
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


__snake_case					:							Dict           =							logging.get_logger(__name__)

__snake_case					:							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 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    Optional[int]												=			"""layoutlm"""


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


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A = "default"  , A = None  , )  ->str:
									super().__init__(A  , task=A  , patching_specs=A	)
									UpperCAmelCase__		:Union[str, Any] 		=    config.max_ad_position_embeddings - 1


						@property
						def    A__       (  self	)  ->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  , A  , A = -1  , A = -1  , A = False  , A = None  , )  ->Mapping[str, Any]:
									UpperCAmelCase__		:str 		=    super().generate_dummy_inputs(
									    A  , batch_size=A  , seq_length=A  , is_pair=A  , framework=A	)

									# Generate a dummy bbox
									UpperCAmelCase__		: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

									UpperCAmelCase__		,       UpperCAmelCase__		:Tuple 		=    input_dict['input_ids'].shape
									UpperCAmelCase__		:List[Any] 		=    torch.tensor([*[box] * seq_length]	).tile(A  , 1  , 1	)
									return input_dict







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    np.arange(start=3     ,						stop=SCREAMING_SNAKE_CASE     ,						step=4							).tolist() if k > 0 else [10]
			UpperCAmelCase__		:List[Any] 		=    _quantize(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			# count number of elements per group
			UpperCAmelCase__		:Dict 		=    np.unique(SCREAMING_SNAKE_CASE     ,						return_counts=SCREAMING_SNAKE_CASE							)[1]
			UpperCAmelCase__		:Any 		=    [0] + bins + [np.inf]
			logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE							)							)
			logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE							)							)
			return groups




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A  , A	)  ->Union[str, Any]:
									if not isinstance(A  , A	):
												raise ValueError(
												    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(A	)	)
									UpperCAmelCase__		:Tuple 		=    sampler
									UpperCAmelCase__		:Tuple 		=    group_ids
									UpperCAmelCase__		:str 		=    batch_size


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

									UpperCAmelCase__		:Any 		=    0
									for idx in self.sampler:
												UpperCAmelCase__		:List[Any] 		=    self.group_ids[idx]
												buffer_per_group[group_id].append(A	)
												samples_per_group[group_id].append(A	)
												if len(buffer_per_group[group_id]	) == self.batch_size:
															yield buffer_per_group[group_id]  # TODO
															num_batches += 1
															del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]	) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									UpperCAmelCase__		:Union[str, Any] 		=    len(self	)
									UpperCAmelCase__		:Tuple 		=    expected_num_batches - num_batches
									if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												UpperCAmelCase__		:str 		=    []
												for group_id, idxs in sorted(buffer_per_group.items()  , key=lambda A	: x[0]	):
															batch_idx.extend(A	)
															if len(A	) >= self.batch_size:
																		yield batch_idx[: self.batch_size]
																		UpperCAmelCase__		:int 		=    batch_idx[self.batch_size :]
																		num_remaining -= 1
												if len(A	) > 0:
															yield batch_idx
															num_remaining -= 1
									assert num_remaining == 0


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





