE__						,	type_vocab_size=SCREAMING_SNAKE_CASE__						,	initializer_range=SCREAMING_SNAKE_CASE__						,	layer_norm_eps=SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__						,	)
				A__											=					max_ad_position_embeddings
				A__											=					coordinate_size
				A__											=					shape_size
				A__											=					has_relative_attention_bias
				A__											=					rel_pos_bins
				A__											=					max_rel_pos
				A__											=					has_spatial_attention_bias
				A__											=					rel_ad_pos_bins
				A__											=					max_rel_ad_pos
				A__											=					text_embed
				A__											=					visual_embed
				A__											=					input_size
				A__											=					num_channels
				A__											=					patch_size
				A__											=					classifier_dropout






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


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


			@property
			def   snake_case__	(      self		)      ->	int:
				return 12







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = 3						,	SCREAMING_SNAKE_CASE__ = 40						,	SCREAMING_SNAKE_CASE__ = 40						,	)      ->	Mapping[str, Any]:
				setattr(processor.image_processor						,	"apply_ocr"						,	SCREAMING_SNAKE_CASE__		)

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

				# Generate dummy bounding boxes
				A__											=					[[[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)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




