
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 			__A( unittest.TestCase			):




				def __init__(     self		,     _snake_case		,     _snake_case=13		,     _snake_case=7		,     _snake_case=True		,     _snake_case=True		,     _snake_case=True		,     _snake_case=True		,     _snake_case=99		,     _snake_case=32		,     _snake_case=5		,     _snake_case=4		,     _snake_case=37		,     _snake_case="gelu"		,     _snake_case=0.1		,     _snake_case=0.1		,     _snake_case=512		,     _snake_case=16		,     _snake_case=2		,     _snake_case=0.02		,     _snake_case=4		,     )    ->      Optional[int]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size				)

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

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

								__a										=    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=_snake_case		,     initializer_range=self.initializer_range		,     )

								return config, input_ids, token_type_ids, attention_mask




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[Any]:


								'''simple docstring'''




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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    self.prepare_config_and_inputs()
								__a							,     __a							,     __a							,     __a										=    config_and_inputs

								__a										=    True
								__a										=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
								__a										=    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 			__A( a  ,   unittest.TestCase			):
				snake_case_						   =    True

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




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      str:


								'''simple docstring'''




								__a										=    FlaxRobertaModelTester(self				)




				@slow
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								for model_class_name in self.all_model_classes:
												__a										=    model_class_name.from_pretrained('''roberta-base'''		,     from_pt=_snake_case				)
												__a										=    model(np.ones((1, 1)				)				)
												self.assertIsNotNone(_snake_case				)

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


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

A     :	Optional[int]    = {
    'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}







class 			__A( a			):
				snake_case_						   =    '''levit'''




				def __init__(     self		,     _snake_case=224		,     _snake_case=3		,     _snake_case=3		,     _snake_case=2		,     _snake_case=1		,     _snake_case=16		,     _snake_case=[128, 256, 384]		,     _snake_case=[4, 8, 12]		,     _snake_case=[4, 4, 4]		,     _snake_case=[16, 16, 16]		,     _snake_case=0		,     _snake_case=[2, 2, 2]		,     _snake_case=[2, 2, 2]		,     _snake_case=0.02		,     **_snake_case		,     )    ->      Optional[Any]:


								'''simple docstring'''




								super().__init__(**_snake_case				)
								__a										=    image_size
								__a										=    num_channels
								__a										=    kernel_size
								__a										=    stride
								__a										=    padding
								__a										=    hidden_sizes
								__a										=    num_attention_heads
								__a										=    depths
								__a										=    key_dim
								__a										=    drop_path_rate
								__a										=    patch_size
								__a										=    attention_ratio
								__a										=    mlp_ratio
								__a										=    initializer_range
								__a										=    [
								    ['''Subsample''', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
								    ['''Subsample''', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
								]







class 			__A( a			):
				snake_case_						   =    version.parse('''1.11'''			)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Mapping[str, Mapping[int, str]]:


								'''simple docstring'''




								return OrderedDict(
								    [
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								    ]				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      float:


								'''simple docstring'''




								return 1E-4