

'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaConfig, 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():
						from transformers.models.roberta.modeling_flax_roberta import (
						    FlaxRobertaForCausalLM,
						    FlaxRobertaForMaskedLM,
						    FlaxRobertaForMultipleChoice,
						    FlaxRobertaForQuestionAnswering,
						    FlaxRobertaForSequenceClassification,
						    FlaxRobertaForTokenClassification,
						    FlaxRobertaModel,
						)




class      __UpperCamelCase       (unittest.TestCase			):







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=7 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=99 ,				_lowerCAmelCase=32 ,				_lowerCAmelCase=5 ,				_lowerCAmelCase=4 ,				_lowerCAmelCase=37 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=512 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=2 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=4 ,				)    ->    List[Any]:
							'''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   _a   (    self						)    ->    Any:
							'''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        =							RobertaConfig(
							    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   _a   (    self						)    ->    Any:
							'''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   _a   (    self						)    ->    List[str]:
							'''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
class      __UpperCamelCase       (_UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					True

	__A			  =					(
	    (
	        FlaxRobertaModel,
	        FlaxRobertaForCausalLM,
	        FlaxRobertaForMaskedLM,
	        FlaxRobertaForSequenceClassification,
	        FlaxRobertaForTokenClassification,
	        FlaxRobertaForMultipleChoice,
	        FlaxRobertaForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							FlaxRobertaModelTester(self						)







	@slow
	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							for model_class_name in self.all_model_classes:
													lowercase        =							model_class_name.from_pretrained("""roberta-base""" ,				from_pt=_lowerCAmelCase						)
													lowercase        =							model(np.ones((1, 1)						)						)
													self.assertIsNotNone(_lowerCAmelCase						)







'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							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        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


