'''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 	__A				(			unittest.TestCase     ):



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



				def  _lowercase						(self   :   str			):
									UpperCAmelCase_	   =       ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size			)

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

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

									UpperCAmelCase_	   =       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]			):
									UpperCAmelCase_	   =       self.prepare_config_and_inputs()
									UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       config_and_inputs
									UpperCAmelCase_	   =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
									return config, inputs_dict






				def  _lowercase						(self   :   Union[str, Any]			):
									UpperCAmelCase_	   =       self.prepare_config_and_inputs()
									UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       config_and_inputs

									UpperCAmelCase_	   =       True
									UpperCAmelCase_	   =       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
									UpperCAmelCase_	   =       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     ):
				a__	:						Tuple	       =  True

				a__	:						Union[str, Any]	       =  (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)



				def  _lowercase						(self   :   List[Any]			):
									UpperCAmelCase_	   =       FlaxRobertaPreLayerNormModelTester(self			)






				@slow
				def  _lowercase						(self   :   Optional[Any]			):

									for model_class_name in self.all_model_classes:
														UpperCAmelCase_	   =       model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,      from_pt=lowerCamelCase_			)
														UpperCAmelCase_	   =       model(np.ones((1, 1)			)			)
														self.assertIsNotNone(lowerCamelCase_			)




@require_flax
class 	__A				(			unittest.TestCase     ):



				@slow
				def  _lowercase						(self   :   List[Any]			):
									UpperCAmelCase_	   =       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,      from_pt=lowerCamelCase_			)

									UpperCAmelCase_	   =       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       ,      dtype=jnp.intaa			)
									UpperCAmelCase_	   =       model(lowerCamelCase_			)[0]
									UpperCAmelCase_	   =       [1, 11, 50265]
									self.assertEqual(list(output.shape			)       ,      lowerCamelCase_			)
									# compare the actual values for a slice.
									UpperCAmelCase_	   =       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  _lowercase						(self   :   Optional[int]			):
									UpperCAmelCase_	   =       FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"       ,      from_pt=lowerCamelCase_			)

									UpperCAmelCase_	   =       np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]       ,      dtype=jnp.intaa			)
									UpperCAmelCase_	   =       model(lowerCamelCase_			)[0]
									# compare the actual values for a slice.
									UpperCAmelCase_	   =       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			)			)





'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




