


'''simple docstring'''


import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
class 	lowerCAmelCase__			(			unittest.TestCase	):
			lowerCAmelCase :	Union[str, Any]          =     MODEL_FOR_CAUSAL_LM_MAPPING
			lowerCAmelCase :	int          =     TF_MODEL_FOR_CAUSAL_LM_MAPPING




			@require_torch
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      pipeline(task="text-generation"	,  model="sshleifer/tiny-ctrl"	,  framework="pt" )
					# Using `do_sample=False` to force deterministic output
					_UpperCAmelCase		:       Any								=      text_generator("This is a test"	,  do_sample=lowerCamelCase__ )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        {
					            "generated_text": (
					                "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
					                " oscope. FiliFili@@"
					            )
					        }
					    ]	,  )

					_UpperCAmelCase		:       Dict								=      text_generator(["This is a test", "This is a second test"] )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        [
					            {
					                "generated_text": (
					                    "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope."
					                    " oscope. FiliFili@@"
					                )
					            }
					        ],
					        [
					            {
					                "generated_text": (
					                    "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy"
					                    " oscope. oscope. FiliFili@@"
					                )
					            }
					        ],
					    ]	,  )

					_UpperCAmelCase		:       int								=      text_generator("This is a test"	,  do_sample=lowerCamelCase__	,  num_return_sequences=2	,  return_tensors=lowerCamelCase__ )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        {"generated_token_ids": ANY(lowerCamelCase__ )},
					        {"generated_token_ids": ANY(lowerCamelCase__ )},
					    ]	,  )
					_UpperCAmelCase		:       Any								=      text_generator.model.config.eos_token_id
					_UpperCAmelCase		:       List[Any]								=      "<pad>"
					_UpperCAmelCase		:       Union[str, Any]								=      text_generator(
					    ["This is a test", "This is a second test"]	,  do_sample=lowerCamelCase__	,  num_return_sequences=2	,  batch_size=2	,  return_tensors=lowerCamelCase__	,  )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        [
					            {"generated_token_ids": ANY(lowerCamelCase__ )},
					            {"generated_token_ids": ANY(lowerCamelCase__ )},
					        ],
					        [
					            {"generated_token_ids": ANY(lowerCamelCase__ )},
					            {"generated_token_ids": ANY(lowerCamelCase__ )},
					        ],
					    ]	,  )




			@require_tf
			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      pipeline(task="text-generation"	,  model="sshleifer/tiny-ctrl"	,  framework="tf" )

					# Using `do_sample=False` to force deterministic output
					_UpperCAmelCase		:       Dict								=      text_generator("This is a test"	,  do_sample=lowerCamelCase__ )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        {
					            "generated_text": (
					                "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
					                " please,"
					            )
					        }
					    ]	,  )

					_UpperCAmelCase		:       Optional[Any]								=      text_generator(["This is a test", "This is a second test"]	,  do_sample=lowerCamelCase__ )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        [
					            {
					                "generated_text": (
					                    "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵"
					                    " please,"
					                )
					            }
					        ],
					        [
					            {
					                "generated_text": (
					                    "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes"
					                    " Cannes 閲閲Cannes Cannes Cannes 攵 please,"
					                )
					            }
					        ],
					    ]	,  )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     List[Any] )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      TextGenerationPipeline(model=lowerCamelCase__	,  tokenizer=lowerCamelCase__ )
					return text_generator, ["This is a test", "Another test"]




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      "Hello I believe in"
					_UpperCAmelCase		:       Optional[Any]								=      pipeline("text-generation"	,  model="hf-internal-testing/tiny-random-gpt2" )
					_UpperCAmelCase		:       Union[str, Any]								=      text_generator(lowerCamelCase__ )
					self.assertEqual(
					    lowerCamelCase__	,  [{"generated_text": "Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe"}]	,  )

					_UpperCAmelCase		:       Optional[Any]								=      text_generator(lowerCamelCase__	,  stop_sequence=" fe" )
					self.assertEqual(lowerCamelCase__	,  [{"generated_text": "Hello I believe in fe"}] )




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     int )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[str]								=      text_generator.model
					_UpperCAmelCase		:       List[str]								=      text_generator.tokenizer

					_UpperCAmelCase		:       List[Any]								=      text_generator("This is a test" )
					self.assertEqual(lowerCamelCase__	,  [{"generated_text": ANY(lowerCamelCase__ )}] )
					self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )

					_UpperCAmelCase		:       Dict								=      text_generator("This is a test"	,  return_full_text=lowerCamelCase__ )
					self.assertEqual(lowerCamelCase__	,  [{"generated_text": ANY(lowerCamelCase__ )}] )
					self.assertNotIn("This is a test"	,  outputs[0]["generated_text"] )

