



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 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			int:
							UpperCAmelCase_=       FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base"""					)
							UpperCAmelCase_=       AutoTokenizer.from_pretrained("""xlm-roberta-base"""					)
							UpperCAmelCase_=       """The dog is cute and lives in the garden house"""
							UpperCAmelCase_=       jnp.array([tokenizer.encode(__UpperCAmelCase					)]					)

							UpperCAmelCase_=       (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
							UpperCAmelCase_=       jnp.array(
							    [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]					)

							UpperCAmelCase_=       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					)					)









__A       =						6_5521



def 	__a					(   lowerCAmelCase_       : str    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       1
						UpperCAmelCase_=       0
						for plain_chr in plain_text:
												UpperCAmelCase_=       (a + ord(lowerCAmelCase_    )) % MOD_ADLER
												UpperCAmelCase_=       (b + a) % MOD_ADLER
						return (b << 16) | a




