



"""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


A_	:						Union[str, Any]				=			logging.get_logger(__name__)

A_	:						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 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		List[str]     	=   '''layoutlm'''





	def __init__(			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					Tuple=30_522    ,					_SCREAMING_SNAKE_CASE							:					Dict=768    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=12    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=12    ,					_SCREAMING_SNAKE_CASE							:					Dict=3_072    ,					_SCREAMING_SNAKE_CASE							:					List[Any]="gelu"    ,					_SCREAMING_SNAKE_CASE							:					Tuple=0.1    ,					_SCREAMING_SNAKE_CASE							:					Any=0.1    ,					_SCREAMING_SNAKE_CASE							:					Tuple=512    ,					_SCREAMING_SNAKE_CASE							:					str=2    ,					_SCREAMING_SNAKE_CASE							:					List[str]=0.0_2    ,					_SCREAMING_SNAKE_CASE							:					List[str]=1E-1_2    ,					_SCREAMING_SNAKE_CASE							:					int=0    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]="absolute"    ,					_SCREAMING_SNAKE_CASE							:					Tuple=True    ,					_SCREAMING_SNAKE_CASE							:					int=1_024    ,					**_SCREAMING_SNAKE_CASE							:					Any    ,					)	->  Dict:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					PretrainedConfig    ,					_SCREAMING_SNAKE_CASE							:					str = "default"    ,					_SCREAMING_SNAKE_CASE							:					List[PatchingSpec] = None    ,					)	->  Optional[Any]:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE    ,					task=_SCREAMING_SNAKE_CASE    ,					patching_specs=_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    config.max_ad_position_embeddings - 1





	@property
	def  _lowerCAmelCase    (			self							:					Dict						)	->  Mapping[str, Mapping[int, 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							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					PreTrainedTokenizer    ,					_SCREAMING_SNAKE_CASE							:					int = -1    ,					_SCREAMING_SNAKE_CASE							:					int = -1    ,					_SCREAMING_SNAKE_CASE							:					bool = False    ,					_SCREAMING_SNAKE_CASE							:					Optional[TensorType] = None    ,					)	->  Mapping[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    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
					SCREAMING_SNAKE_CASE	:       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

					SCREAMING_SNAKE_CASE	:       int 							=    input_dict['input_ids'].shape
					SCREAMING_SNAKE_CASE	:       List[str] 							=    torch.tensor([*[box] * seq_length]						).tile(_SCREAMING_SNAKE_CASE    ,					1    ,					1						)
					return input_dict










"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=7    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=3    ,					_SCREAMING_SNAKE_CASE							:					List[str]=18    ,					_SCREAMING_SNAKE_CASE							:					str=30    ,					_SCREAMING_SNAKE_CASE							:					str=400    ,					_SCREAMING_SNAKE_CASE							:					Any=True    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					Dict=0.8_7_5    ,					_SCREAMING_SNAKE_CASE							:					List[str]=True    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=[0.5, 0.5, 0.5]    ,					_SCREAMING_SNAKE_CASE							:					str=[0.5, 0.5, 0.5]    ,					)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    size if size is not None else {'shortest_edge': 20}
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    parent
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    batch_size
					SCREAMING_SNAKE_CASE	:       Any 							=    num_channels
					SCREAMING_SNAKE_CASE	:       Any 							=    image_size
					SCREAMING_SNAKE_CASE	:       str 							=    min_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    max_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    do_resize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    size
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    crop_pct
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    do_normalize
					SCREAMING_SNAKE_CASE	:       Dict 							=    image_mean
					SCREAMING_SNAKE_CASE	:       str 							=    image_std







	def  _lowerCAmelCase    (			self							:					Dict						)	->  Any:





					"""simple docstring"""


					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "crop_pct": self.crop_pct,
					}




@require_torch
@require_vision
class 						lowerCAmelCase__	(  _lowerCamelCase       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   ConvNextImageProcessor if is_vision_available() else None





	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ConvNextImageProcessingTester(self						)





	@property
	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Dict:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_resize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'size'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'crop_pct'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_normalize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_mean'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_std'						)						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 20}						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 42}						)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					pass





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					Image.Image						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					numpify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					np.ndarray						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       int 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)







	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					torchify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					torch.Tensor						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    image_processing(image_inputs[0]    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        1,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Any 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					return_tensors='pt'						).pixel_values
					self.assertEqual(
					    encoded_images.shape    ,					(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