					_UpperCAmelCase		:       Optional[int]								=      pipeline(task="text-generation"	,  model=lowerCamelCase__	,  tokenizer=lowerCamelCase__	,  return_full_text=lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      text_generator("This is a test" )
					self.assertEqual(lowerCamelCase__	,  [{"generated_text": ANY(lowerCamelCase__ )}] )
					self.assertNotIn("This is a test"	,  outputs[0]["generated_text"] )

					_UpperCAmelCase		:       Dict								=      text_generator("This is a test"	,  return_full_text=lowerCamelCase__ )
					self.assertEqual(lowerCamelCase__	,  [{"generated_text": ANY(lowerCamelCase__ )}] )
					self.assertTrue(outputs[0]["generated_text"].startswith("This is a test" ) )

					_UpperCAmelCase		:       Any								=      text_generator(["This is great !", "Something else"]	,  num_return_sequences=2	,  do_sample=lowerCamelCase__ )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
					        [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
					    ]	,  )

					if text_generator.tokenizer.pad_token is not None:
							_UpperCAmelCase		:       Union[str, Any]								=      text_generator(
							    ["This is great !", "Something else"]	,  num_return_sequences=2	,  batch_size=2	,  do_sample=lowerCamelCase__ )
							self.assertEqual(
							    lowerCamelCase__	,  [
							        [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
							        [{"generated_text": ANY(lowerCamelCase__ )}, {"generated_text": ANY(lowerCamelCase__ )}],
							    ]	,  )

					with self.assertRaises(lowerCamelCase__ ):
							_UpperCAmelCase		:       Tuple								=      text_generator("test"	,  return_full_text=lowerCamelCase__	,  return_text=lowerCamelCase__ )
					with self.assertRaises(lowerCamelCase__ ):
							_UpperCAmelCase		:       Tuple								=      text_generator("test"	,  return_full_text=lowerCamelCase__	,  return_tensors=lowerCamelCase__ )
					with self.assertRaises(lowerCamelCase__ ):
							_UpperCAmelCase		:       Dict								=      text_generator("test"	,  return_text=lowerCamelCase__	,  return_tensors=lowerCamelCase__ )

					# Empty prompt is slighly special
					# it requires BOS token to exist.
					# Special case for Pegasus which will always append EOS so will
					# work even without BOS.
					if (
					    text_generator.tokenizer.bos_token_id is not None
					    or "Pegasus" in tokenizer.__class__.__name__
					    or "Git" in model.__class__.__name__
					):
							_UpperCAmelCase		:       List[Any]								=      text_generator("" )
							self.assertEqual(lowerCamelCase__	,  [{"generated_text": ANY(lowerCamelCase__ )}] )
					else:
							with self.assertRaises((ValueError, AssertionError) ):
									_UpperCAmelCase		:       int								=      text_generator("" )

					if text_generator.framework == "tf":
							# TF generation does not support max_new_tokens, and it's impossible
							# to control long generation with only max_length without
							# fancy calculation, dismissing tests for now.
							return
					# We don't care about infinite range models.
					# They already work.
					# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
					_UpperCAmelCase		:       Dict								=      ["RwkvForCausalLM", "XGLMForCausalLM", "GPTNeoXForCausalLM"]
					if (
					    tokenizer.model_max_length < 1_00_00
					    and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
					):
							# Handling of large generations
							with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
									text_generator("This is a test" * 5_00	,  max_new_tokens=20 )

							_UpperCAmelCase		:       Any								=      text_generator("This is a test" * 5_00	,  handle_long_generation="hole"	,  max_new_tokens=20 )
							# Hole strategy cannot work
							with self.assertRaises(lowerCamelCase__ ):
									text_generator(
									    "This is a test" * 5_00	,  handle_long_generation="hole"	,  max_new_tokens=tokenizer.model_max_length + 10	,  )




			@require_torch
			@require_accelerate
			@require_torch_gpu
			def 							lowerCAmelCase__    (		self  :     str )   ->Optional[int]:



					'''simple docstring'''







					import torch

					# Classic `model_kwargs`
					_UpperCAmelCase		:       Any								=      pipeline(
					    model="hf-internal-testing/tiny-random-bloom"	,  model_kwargs={"device_map": "auto", "torch_dtype": torch.bfloataa}	,  )
					self.assertEqual(pipe.model.device	,  torch.device(0 ) )
					self.assertEqual(pipe.model.lm_head.weight.dtype	,  torch.bfloataa )
					_UpperCAmelCase		:       Optional[int]								=      pipe("This is a test" )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        {
					            "generated_text": (
					                "This is a test test test test test test test test test test test test test test test test"
					                " test"
					            )
					        }
					    ]	,  )

					# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
					_UpperCAmelCase		:       List[str]								=      pipeline(model="hf-internal-testing/tiny-random-bloom"	,  device_map="auto"	,  torch_dtype=torch.bfloataa )
					self.assertEqual(pipe.model.device	,  torch.device(0 ) )
					self.assertEqual(pipe.model.lm_head.weight.dtype	,  torch.bfloataa )
					_UpperCAmelCase		:       Optional[Any]								=      pipe("This is a test" )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        {
					            "generated_text": (
					                "This is a test test test test test test test test test test test test test test test test"
					                " test"
					            )
					        }
					    ]	,  )

					# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
					_UpperCAmelCase		:       Dict								=      pipeline(model="hf-internal-testing/tiny-random-bloom"	,  device_map="auto" )
					self.assertEqual(pipe.model.device	,  torch.device(0 ) )
					self.assertEqual(pipe.model.lm_head.weight.dtype	,  torch.floataa )
					_UpperCAmelCase		:       int								=      pipe("This is a test" )
					self.assertEqual(
					    lowerCamelCase__	,  [
					        {
					            "generated_text": (
					                "This is a test test test test test test test test test test test test test test test test"
					                " test"
					            )
					        }
					    ]	,  )




			@require_torch
			@require_torch_gpu
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Any:



					'''simple docstring'''







					import torch

					_UpperCAmelCase		:       str								=      pipeline(model="hf-internal-testing/tiny-random-bloom"	,  device=0	,  torch_dtype=torch.floataa )
					pipe("This is a test" )




			@require_torch
			@require_accelerate
			@require_torch_gpu
			def 							lowerCAmelCase__    (		self  :     List[Any] )   ->Any:



					'''simple docstring'''







					import torch

					_UpperCAmelCase		:       Dict								=      pipeline(model="hf-internal-testing/tiny-random-bloom"	,  device_map="auto"	,  torch_dtype=torch.floataa )
					pipe("This is a test"	,  do_sample=lowerCamelCase__	,  top_p=0.5 )






			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      "Hello world"
					_UpperCAmelCase		:       int								=      pipeline("text-generation"	,  model="hf-internal-testing/tiny-random-gpt2" )
					if text_generator.model.framework == "tf":
							_UpperCAmelCase		:       Optional[int]								=      logging.get_logger("transformers.generation.tf_utils" )
					else:
							_UpperCAmelCase		:       Optional[Any]								=      logging.get_logger("transformers.generation.utils" )
					_UpperCAmelCase		:       str								=      "Both `max_new_tokens`"  # The beggining of the message to be checked in this test

					# Both are set by the user -> log warning
					with CaptureLogger(lowerCamelCase__ ) as cl:
							_UpperCAmelCase		:       Tuple								=      text_generator(lowerCamelCase__	,  max_length=10	,  max_new_tokens=1 )
					self.assertIn(lowerCamelCase__	,  cl.out )

					# The user only sets one -> no warning
					with CaptureLogger(lowerCamelCase__ ) as cl:
							_UpperCAmelCase		:       Optional[Any]								=      text_generator(lowerCamelCase__	,  max_new_tokens=1 )
					self.assertNotIn(lowerCamelCase__	,  cl.out )

					with CaptureLogger(lowerCamelCase__ ) as cl:
							_UpperCAmelCase		:       Union[str, Any]								=      text_generator(lowerCamelCase__	,  max_length=10 )
					self.assertNotIn(lowerCamelCase__	,  cl.out )







'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						)






