



"""simple docstring"""





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def 	UpperCAmelCase     (		A	:						Optional[int]     ,				A	:						List[Any]     ,				A	:						Optional[int]     ,				A	:						Union[str, Any]       ):

		'''simple docstring'''


		if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE       ):
				_UpperCAmelCase							 =		np.full((len(_SCREAMING_SNAKE_CASE       ), sequence_length, 2)     ,				_SCREAMING_SNAKE_CASE       )
		else:
				_UpperCAmelCase							 =		np.full((len(_SCREAMING_SNAKE_CASE       ), sequence_length)     ,				_SCREAMING_SNAKE_CASE       )

		for i, tensor in enumerate(_SCREAMING_SNAKE_CASE       ):
				if padding_side == "right":
						if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE       ):
								_UpperCAmelCase							 =		tensor[:sequence_length]
						else:
								_UpperCAmelCase							 =		tensor[:sequence_length]
				else:
						if isinstance(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE       ):
								_UpperCAmelCase							 =		tensor[:sequence_length]
						else:
								_UpperCAmelCase							 =		tensor[:sequence_length]

		return out_tensor.tolist()




def 	UpperCAmelCase     (		A	:						Any       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		ord(_SCREAMING_SNAKE_CASE       )
		if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
				return True
		_UpperCAmelCase							 =		unicodedata.category(_SCREAMING_SNAKE_CASE       )
		if cat.startswith('P'       ):
				return True
		return False






@dataclass
class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					42
							_UpperCAmelCase      				=					True
							_UpperCAmelCase      				=					None
							_UpperCAmelCase      				=					None
							_UpperCAmelCase      				=					-1_00
							_UpperCAmelCase      				=					"pt"






							def        lowerCamelCase_   (  self	,       snake_case						)			->							str:
									import torch

									_UpperCAmelCase							 =		'label' if 'label' in features[0].keys() else 'labels'
									_UpperCAmelCase							 =		[feature[label_name] for feature in features] if label_name in features[0].keys() else None
									_UpperCAmelCase							 =		self.tokenizer.pad(
									    snake_case	,       padding=self.padding	,       max_length=self.max_length	,       pad_to_multiple_of=self.pad_to_multiple_of	,       return_tensors='pt' if labels is None else None	,       )

									if labels is None:
											return batch

									_UpperCAmelCase							 =		torch.tensor(batch['entity_ids']						).shape[1]
									_UpperCAmelCase							 =		self.tokenizer.padding_side
									if padding_side == "right":
											_UpperCAmelCase							 =		[
											    list(snake_case						) + [self.label_pad_token_id] * (sequence_length - len(snake_case						)) for label in labels
											]
									else:
											_UpperCAmelCase							 =		[
											    [self.label_pad_token_id] * (sequence_length - len(snake_case						)) + list(snake_case						) for label in labels
											]

									_UpperCAmelCase							 =		[feature['ner_tags'] for feature in features]
									_UpperCAmelCase							 =		padding_tensor(snake_case	,       -1	,       snake_case	,       snake_case						)
									_UpperCAmelCase							 =		[feature['original_entity_spans'] for feature in features]
									_UpperCAmelCase							 =		padding_tensor(snake_case	,       (-1, -1)	,       snake_case	,       snake_case						)
									_UpperCAmelCase							 =		{k: torch.tensor(snake_case	,       dtype=torch.intaa						) for k, v in batch.items()}

									return batch







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''beit'''






							def __init__(  self	,       snake_case=8192	,       snake_case=768	,       snake_case=12	,       snake_case=12	,       snake_case=3072	,       snake_case="gelu"	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=224	,       snake_case=16	,       snake_case=3	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=False	,       snake_case=0.1	,       snake_case=0.1	,       snake_case=True	,       snake_case=[3, 5, 7, 11]	,       snake_case=[1, 2, 3, 6]	,       snake_case=True	,       snake_case=0.4	,       snake_case=256	,       snake_case=1	,       snake_case=False	,       snake_case=255	,       **snake_case	,       )			->							str:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		vocab_size
									_UpperCAmelCase							 =		hidden_size
									_UpperCAmelCase							 =		num_hidden_layers
									_UpperCAmelCase							 =		num_attention_heads
									_UpperCAmelCase							 =		intermediate_size
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		use_mask_token
									_UpperCAmelCase							 =		use_absolute_position_embeddings
									_UpperCAmelCase							 =		use_relative_position_bias
									_UpperCAmelCase							 =		use_shared_relative_position_bias
									_UpperCAmelCase							 =		layer_scale_init_value
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		use_mean_pooling
									# decode head attributes (semantic segmentation)
									_UpperCAmelCase							 =		out_indices
									_UpperCAmelCase							 =		pool_scales
									# auxiliary head attributes (semantic segmentation)
									_UpperCAmelCase							 =		use_auxiliary_head
									_UpperCAmelCase							 =		auxiliary_loss_weight
									_UpperCAmelCase							 =		auxiliary_channels
									_UpperCAmelCase							 =		auxiliary_num_convs
									_UpperCAmelCase							 =		auxiliary_concat_input
									_UpperCAmelCase							 =		semantic_loss_ignore_index







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


