






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate

@dataclass(frozen=lowercase	)
class        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   str        =			field(default="""image-classification"""						,      metadata={"""include_in_asdict_even_if_is_default""": True}	)
				_snake_case				:   ClassVar[Features]        =			Features({"""image""": Image()}	)
				_snake_case				:   ClassVar[Features]        =			Features({"""labels""": ClassLabel}	)
				_snake_case				:   str        =			"image"
				_snake_case				:   str        =			"labels"







				def 				__a   ( self     :Any  ,     lowerCamelCase__     :Dict     ):
					if self.label_column not in features:
						raise ValueError(f"""Column {self.label_column} is not present in features."""     )
					if not isinstance(features[self.label_column]  ,     lowerCamelCase__     ):
						raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""     )
					UpperCamelCase__ :Union[str, Any]						=      copy.deepcopy(self     )
					UpperCamelCase__ :Any						=      self.label_schema.copy()
					UpperCamelCase__ :int						=      features[self.label_column]
					UpperCamelCase__ :Optional[Any]						=      label_schema
					return task_template







				@property
				def 				__a   ( self     :List[Any]     ):
					return {
					    self.image_column: "image",
					    self.label_column: "labels",
					}







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  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Dict=13,    lowerCamelCase		:       Optional[int]=7,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Union[str, Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       List[Any]=99,    lowerCamelCase		:       Any=32,    lowerCamelCase		:       List[str]=5,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       Union[str, Any]=37,    lowerCamelCase		:       Optional[int]="gelu",    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       List[str]=0.1,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       int=16,    lowerCamelCase		:       str=2,    lowerCamelCase		:       int=0.02,    lowerCamelCase		:       int=4,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     seq_length
								lowercase__  					=     is_training
								lowercase__  					=     use_attention_mask
								lowercase__  					=     use_token_type_ids
								lowercase__  					=     use_labels
								lowercase__  					=     vocab_size
								lowercase__  					=     hidden_size
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     intermediate_size
								lowercase__  					=     hidden_act
								lowercase__  					=     hidden_dropout_prob
								lowercase__  					=     attention_probs_dropout_prob
								lowercase__  					=     max_position_embeddings
								lowercase__  					=     type_vocab_size
								lowercase__  					=     type_sequence_label_size
								lowercase__  					=     initializer_range
								lowercase__  					=     num_choices




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.vocab_size    )

								lowercase__  					=     None
								if self.use_attention_mask:
												lowercase__  					=     random_attention_mask([self.batch_size, self.seq_length]    )

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

								lowercase__  					=     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 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs
								lowercase__  					=     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs

								lowercase__  					=     True
								lowercase__  					=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								lowercase__  					=     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  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			True

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




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModelTester(self    )




				@slow
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								for model_class_name in self.all_model_classes:
												lowercase__  					=     model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )
												lowercase__  					=     model(np.ones((1, 1)    )    )
												self.assertIsNotNone(lowerCamelCase    )



@require_flax
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@slow
				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								lowercase__  					=     [1, 11, 50_265]
								self.assertEqual(list(output.shape    ),    lowerCamelCase    )
								# compare the actual values for a slice.
								lowercase__  					=     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],    lowerCamelCase,    atol=1E-4    )    )




				@slow
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								# compare the actual values for a slice.
								lowercase__  					=     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],    lowerCamelCase,    atol=1E-4    )    )




