


'''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 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						if isinstance(lowerCamelCase,				lowerCamelCase):
												__lowerCAmelCase							       =				np.full((len(lowerCamelCase), sequence_length, 2),				lowerCamelCase)
						else:
												__lowerCAmelCase							       =				np.full((len(lowerCamelCase), sequence_length),				lowerCamelCase)

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

						return out_tensor.tolist()

def 							__magic_name__(						lowerCamelCase):
						__lowerCAmelCase							       =				ord(lowerCamelCase)
						if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
												return True
						__lowerCAmelCase							       =				unicodedata.category(lowerCamelCase)
						if cat.startswith('''P'''):
												return True
						return False




@dataclass
class       a__				( __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 					_snake_case  (self       ,			__lowercase		):
										import torch

										__lowerCAmelCase							       =				'''label''' if '''label''' in features[0].keys() else '''labels'''
										__lowerCAmelCase							       =				[feature[label_name] for feature in features] if label_name in features[0].keys() else None
										__lowerCAmelCase							       =				self.tokenizer.pad(
										    __lowercase       ,			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

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

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

										return batch





'''simple docstring'''

from typing import TYPE_CHECKING

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


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

