'''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			):






   """simple docstring"""
   @slow
   def 		_lowerCamelCase (self				)    ->     str:
        lowercase_					:       Dict     				=					FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'				)
        lowercase_					:       Optional[int]     				=					AutoTokenizer.from_pretrained('xlm-roberta-base'				)
        lowercase_					:       int     				=					'The dog is cute and lives in the garden house'
        lowercase_					:       Optional[Any]     				=					jnp.array([tokenizer.encode(_a				)]				)

        lowercase_					:       Any     				=					(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
        lowercase_					:       Union[str, Any]     				=					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_					:       int     				=					model(_a				)['last_hidden_state']
        self.assertEqual(output.shape       ,				_a				)
        # compare the actual values for a slice of last dim
        self.assertTrue(jnp.allclose(output[:, :, -1]       ,				_a       ,				atol=1e-3				)				)





'''simple docstring'''




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


_A								=   logging.get_logger(__name__)

_A								=   {
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       Any     =    '''mra'''
   def __init__(self       ,				_a=50_265       ,				_a=768       ,				_a=12       ,				_a=12       ,				_a=3_072       ,				_a="gelu"       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=1       ,				_a=0.02       ,				_a=1e-5       ,				_a="absolute"       ,				_a=4       ,				_a="full"       ,				_a=0       ,				_a=0       ,				_a=1       ,				_a=0       ,				_a=2       ,				**_a       ,				)    ->     int:
        super().__init__(pad_token_id=_a       ,				bos_token_id=_a       ,				eos_token_id=_a       ,				**_a				)

        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       List[str]     				=					max_position_embeddings
        lowercase_					:       Optional[Any]     				=					hidden_size
        lowercase_					:       Optional[int]     				=					num_hidden_layers
        lowercase_					:       Dict     				=					num_attention_heads
        lowercase_					:       Optional[int]     				=					intermediate_size
        lowercase_					:       Dict     				=					hidden_act
        lowercase_					:       str     				=					hidden_dropout_prob
        lowercase_					:       int     				=					attention_probs_dropout_prob
        lowercase_					:       str     				=					initializer_range
        lowercase_					:       Union[str, Any]     				=					type_vocab_size
        lowercase_					:       Any     				=					layer_norm_eps
        lowercase_					:       Union[str, Any]     				=					position_embedding_type
        lowercase_					:       Any     				=					block_per_row
        lowercase_					:       Optional[int]     				=					approx_mode
        lowercase_					:       int     				=					initial_prior_first_n_blocks
        lowercase_					:       str     				=					initial_prior_diagonal_n_blocks




