



"""simple docstring"""







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


UpperCamelCase     =							logging.get_logger(__name__)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     "roc_bert"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=30522						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-12						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="absolute"						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=910						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=24858						,	SCREAMING_SNAKE_CASE__=True						,	**SCREAMING_SNAKE_CASE__						,	)      ->	str:
				A__											=					vocab_size
				A__											=					max_position_embeddings
				A__											=					hidden_size
				A__											=					num_hidden_layers
				A__											=					num_attention_heads
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					hidden_dropout_prob
				A__											=					attention_probs_dropout_prob
				A__											=					initializer_range
				A__											=					type_vocab_size
				A__											=					layer_norm_eps
				A__											=					use_cache
				A__											=					enable_pronunciation
				A__											=					enable_shape
				A__											=					pronunciation_embed_dim
				A__											=					pronunciation_vocab_size
				A__											=					shape_embed_dim
				A__											=					shape_vocab_size
				A__											=					concat_input
				A__											=					position_embedding_type
				A__											=					classifier_dropout
				super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)









"""simple docstring"""






import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
     import jax.numpy as jnp

     from transformers import FlaxXLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_flax
class     UpperCAmelCase    (	unittest.TestCase  ):



   @slow
   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      FlaxXLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
     _snake_case   	=      AutoTokenizer.from_pretrained('''xlm-roberta-base'''				)
     _snake_case   	=      '''The dog is cute and lives in the garden house'''
     _snake_case   	=      jnp.array([tokenizer.encode(__lowerCamelCase				)]				)

     _snake_case   	=      (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
     _snake_case   	=      jnp.array(
         [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]				)

     _snake_case   	=      model(__lowerCamelCase				)['''last_hidden_state''']
     self.assertEqual(output.shape							,       __lowerCamelCase				)
     # compare the actual values for a slice of last dim
     self.assertTrue(jnp.allclose(output[:, :, -1]							,       __lowerCamelCase							,       atol=1E-3				)				)





