



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A				:    Optional[int]     						=				logging.get_logger(__name__)

_A				:    List[str]     						=				{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class 		lowerCamelCase__      (						lowerCAmelCase__     ):




					'''simple docstring'''



					A_						    =						"convbert"




					def __init__(  self				:	int     ,  UpperCamelCase_				:	int=3_0522     ,  UpperCamelCase_				:	Dict=768     ,  UpperCamelCase_				:	Any=12     ,  UpperCamelCase_				:	Tuple=12     ,  UpperCamelCase_				:	List[Any]=3072     ,  UpperCamelCase_				:	Union[str, Any]="gelu"     ,  UpperCamelCase_				:	List[str]=0.1     ,  UpperCamelCase_				:	str=0.1     ,  UpperCamelCase_				:	Optional[Any]=512     ,  UpperCamelCase_				:	List[str]=2     ,  UpperCamelCase_				:	Any=0.02     ,  UpperCamelCase_				:	int=1E-12     ,  UpperCamelCase_				:	Optional[Any]=1     ,  UpperCamelCase_				:	str=0     ,  UpperCamelCase_				:	str=2     ,  UpperCamelCase_				:	List[Any]=768     ,  UpperCamelCase_				:	Optional[Any]=2     ,  UpperCamelCase_				:	Optional[Any]=9     ,  UpperCamelCase_				:	Tuple=1     ,  UpperCamelCase_				:	Dict=None     ,  **UpperCamelCase_				:	Optional[Any]     ,  )  ->       Optional[int]:



						'''simple docstring'''





						super().__init__(
						    pad_token_id=_lowerCamelCase     ,  bos_token_id=_lowerCamelCase     ,  eos_token_id=_lowerCamelCase     ,  **_lowerCamelCase     ,  )

						_lowercase					:       int       =		vocab_size
						_lowercase					:       Optional[int]       =		hidden_size
						_lowercase					:       Optional[int]       =		num_hidden_layers
						_lowercase					:       Dict       =		num_attention_heads
						_lowercase					:       Any       =		intermediate_size
						_lowercase					:       Tuple       =		hidden_act
						_lowercase					:       List[str]       =		hidden_dropout_prob
						_lowercase					:       Union[str, Any]       =		attention_probs_dropout_prob
						_lowercase					:       List[Any]       =		max_position_embeddings
						_lowercase					:       Optional[Any]       =		type_vocab_size
						_lowercase					:       List[Any]       =		initializer_range
						_lowercase					:       Optional[int]       =		layer_norm_eps
						_lowercase					:       List[Any]       =		embedding_size
						_lowercase					:       int       =		head_ratio
						_lowercase					:       Union[str, Any]       =		conv_kernel_size
						_lowercase					:       Dict       =		num_groups
						_lowercase					:       Tuple       =		classifier_dropout






class 		lowerCamelCase__      (						lowerCAmelCase__     ):




					'''simple docstring'''



					@property
					def 						__UpperCAmelCase     (  self				:	str					)  ->       Mapping[str, Mapping[int, str]]:



						'''simple docstring'''





						if self.task == "multiple-choice":
							_lowercase					:       List[str]       =		{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							_lowercase					:       int       =		{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						    ]					)





'''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 		lowerCamelCase__      (						unittest.TestCase     ):




					'''simple docstring'''



					@slow
					def 						__UpperCAmelCase     (  self				:	int					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       str       =		AutoTokenizer.from_pretrained('xlm-roberta-base'					)
						_lowercase					:       List[Any]       =		'The dog is cute and lives in the garden house'
						_lowercase					:       Optional[int]       =		jnp.array([tokenizer.encode(UpperCamelCase_					)]					)

						_lowercase					:       int       =		(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
						_lowercase					:       Tuple       =		jnp.array(
						    [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]					)

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