
from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


lowercase__		:List[Any]			  =   {
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 ='''ernie_m'''
							lowercase_	:	Dict[str, str]	 ={"dropout": "classifier_dropout", "num_classes": "num_labels"}




							def __init__(    self       ,A__ = 2_5_0_0_0_2       ,A__ = 7_6_8       ,A__ = 1_2       ,A__ = 1_2       ,A__ = 3_0_7_2       ,A__ = "gelu"       ,A__ = 0.1       ,A__ = 0.1       ,A__ = 5_1_4       ,A__ = 0.02       ,A__ = 1       ,A__ = 1E-05       ,A__=None       ,A__=False       ,A__=0.0       ,**A__       ,):
												super().__init__(pad_token_id=A__       ,**A__)
												lowercase						     =						vocab_size
												lowercase						     =						hidden_size
												lowercase						     =						num_hidden_layers
												lowercase						     =						num_attention_heads
												lowercase						     =						intermediate_size
												lowercase						     =						hidden_act
												lowercase						     =						hidden_dropout_prob
												lowercase						     =						attention_probs_dropout_prob
												lowercase						     =						max_position_embeddings
												lowercase						     =						initializer_range
												lowercase						     =						layer_norm_eps
												lowercase						     =						classifier_dropout
												lowercase						     =						is_decoder
												lowercase						     =						act_dropout






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     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base''')

												lowercase						     =						{
												    '''input_ids''': tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]       ,dtype=tf.intaa),  # "My dog is cute"
												    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]       ,dtype=tf.intaa),
												}

												lowercase						     =						model(A__)['''last_hidden_state''']
												lowercase						     =						tf.TensorShape((1, 6, 7_6_8))
												self.assertEqual(output.shape       ,A__)
												# compare the actual values for a slice.
												lowercase						     =						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))




