
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						=						logging.get_logger(__name__)

UpperCamelCase_						=						{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}

class      _SCREAMING_SNAKE_CASE				(		snake_case__				):
				lowerCamelCase_	  =  """lilt"""





				def __init__(		self			:		Union[str, Any]       ,   snake_case_			:		List[Any]=3_0522       ,   snake_case_			:		int=768       ,   snake_case_			:		Any=12       ,   snake_case_			:		Optional[Any]=12       ,   snake_case_			:		str=3072       ,   snake_case_			:		str="gelu"       ,   snake_case_			:		Union[str, Any]=0.1       ,   snake_case_			:		str=0.1       ,   snake_case_			:		Any=512       ,   snake_case_			:		Union[str, Any]=2       ,   snake_case_			:		Optional[Any]=0.02       ,   snake_case_			:		Union[str, Any]=1E-12       ,   snake_case_			:		List[str]=0       ,   snake_case_			:		Any="absolute"       ,   snake_case_			:		List[Any]=None       ,   snake_case_			:		Optional[Any]=4       ,   snake_case_			:		int=1024       ,   **snake_case_			:		Tuple       ,   ):


								"""simple docstring"""

								super().__init__(pad_token_id=__lowercase       ,   **__lowercase				)

								A  :	int				=					vocab_size
								A  :	List[Any]				=					hidden_size
								A  :	Dict				=					num_hidden_layers
								A  :	Any				=					num_attention_heads
								A  :	Union[str, Any]				=					hidden_act
								A  :	Dict				=					intermediate_size
								A  :	Optional[int]				=					hidden_dropout_prob
								A  :	Optional[Any]				=					attention_probs_dropout_prob
								A  :	Tuple				=					max_position_embeddings
								A  :	Any				=					type_vocab_size
								A  :	Union[str, Any]				=					initializer_range
								A  :	List[str]				=					layer_norm_eps
								A  :	Dict				=					position_embedding_type
								A  :	Dict				=					classifier_dropout
								A  :	List[str]				=					channel_shrink_ratio
								A  :	Tuple				=					max_ad_position_embeddings






"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,     expected_slice.numpy()    ,     atol=1e-4      )      )




