




'''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 	snake_case_					(UpperCamelCase :						List[str]				,		UpperCamelCase :						List[Any]				,		UpperCamelCase :						Dict				,		UpperCamelCase :						Tuple   ):





							'''simple docstring'''


							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 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_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					(	_a					):
							lowercase_           =       42
							lowercase_           =       True
							lowercase_           =       None
							lowercase_           =       None
							lowercase_           =       -100
							lowercase_           =       'pt'







							def      __lowerCAmelCase    (						self :						Optional[int]							,    lowerCAmelCase_ :						Optional[int]      )    ->							Optional[Any]:


														"""simple docstring"""


														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(
														    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

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

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

														return batch







'''simple docstring'''


_snake_case   :   Any     							=	tuple[float, float, float]
_snake_case   :   Optional[int]     							=	tuple[float, float, float]
def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad   ):





							'''simple docstring'''


							_a			  =       end_pointa[0] - end_pointa[0]
							_a			  =       end_pointa[1] - end_pointa[1]
							_a			  =       end_pointa[2] - end_pointa[2]
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						Vectorad   ):





							'''simple docstring'''


							_a			  =       ab[1] * ac[2] - ab[2] * ac[1]  # *i
							_a			  =       (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
							_a			  =       ab[0] * ac[1] - ab[1] * ac[0]  # *k
							return (x, y, z)
def 	snake_case_					(UpperCamelCase :						Vectorad				,		UpperCamelCase :						int   ):





							'''simple docstring'''


							return tuple(round(UpperCamelCase				,		UpperCamelCase   ) for x in vector   ) == (0, 0, 0)

def 	snake_case_					(UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						Pointad				,		UpperCamelCase :						int = 10   ):





							'''simple docstring'''


							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							_a			  =       create_vector(UpperCamelCase				,		UpperCamelCase   )
							return is_zero_vector(get_ad_vectors_cross(UpperCamelCase				,		UpperCamelCase   )				,		UpperCamelCase   )

