




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      ):

						def __init__(		self :Optional[int]     ,			__snake_case :Dict     ,			__snake_case :Union[str, Any]=13     ,			__snake_case :Dict=7     ,			__snake_case :Dict=True     ,			__snake_case :Dict=True     ,			__snake_case :Union[str, Any]=True     ,			__snake_case :Any=True     ,			__snake_case :List[Any]=99     ,			__snake_case :Dict=32     ,			__snake_case :Union[str, Any]=5     ,			__snake_case :List[Any]=4     ,			__snake_case :Optional[Any]=37     ,			__snake_case :List[str]="gelu"     ,			__snake_case :str=0.1     ,			__snake_case :Tuple=0.1     ,			__snake_case :Optional[Any]=5_12     ,			__snake_case :Union[str, Any]=16     ,			__snake_case :Optional[Any]=2     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Optional[int]=4     ,			):
										'''simple docstring'''

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

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size			)

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

										__magic_name__						:						str										=None
										if self.use_token_type_ids:
														__magic_name__						:						Union[str, Any]										=ids_tensor([self.batch_size, self.seq_length]     ,			self.type_vocab_size			)

										__magic_name__						:						Dict										=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=__snake_case     ,			initializer_range=self.initializer_range     ,			)

										return config, input_ids, token_type_ids, attention_mask

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

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


						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=self.prepare_config_and_inputs()
										__magic_name__					,			__magic_name__					,			__magic_name__					,			__magic_name__						:						Dict										=config_and_inputs

										__magic_name__						:						int										=True
										__magic_name__						:						int										=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
										__magic_name__						:						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 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       True

						UpperCamelCase      =       (
						    (
						        FlaxRobertaPreLayerNormModel,
						        FlaxRobertaPreLayerNormForCausalLM,
						        FlaxRobertaPreLayerNormForMaskedLM,
						        FlaxRobertaPreLayerNormForSequenceClassification,
						        FlaxRobertaPreLayerNormForTokenClassification,
						        FlaxRobertaPreLayerNormForMultipleChoice,
						        FlaxRobertaPreLayerNormForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=FlaxRobertaPreLayerNormModelTester(self			)


						@slow
						def      A__    (		self :Tuple			):
										'''simple docstring'''

										for model_class_name in self.all_model_classes:
														__magic_name__						:						List[str]										=model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""     ,			from_pt=__snake_case			)
														__magic_name__						:						Any										=model(np.ones((1, 1)			)			)
														self.assertIsNotNone(__snake_case			)




@require_flax
class 		__A   (      unittest.TestCase      ):

						@slow
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""     ,			from_pt=__snake_case			)

										__magic_name__						:						int										=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			)
										__magic_name__						:						Optional[int]										=model(__snake_case			)[0]
										__magic_name__						:						str										=[1, 11, 5_02_65]
										self.assertEqual(list(output.shape			)     ,			__snake_case			)
										# compare the actual values for a slice.
										__magic_name__						:						List[Any]										=np.array(
										    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,			dtype=np.floataa			)
										self.assertTrue(np.allclose(output[:, :3, :3]     ,			__snake_case     ,			atol=1E-4			)			)


						@slow
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""     ,			from_pt=__snake_case			)

										__magic_name__						:						int										=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			)
										__magic_name__						:						List[Any]										=model(__snake_case			)[0]
										# compare the actual values for a slice.
										__magic_name__						:						List[Any]										=np.array(
										    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,			dtype=np.floataa			)
										self.assertTrue(np.allclose(output[:, :3, :3]     ,			__snake_case     ,			atol=1E-4			)			)












# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase      :       Dict    =						abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_addoption_shared

     pytest_addoption_shared(__snake_case							)





def    __lowerCAmelCase   (    __snake_case							):
     from diffusers.utils.testing_utils import pytest_terminal_summary_main

     __lowerCAmelCase             =       terminalreporter.config.getoption("--make-reports"							)
     if make_reports:
          pytest_terminal_summary_main(__snake_case       ,			id=__snake_case							)






