





'''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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):






				if isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								_snake_case :			List[Any]  						=   np.full((len(__lowerCAmelCase ), sequence_length, 2)	,			__lowerCAmelCase )
				else:
								_snake_case :			List[str]  						=   np.full((len(__lowerCAmelCase ), sequence_length)	,			__lowerCAmelCase )

				for i, tensor in enumerate(__lowerCAmelCase ):
								if padding_side == "right":
												if isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
																_snake_case :			Optional[Any]  						=   tensor[:sequence_length]
												else:
																_snake_case :			List[Any]  						=   tensor[:sequence_length]
								else:
												if isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
																_snake_case :			Union[str, Any]  						=   tensor[:sequence_length]
												else:
																_snake_case :			List[Any]  						=   tensor[:sequence_length]

				return out_tensor.tolist()

def       A__(		__lowerCAmelCase ):
				_snake_case :			str  						=   ord(__lowerCAmelCase )
				if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
								return True
				_snake_case :			Tuple  						=   unicodedata.category(__lowerCAmelCase )
				if cat.startswith('P' ):
								return True
				return False


@dataclass
class 			lowercase				(							a_     ):



						"""simple docstring"""

						_UpperCamelCase  :	PreTrainedTokenizerBase
						_UpperCamelCase  :	Union[bool, str, PaddingStrategy]								=	True
						_UpperCamelCase  :	Optional[int]								=	None
						_UpperCamelCase  :	Optional[int]								=	None
						_UpperCamelCase  :	int								=	-100
						_UpperCamelCase  :	str								=	"pt"







						def 		__UpperCAmelCase       (    self			:		Any ,				lowerCamelCase_			:		Dict					):





										'''simple docstring'''







										import torch

										_snake_case :			Tuple  						=   'label' if 'label' in features[0].keys() else 'labels'
										_snake_case :			List[Any]  						=   [feature[label_name] for feature in features] if label_name in features[0].keys() else None
										_snake_case :			Dict  						=   self.tokenizer.pad(
										    lowerCamelCase_ ,				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

										_snake_case :			int  						=   torch.tensor(batch['entity_ids']					).shape[1]
										_snake_case :			Union[str, Any]  						=   self.tokenizer.padding_side
										if padding_side == "right":
														_snake_case :			List[str]  						=   [
														    list(lowerCamelCase_					) + [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase_					)) for label in labels
														]
										else:
														_snake_case :			Optional[Any]  						=   [
														    [self.label_pad_token_id] * (sequence_length - len(lowerCamelCase_					)) + list(lowerCamelCase_					) for label in labels
														]

										_snake_case :			Tuple  						=   [feature['ner_tags'] for feature in features]
										_snake_case :			Dict  						=   padding_tensor(lowerCamelCase_ ,				-1 ,				lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			List[str]  						=   [feature['original_entity_spans'] for feature in features]
										_snake_case :			Union[str, Any]  						=   padding_tensor(lowerCamelCase_ ,				(-1, -1) ,				lowerCamelCase_ ,				lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   {k: torch.tensor(lowerCamelCase_ ,				dtype=torch.intaa					) for k, v in batch.items()}

										return batch













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_		:				Optional[Any]				=						{
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Tuple				=						[
		    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoCBertForCausalLM''',
		    '''RoCBertForMaskedLM''',
		    '''RoCBertForMultipleChoice''',
		    '''RoCBertForPreTraining''',
		    '''RoCBertForQuestionAnswering''',
		    '''RoCBertForSequenceClassification''',
		    '''RoCBertForTokenClassification''',
		    '''RoCBertLayer''',
		    '''RoCBertModel''',
		    '''RoCBertPreTrainedModel''',
		    '''load_tf_weights_in_roc_bert''',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		lowercase_		:				Tuple				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






