




"""simple docstring"""






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
			import torch






@require_torch
@require_optimum
@slow
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			def 					A_    (  self  ):
							_lowerCamelCase     :			Tuple    =    'hf-internal-testing/tiny-random-t5'
							_lowerCamelCase     :			Optional[int]    =    AutoTokenizer.from_pretrained(lowercase  )
							_lowerCamelCase     :			int    =    AutoModelForSeqaSeqLM.from_pretrained(lowercase  )

							_lowerCamelCase     :			Optional[Any]    =    tokenizer('This is me'  ,      return_tensors='pt'  )

							_lowerCamelCase     :			List[str]    =    model.to_bettertransformer()

							self.assertTrue(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

							_lowerCamelCase     :			List[str]    =    model.generate(**lowercase  )

							_lowerCamelCase     :			Tuple    =    model.reverse_bettertransformer()

							self.assertFalse(any('BetterTransformer' in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

							with tempfile.TemporaryDirectory() as tmpdirname:
											model.save_pretrained(lowercase  )

											_lowerCamelCase     :			int    =    AutoModelForSeqaSeqLM.from_pretrained(lowercase  )

											self.assertFalse(
											    any('BetterTransformer' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()  )  )

											_lowerCamelCase     :			Optional[Any]    =    model_reloaded.generate(**lowercase  )
											self.assertTrue(torch.allclose(lowercase  ,      lowercase  )  )






			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    'hf-internal-testing/tiny-random-t5'
							_lowerCamelCase     :			Union[str, Any]    =    AutoModelForSeqaSeqLM.from_pretrained(lowercase  )

							_lowerCamelCase     :			str    =    model.to_bettertransformer()

							with tempfile.TemporaryDirectory() as tmpdirname:
											with self.assertRaises(lowercase  ):
															model.save_pretrained(lowercase  )

											_lowerCamelCase     :			List[str]    =    model.reverse_bettertransformer()
											model.save_pretrained(lowercase  )

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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	@slow
	def  A							(  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(lowercase	)]	)

				_snake_case		=			(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
				_snake_case		=			jnp.array(
				    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]	)

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