





'''simple docstring'''

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase__      :   Optional[Any]      	=			logging.get_logger(__name__)

lowerCAmelCase__      :   int      	=			{
    "microsoft/layoutlm-base-uncased": (
        "https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json"
    ),
    "microsoft/layoutlm-large-uncased": (
        "https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json"
    ),
}


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	'''layoutlm'''
					def __init__(   self       :      int		,	UpperCAmelCase_       :      Optional[Any]=30_522		,	UpperCAmelCase_       :      Any=768		,	UpperCAmelCase_       :      Tuple=12		,	UpperCAmelCase_       :      str=12		,	UpperCAmelCase_       :      Optional[int]=3_072		,	UpperCAmelCase_       :      Optional[int]="gelu"		,	UpperCAmelCase_       :      int=0.1		,	UpperCAmelCase_       :      str=0.1		,	UpperCAmelCase_       :      str=512		,	UpperCAmelCase_       :      str=2		,	UpperCAmelCase_       :      Optional[int]=0.02		,	UpperCAmelCase_       :      Tuple=1e-12		,	UpperCAmelCase_       :      Tuple=0		,	UpperCAmelCase_       :      Any="absolute"		,	UpperCAmelCase_       :      int=True		,	UpperCAmelCase_       :      List[str]=1_024		,	**UpperCAmelCase_       :      Optional[Any]		,	):







							"""simple docstring"""
							super().__init__(pad_token_id=UpperCAmelCase_		,	**UpperCAmelCase_		)
							__UpperCAmelCase	:							List[Any]     =     vocab_size
							__UpperCAmelCase	:							Optional[int]     =     hidden_size
							__UpperCAmelCase	:							Union[str, Any]     =     num_hidden_layers
							__UpperCAmelCase	:							List[str]     =     num_attention_heads
							__UpperCAmelCase	:							List[str]     =     hidden_act
							__UpperCAmelCase	:							Optional[int]     =     intermediate_size
							__UpperCAmelCase	:							List[Any]     =     hidden_dropout_prob
							__UpperCAmelCase	:							Optional[Any]     =     attention_probs_dropout_prob
							__UpperCAmelCase	:							Dict     =     max_position_embeddings
							__UpperCAmelCase	:							Tuple     =     type_vocab_size
							__UpperCAmelCase	:							int     =     initializer_range
							__UpperCAmelCase	:							Optional[int]     =     layer_norm_eps
							__UpperCAmelCase	:							str     =     position_embedding_type
							__UpperCAmelCase	:							Any     =     use_cache
							__UpperCAmelCase	:							Union[str, Any]     =     max_ad_position_embeddings






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		,	UpperCAmelCase_       :      PretrainedConfig		,	UpperCAmelCase_       :      str = "default"		,	UpperCAmelCase_       :      List[PatchingSpec] = None		,	):







							"""simple docstring"""
							super().__init__(UpperCAmelCase_		,	task=UpperCAmelCase_		,	patching_specs=UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     config.max_ad_position_embeddings - 1
					@property
					def 		lowerCamelCase_   (   self       :      List[str]		):







							"""simple docstring"""
							return OrderedDict(
							    [
							        ("input_ids", {0: "batch", 1: "sequence"}),
							        ("bbox", {0: "batch", 1: "sequence"}),
							        ("attention_mask", {0: "batch", 1: "sequence"}),
							        ("token_type_ids", {0: "batch", 1: "sequence"}),
							    ]		)






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      PreTrainedTokenizer		,	UpperCAmelCase_       :      int = -1		,	UpperCAmelCase_       :      int = -1		,	UpperCAmelCase_       :      bool = False		,	UpperCAmelCase_       :      Optional[TensorType] = None		,	):







							"""simple docstring"""
							__UpperCAmelCase	:							List[Any]     =     super().generate_dummy_inputs(
							    UpperCAmelCase_		,	batch_size=UpperCAmelCase_		,	seq_length=UpperCAmelCase_		,	is_pair=UpperCAmelCase_		,	framework=UpperCAmelCase_		)

							# Generate a dummy bbox
							__UpperCAmelCase	:							Dict     =     [48, 84, 73, 128]

							if not framework == TensorType.PYTORCH:
									raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."		)

							if not is_torch_available():
									raise ValueError("Cannot generate dummy inputs without PyTorch installed."		)
							import torch

							__UpperCAmelCase  ,			__UpperCAmelCase	:							List[str]     =     input_dict["input_ids"].shape
							__UpperCAmelCase	:							str     =     torch.tensor([*[box] * seq_length]		).tile(UpperCAmelCase_		,	1		,	1		)
							return input_dict











'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




