




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


UpperCAmelCase__    :							List[Any]       		=     logging.get_logger(__name__)

UpperCAmelCase__    :							Union[str, 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   UpperCAmelCase    (      a_ ):
   __SCREAMING_SNAKE_CASE		:				Dict      					=       'layoutlm'






   def __init__(						self :							Optional[int]    ,       UpperCamelCase_ :							Any=3_05_22    ,       UpperCamelCase_ :							List[Any]=7_68    ,       UpperCamelCase_ :							Optional[int]=12    ,       UpperCamelCase_ :							int=12    ,       UpperCamelCase_ :							Union[str, Any]=30_72    ,       UpperCamelCase_ :							str="gelu"    ,       UpperCamelCase_ :							Any=0.1    ,       UpperCamelCase_ :							Tuple=0.1    ,       UpperCamelCase_ :							Any=5_12    ,       UpperCamelCase_ :							Union[str, Any]=2    ,       UpperCamelCase_ :							str=0.02    ,       UpperCamelCase_ :							Optional[int]=1E-12    ,       UpperCamelCase_ :							str=0    ,       UpperCamelCase_ :							Tuple="absolute"    ,       UpperCamelCase_ :							Tuple=True    ,       UpperCamelCase_ :							Any=10_24    ,       **UpperCamelCase_ :							Tuple    ,       )->      Optional[int]:
     super().__init__(pad_token_id=UpperCamelCase_    ,       **UpperCamelCase_  )
     _lowercase:			List[str]      =     vocab_size
     _lowercase:			Optional[int]      =     hidden_size
     _lowercase:			int      =     num_hidden_layers
     _lowercase:			Optional[Any]      =     num_attention_heads
     _lowercase:			Optional[Any]      =     hidden_act
     _lowercase:			Union[str, Any]      =     intermediate_size
     _lowercase:			Union[str, Any]      =     hidden_dropout_prob
     _lowercase:			Optional[int]      =     attention_probs_dropout_prob
     _lowercase:			Optional[int]      =     max_position_embeddings
     _lowercase:			Any      =     type_vocab_size
     _lowercase:			Optional[int]      =     initializer_range
     _lowercase:			List[Any]      =     layer_norm_eps
     _lowercase:			str      =     position_embedding_type
     _lowercase:			Any      =     use_cache
     _lowercase:			Union[str, Any]      =     max_ad_position_embeddings

class   UpperCAmelCase    (      a_ ):






   def __init__(						self :							Optional[Any]    ,       UpperCamelCase_ :							PretrainedConfig    ,       UpperCamelCase_ :							str = "default"    ,       UpperCamelCase_ :							List[PatchingSpec] = None    ,       )->      Dict:
     super().__init__(UpperCamelCase_    ,       task=UpperCamelCase_    ,       patching_specs=UpperCamelCase_  )
     _lowercase:			int      =     config.max_ad_position_embeddings - 1






   @property
   def 			_a    (						self :							Optional[int]  )->      Mapping[str, Mapping[int, str]]:
     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 :							str    ,       UpperCamelCase_ :							PreTrainedTokenizer    ,       UpperCamelCase_ :							int = -1    ,       UpperCamelCase_ :							int = -1    ,       UpperCamelCase_ :							bool = False    ,       UpperCamelCase_ :							Optional[TensorType] = None    ,       )->      Mapping[str, Any]:
     _lowercase:			Optional[Any]      =     super().generate_dummy_inputs(
         UpperCamelCase_    ,       batch_size=UpperCamelCase_    ,       seq_length=UpperCamelCase_    ,       is_pair=UpperCamelCase_    ,       framework=UpperCamelCase_  )

     # Generate a dummy bbox
     _lowercase:			List[Any]      =     [48, 84, 73, 1_28]

     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

     _lowercase:			Tuple      =     input_dict['input_ids'].shape
     _lowercase:			List[str]      =     torch.tensor([*[box] * seq_length]  ).tile(UpperCamelCase_    ,       1    ,       1  )
     return input_dict

import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput




@dataclass
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]







class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		int	)			->				Tuple:
        _UpperCamelCase  :							str	    =	CustomOutput(images=np.random.rand(1							,      3							,      4							,      4	)	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							str	    =	CustomOutput(images=[PIL.Image.new('RGB'							,      (4, 4)	)]	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        # test output reinitialization with a `dict` for compatibility with `accelerate`
        _UpperCamelCase  :							Tuple	    =	CustomOutput({'images': np.random.rand(1							,      3							,      4							,      4	)}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							int	    =	CustomOutput({'images': [PIL.Image.new('RGB'							,      (4, 4)	)]}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)



