






"""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		):


							@slow
							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  List[str]		  =  TFXLMRobertaModel.from_pretrained("""jplu/tf-xlm-roberta-base"""							)

													A__						:  Tuple		  =  {
													    """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							),
													}

													A__						:  Any		  =  model(snake_case							)["""last_hidden_state"""]
													A__						:  Union[str, Any]		  =  tf.TensorShape((1, 6, 768)							)
													self.assertEqual(output.shape			,    snake_case							)
													# compare the actual values for a slice.
													A__						:  Optional[Any]		  =  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							)							)














"""simple docstring"""



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


A_    =							logging.get_logger(__name__)

A_    =							{
    '''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							'roc_bert'


							def __init__(			self      :						Tuple			,    snake_case      :						Union[str, Any]=3_0522			,    snake_case      :						Optional[Any]=768			,    snake_case      :						Any=12			,    snake_case      :						Tuple=12			,    snake_case      :						Any=3072			,    snake_case      :						Tuple="gelu"			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						List[str]=0.1			,    snake_case      :						List[str]=512			,    snake_case      :						int=2			,    snake_case      :						Optional[Any]=0.02			,    snake_case      :						str=1e-12			,    snake_case      :						int=True			,    snake_case      :						Optional[Any]=0			,    snake_case      :						str="absolute"			,    snake_case      :						int=None			,    snake_case      :						int=True			,    snake_case      :						Optional[int]=True			,    snake_case      :						List[Any]=768			,    snake_case      :						Dict=910			,    snake_case      :						Tuple=512			,    snake_case      :						Tuple=2_4858			,    snake_case      :						Optional[Any]=True			,    **snake_case      :						Any			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  vocab_size
													A__						:  Union[str, Any]		  =  max_position_embeddings
													A__						:  Any		  =  hidden_size
													A__						:  Optional[int]		  =  num_hidden_layers
													A__						:  Optional[Any]		  =  num_attention_heads
													A__						:  int		  =  intermediate_size
													A__						:  Optional[Any]		  =  hidden_act
													A__						:  Any		  =  hidden_dropout_prob
													A__						:  Tuple		  =  attention_probs_dropout_prob
													A__						:  Union[str, Any]		  =  initializer_range
													A__						:  str		  =  type_vocab_size
													A__						:  Any		  =  layer_norm_eps
													A__						:  List[str]		  =  use_cache
													A__						:  List[str]		  =  enable_pronunciation
													A__						:  int		  =  enable_shape
													A__						:  Tuple		  =  pronunciation_embed_dim
													A__						:  str		  =  pronunciation_vocab_size
													A__						:  Optional[Any]		  =  shape_embed_dim
													A__						:  str		  =  shape_vocab_size
													A__						:  List[str]		  =  concat_input
													A__						:  str		  =  position_embedding_type
													A__						:  str		  =  classifier_dropout
													super().__init__(pad_token_id=snake_case			,    **snake_case							)