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def __init__(		self  :     int	,  lowerCamelCase__  :     str	,  lowerCamelCase__  :     str=13	,  lowerCamelCase__  :     Dict=7	,  lowerCamelCase__  :     str=True	,  lowerCamelCase__  :     List[str]=True	,  lowerCamelCase__  :     Dict=True	,  lowerCamelCase__  :     int=True	,  lowerCamelCase__  :     Tuple=99	,  lowerCamelCase__  :     Optional[int]=32	,  lowerCamelCase__  :     str=5	,  lowerCamelCase__  :     List[Any]=4	,  lowerCamelCase__  :     Any=37	,  lowerCamelCase__  :     List[Any]="gelu"	,  lowerCamelCase__  :     int=0.1	,  lowerCamelCase__  :     Tuple=0.1	,  lowerCamelCase__  :     Optional[int]=5_12	,  lowerCamelCase__  :     Any=16	,  lowerCamelCase__  :     Optional[Any]=2	,  lowerCamelCase__  :     Optional[Any]=0.0_2	,  lowerCamelCase__  :     Optional[int]=4	,  )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      parent
					_UpperCAmelCase		:       Optional[int]								=      batch_size
					_UpperCAmelCase		:       List[Any]								=      seq_length
					_UpperCAmelCase		:       Dict								=      is_training
					_UpperCAmelCase		:       int								=      use_attention_mask
					_UpperCAmelCase		:       List[Any]								=      use_token_type_ids
					_UpperCAmelCase		:       int								=      use_labels
					_UpperCAmelCase		:       str								=      vocab_size
					_UpperCAmelCase		:       Tuple								=      hidden_size
					_UpperCAmelCase		:       Dict								=      num_hidden_layers
					_UpperCAmelCase		:       List[Any]								=      num_attention_heads
					_UpperCAmelCase		:       Tuple								=      intermediate_size
					_UpperCAmelCase		:       List[Any]								=      hidden_act
					_UpperCAmelCase		:       Union[str, Any]								=      hidden_dropout_prob
					_UpperCAmelCase		:       List[str]								=      attention_probs_dropout_prob
					_UpperCAmelCase		:       Optional[int]								=      max_position_embeddings
					_UpperCAmelCase		:       Tuple								=      type_vocab_size
					_UpperCAmelCase		:       int								=      type_sequence_label_size
					_UpperCAmelCase		:       List[str]								=      initializer_range
					_UpperCAmelCase		:       Union[str, Any]								=      num_choices




			def 							lowerCAmelCase__    (		self  :     int )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

					_UpperCAmelCase		:       Any								=      None
					if self.use_attention_mask:
							_UpperCAmelCase		:       Optional[Any]								=      random_attention_mask([self.batch_size, self.seq_length] )

					_UpperCAmelCase		:       int								=      None
					if self.use_token_type_ids:
							_UpperCAmelCase		:       List[Any]								=      ids_tensor([self.batch_size, self.seq_length]	,  self.type_vocab_size )

					_UpperCAmelCase		:       Tuple								=      RobertaPreLayerNormConfig(
					    vocab_size=self.vocab_size	,  hidden_size=self.hidden_size	,  num_hidden_layers=self.num_hidden_layers	,  num_attention_heads=self.num_attention_heads	,  intermediate_size=self.intermediate_size	,  hidden_act=self.hidden_act	,  hidden_dropout_prob=self.hidden_dropout_prob	,  attention_probs_dropout_prob=self.attention_probs_dropout_prob	,  max_position_embeddings=self.max_position_embeddings	,  type_vocab_size=self.type_vocab_size	,  is_decoder=lowerCamelCase__	,  initializer_range=self.initializer_range	,  )

					return config, input_ids, token_type_ids, attention_mask




			def 							lowerCAmelCase__    (		self  :     Dict )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      config_and_inputs
					_UpperCAmelCase		:       Optional[Any]								=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict






			def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.prepare_config_and_inputs()
					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      config_and_inputs

					_UpperCAmelCase		:       List[Any]								=      True
					_UpperCAmelCase		:       List[str]								=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
					_UpperCAmelCase		:       int								=      ids_tensor([self.batch_size, self.seq_length]	,  vocab_size=2 )

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	Tuple          =     True

			lowerCAmelCase :	Tuple          =     (
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      FlaxRobertaPreLayerNormModelTester(self )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->int:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:       Any								=      model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      model(np.ones((1, 1) ) )
							self.assertIsNotNone(lowerCamelCase__ )






@require_flax
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@slow
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       str								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Tuple								=      model(lowerCamelCase__ )[0]
					_UpperCAmelCase		:       int								=      [1, 11, 5_02_65]
					self.assertEqual(list(output.shape )	,  lowerCamelCase__ )
					# compare the actual values for a slice.
					_UpperCAmelCase		:       int								=      np.array(
					    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )






			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"	,  from_pt=lowerCamelCase__ )

					_UpperCAmelCase		:       List[Any]								=      np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	,  dtype=jnp.intaa )
					_UpperCAmelCase		:       Optional[Any]								=      model(lowerCamelCase__ )[0]
					# compare the actual values for a slice.
					_UpperCAmelCase		:       str								=      np.array(
					    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]	,  dtype=np.floataa )
					self.assertTrue(np.allclose(output[:, :3, :3]	,  lowerCamelCase__	,  atol=1E-4 ) )



