from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest



@require_tokenizers
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		SqueezeBertTokenizer
							_lowerCamelCase						=		SqueezeBertTokenizerFast
							_lowerCamelCase						=		True



							def        UpperCamelCase       (						self      ,				**lowercase_				):
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname      ,				**lowercase_				)



							@slow
							def        UpperCamelCase       (						self				):
													_snake_case						:     List[str]				=    SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"				)

													_snake_case						:     str				=    tokenizer.encode("sequence builders"      ,				add_special_tokens=lowercase_				)
													_snake_case						:     List[Any]				=    tokenizer.encode("multi-sequence build"      ,				add_special_tokens=lowercase_				)

													_snake_case						:     Union[str, Any]				=    tokenizer.build_inputs_with_special_tokens(lowercase_				)
													_snake_case						:     Any				=    tokenizer.build_inputs_with_special_tokens(lowercase_      ,				lowercase_				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation



@require_tf
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]				)
													_snake_case						:     List[Any]				=    get_tf_activation("gelu"				)
													_snake_case						:     Any				=    get_tf_activation("gelu_10"				)

													_snake_case						:     Dict				=    gelu(lowercase_				)
													_snake_case						:     Optional[int]				=    geluaa(lowercase_				)

													_snake_case						:     Tuple				=    tf.where(y_gelu_aa < 10.0      ,				1.0      ,				0.0				)

													self.assertEqual(tf.math.reduce_max(lowercase_				).numpy().item()      ,				10.0				)
													self.assertTrue(np.allclose(y_gelu * clipped_mask      ,				y_gelu_aa * clipped_mask				)				)



							def        UpperCamelCase       (						self				):
													get_tf_activation("gelu"				)
													get_tf_activation("gelu_10"				)
													get_tf_activation("gelu_fast"				)
													get_tf_activation("gelu_new"				)
													get_tf_activation("glu"				)
													get_tf_activation("mish"				)
													get_tf_activation("quick_gelu"				)
													get_tf_activation("relu"				)
													get_tf_activation("sigmoid"				)
													get_tf_activation("silu"				)
													get_tf_activation("swish"				)
													get_tf_activation("tanh"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation("bogus"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation(lowercase_				)