



"""simple docstring"""
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



"""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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    vocab_size=lowerCAmelCase		,    hidden_size=lowerCAmelCase		,    num_hidden_layers=lowerCAmelCase		,    num_attention_heads=lowerCAmelCase		,    intermediate_size=lowerCAmelCase		,    hidden_act=lowerCAmelCase		,    hidden_dropout_prob=lowerCAmelCase		,    attention_probs_dropout_prob=lowerCAmelCase		,    max_position_embeddings=lowerCAmelCase		,    type_vocab_size=lowerCAmelCase		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""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 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

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

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

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * 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)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


