


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     a		(unittest.TestCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     lowerCamelCase	:  List[str]   ,     lowerCamelCase	:  Union[str, Any]=13   ,     lowerCamelCase	:  Dict=7   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  Dict=True   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  Union[str, Any]=True   ,     lowerCamelCase	:  str=99   ,     lowerCamelCase	:  Optional[int]=32   ,     lowerCamelCase	:  Union[str, Any]=5   ,     lowerCamelCase	:  Optional[int]=4   ,     lowerCamelCase	:  Optional[int]=37   ,     lowerCamelCase	:  Union[str, Any]="gelu"   ,     lowerCamelCase	:  int=0.1   ,     lowerCamelCase	:  Union[str, Any]=0.1   ,     lowerCamelCase	:  Optional[int]=512   ,     lowerCamelCase	:  List[str]=16   ,     lowerCamelCase	:  Dict=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  Dict=4   ,     )     ->   Optional[Any]:
														__snake_case		:      Tuple										=       parent
														__snake_case		:      Any										=       batch_size
														__snake_case		:      List[Any]										=       seq_length
														__snake_case		:      Any										=       is_training
														__snake_case		:      Optional[int]										=       use_attention_mask
														__snake_case		:      Any										=       use_token_type_ids
														__snake_case		:      Tuple										=       use_labels
														__snake_case		:      List[str]										=       vocab_size
														__snake_case		:      Tuple										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      str										=       num_attention_heads
														__snake_case		:      Union[str, Any]										=       intermediate_size
														__snake_case		:      Optional[int]										=       hidden_act
														__snake_case		:      List[str]										=       hidden_dropout_prob
														__snake_case		:      Any										=       attention_probs_dropout_prob
														__snake_case		:      Dict										=       max_position_embeddings
														__snake_case		:      Any										=       type_vocab_size
														__snake_case		:      int										=       type_sequence_label_size
														__snake_case		:      str										=       initializer_range
														__snake_case		:      str										=       num_choices

							def    __snake_case (							self	:  Tuple    )     ->   Union[str, Any]:
														__snake_case		:      List[str]										=       ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size    )

														__snake_case		:      Optional[int]										=       None
														if self.use_attention_mask:
																					__snake_case		:      Dict										=       random_attention_mask([self.batch_size, self.seq_length]    )

														__snake_case		:      str										=       None
														if self.use_token_type_ids:
																					__snake_case		:      Tuple										=       ids_tensor([self.batch_size, self.seq_length]   ,     self.type_vocab_size    )

														__snake_case		:      Union[str, Any]										=       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    __snake_case (							self	:  List[Any]    )     ->   Dict:
														__snake_case		:      Dict										=       self.prepare_config_and_inputs()
														__snake_case		:      List[Any]										=       config_and_inputs
														__snake_case		:      Dict										=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
														return config, inputs_dict






							def    __snake_case (							self	:  List[str]    )     ->   Optional[int]:
														__snake_case		:      Union[str, Any]										=       self.prepare_config_and_inputs()
														__snake_case		:      List[Any]										=       config_and_inputs

														__snake_case		:      Tuple										=       True
														__snake_case		:      Optional[int]										=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
														__snake_case		:      Optional[Any]										=       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     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							str					  =  True

							__UpperCAmelCase   :							List[Any]					  =  (
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def    __snake_case (							self	:  Tuple    )     ->   Tuple:
														__snake_case		:      Tuple										=       FlaxRobertaPreLayerNormModelTester(self    )






							@slow
							def    __snake_case (							self	:  Optional[Any]    )     ->   str:
														for model_class_name in self.all_model_classes:
																					__snake_case		:      str										=       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,     from_pt=lowerCamelCase    )
																					__snake_case		:      Tuple										=       model(np.ones((1, 1)    )    )
																					self.assertIsNotNone(lowerCamelCase    )

@require_flax
class     a		(unittest.TestCase				):




							"""simple docstring"""


							@slow
							def    __snake_case (							self	:  List[Any]    )     ->   Optional[Any]:
														__snake_case		:      Dict										=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,     from_pt=lowerCamelCase    )

														__snake_case		:      List[Any]										=       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   ,     dtype=jnp.intaa    )
														__snake_case		:      Optional[Any]										=       model(lowerCamelCase    )[0]
														__snake_case		:      List[str]										=       [1, 11, 50265]
														self.assertEqual(list(output.shape    )   ,     lowerCamelCase    )
														# compare the actual values for a slice.
														__snake_case		:      List[Any]										=       np.array(
														    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]   ,     dtype=np.floataa    )
														self.assertTrue(np.allclose(output[:, :3, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )






							@slow
							def    __snake_case (							self	:  List[str]    )     ->   Optional[Any]:
														__snake_case		:      Union[str, Any]										=       FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"   ,     from_pt=lowerCamelCase    )

														__snake_case		:      str										=       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]   ,     dtype=jnp.intaa    )
														__snake_case		:      List[Any]										=       model(lowerCamelCase    )[0]
														# compare the actual values for a slice.
														__snake_case		:      List[str]										=       np.array(
														    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]   ,     dtype=np.floataa    )
														self.assertTrue(np.allclose(output[:, :3, :3]   ,     lowerCamelCase   ,     atol=1E-4    )    )



from ..utils import DummyObject, requires_backends

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							int					  =  ["speech"]

							def __init__(							self	:  List[Any]   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														requires_backends(self   ,     ["speech"]    )

class     a		(metaclass=_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[Any]					  =  ["speech"]

							def __init__(							self	:  int   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  List[Any]    )     ->   Optional[int]:
														requires_backends(self   ,     ["speech"]    )

