




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_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: List[Any]					,	UpperCamelCase__							: int					,	UpperCamelCase__							: str )	->			Union[str, Any]:





				"""simple docstring"""


				if isinstance(UpperCamelCase__					,	UpperCamelCase__ ):
								__lowerCamelCase									=       np.full((len(UpperCamelCase__ ), sequence_length, 2)					,	UpperCamelCase__ )
				else:
								__lowerCamelCase									=       np.full((len(UpperCamelCase__ ), sequence_length)					,	UpperCamelCase__ )

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

				return out_tensor.tolist()







def      lowerCamelCase_						(			UpperCamelCase__							: List[Any] )	->			List[Any]:





				"""simple docstring"""


				__lowerCamelCase									=       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
				__lowerCamelCase									=       unicodedata.category(UpperCamelCase__ )
				if cat.startswith('P' ):
								return True
				return False







@dataclass
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







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


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							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(
							    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

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

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

							return batch








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.activations import gelu_new, gelu_python, get_activation







@require_torch
class        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertTrue(torch.allclose(gelu_python(lowerCamelCase__				)     ,		torch_builtin(lowerCamelCase__				)				)				)
							self.assertFalse(torch.allclose(gelu_python(lowerCamelCase__				)     ,		gelu_new(lowerCamelCase__				)				)				)


			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       get_activation('gelu_10'				)

							__lowerCamelCase									=       torch_builtin(lowerCamelCase__				)
							__lowerCamelCase									=       geluaa(lowerCamelCase__				)

							__lowerCamelCase									=       torch.where(y_gelu_aa < 10.0     ,		1     ,		0				)

							self.assertTrue(torch.max(lowerCamelCase__				).item() == 10.0				)
							self.assertTrue(torch.allclose(y_gelu * clipped_mask     ,		y_gelu_aa * clipped_mask				)				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							get_activation('gelu'				)
							get_activation('gelu_10'				)
							get_activation('gelu_fast'				)
							get_activation('gelu_new'				)
							get_activation('gelu_python'				)
							get_activation('gelu_pytorch_tanh'				)
							get_activation('linear'				)
							get_activation('mish'				)
							get_activation('quick_gelu'				)
							get_activation('relu'				)
							get_activation('sigmoid'				)
							get_activation('silu'				)
							get_activation('swish'				)
							get_activation('tanh'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation('bogus'				)
							with self.assertRaises(lowerCamelCase__				):
											get_activation(lowerCamelCase__				)






			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       get_activation('gelu'				)
							__lowerCamelCase									=       1
							__lowerCamelCase									=       get_activation('gelu'				)
							self.assertEqual(acta.a     ,		1				)
							with self.assertRaises(lowerCamelCase__				):
											__lowerCamelCase									=       acta.a


