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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					if isinstance(__UpperCAmelCase				,    __UpperCAmelCase						):
										__a		 =       np.full((len(__UpperCAmelCase						), sequence_length, 2)				,    __UpperCAmelCase						)
					else:
										__a		 =       np.full((len(__UpperCAmelCase						), sequence_length)				,    __UpperCAmelCase						)

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

					return out_tensor.tolist()
def 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       ord(__UpperCAmelCase						)
					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
					__a		 =       unicodedata.category(__UpperCAmelCase						)
					if cat.startswith('P'						):
										return True
					return False



@dataclass
class 						a__							(       _UpperCAmelCase		):
			A__    :       PreTrainedTokenizerBase
			A__    :       Union[bool, str, PaddingStrategy]        =      True
			A__    :       Optional[int]        =      None
			A__    :       Optional[int]        =      None
			A__    :       int        =      -1_00
			A__    :       str        =      "pt"



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   List[str]:
								import torch

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

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

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

								return batch






from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Union[str, Any]							=	{"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Union[str, Any]							=	["""GPTNeoXTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		List[str]							=	[
	    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXForCausalLM""",
	    """GPTNeoXForQuestionAnswering""",
	    """GPTNeoXForSequenceClassification""",
	    """GPTNeoXForTokenClassification""",
	    """GPTNeoXLayer""",
	    """GPTNeoXModel""",
	    """GPTNeoXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

	lowerCamelCase_    :		Dict							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




