




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


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

__A :		Optional[Any]      							=    {
    '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 							lowerCamelCase		( UpperCAmelCase_				):
   lowercase       :  List[str]      =				'layoutlm'







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_=3_0522    ,      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_=0.02    ,      SCREAMING_SNAKE_CASE_=1e-12    ,      SCREAMING_SNAKE_CASE_=0    ,      SCREAMING_SNAKE_CASE_="absolute"    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=1024    ,      **SCREAMING_SNAKE_CASE_    ,      ):
       super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   str          =							vocab_size
       UpperCamelCase				:   Tuple          =							hidden_size
       UpperCamelCase				:   Dict          =							num_hidden_layers
       UpperCamelCase				:   List[str]          =							num_attention_heads
       UpperCamelCase				:   Optional[int]          =							hidden_act
       UpperCamelCase				:   Optional[Any]          =							intermediate_size
       UpperCamelCase				:   List[Any]          =							hidden_dropout_prob
       UpperCamelCase				:   Any          =							attention_probs_dropout_prob
       UpperCamelCase				:   List[str]          =							max_position_embeddings
       UpperCamelCase				:   Optional[Any]          =							type_vocab_size
       UpperCamelCase				:   List[str]          =							initializer_range
       UpperCamelCase				:   Dict          =							layer_norm_eps
       UpperCamelCase				:   Any          =							position_embedding_type
       UpperCamelCase				:   Union[str, Any]          =							use_cache
       UpperCamelCase				:   Tuple          =							max_ad_position_embeddings







class 							lowerCamelCase		( UpperCAmelCase_				):







   def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "default"    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
       super().__init__(SCREAMING_SNAKE_CASE_    ,      task=SCREAMING_SNAKE_CASE_    ,      patching_specs=SCREAMING_SNAKE_CASE_ )
       UpperCamelCase				:   List[Any]          =							config.max_ad_position_embeddings - 1







   @property
   def       a_							(							self ):
       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       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = -1    ,      SCREAMING_SNAKE_CASE_ = -1    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      ):
       UpperCamelCase				:   Optional[int]          =							super().generate_dummy_inputs(
           SCREAMING_SNAKE_CASE_    ,      batch_size=SCREAMING_SNAKE_CASE_    ,      seq_length=SCREAMING_SNAKE_CASE_    ,      is_pair=SCREAMING_SNAKE_CASE_    ,      framework=SCREAMING_SNAKE_CASE_ )

       # Generate a dummy bbox
       UpperCamelCase				:   Optional[int]          =							[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				:   Optional[Any]          =							input_dict["""input_ids"""].shape
       UpperCamelCase				:   Union[str, Any]          =							torch.tensor([*[box] * seq_length] ).tile(SCREAMING_SNAKE_CASE_    ,      1    ,      1 )
       return input_dict










"""simple docstring"""




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[int]          =							min(img.size    )

    if s < target_image_size:
        raise ValueError(f'min dim for image {s} < {target_image_size}'    )

    UpperCamelCase				:   Dict          =							target_image_size / s
    UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
    UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
    UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
    UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
    return img
def 					A_			(		snake_case_     :       List[Any]    ):







    '''simple docstring'''
    UpperCamelCase				:   Any          =							2.0 * x - 1.0
    return x
def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):







    '''simple docstring'''
    UpperCamelCase				:   Dict          =							x.detach().cpu()
    if process:
        UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
    UpperCamelCase				:   List[Any]          =							x.numpy()
    if process:
        UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
    UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
    if not x.mode == mode:
        UpperCamelCase				:   Any          =							x.convert(snake_case_    )
    return x
def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
    UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
    UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
    return x


def 					A_			(		snake_case_     :       int    ):







    '''simple docstring'''
    UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
    return x.permute(2						,0						,1    ).unsqueeze(0    )




