


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Any					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			List[Any]=13					,      __lowerCamelCase							:			int=30					,      __lowerCamelCase							:			Dict=2					,      __lowerCamelCase							:			Optional[int]=3					,      __lowerCamelCase							:			List[str]=True					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			int=32					,      __lowerCamelCase							:			Dict=2					,      __lowerCamelCase							:			List[str]=4					,      __lowerCamelCase							:			List[Any]=37					,      __lowerCamelCase							:			Union[str, Any]="gelu"					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			Optional[int]=0.1					,      __lowerCamelCase							:			int=10					,      __lowerCamelCase							:			str=0.0_2					,      __lowerCamelCase							:			Dict=3					,      __lowerCamelCase							:			Optional[int]=0.6					,      __lowerCamelCase							:			List[Any]=None					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  parent
             lowerCamelCase__		:				Tuple    =  batch_size
             lowerCamelCase__		:				Tuple    =  image_size
             lowerCamelCase__		:				Optional[int]    =  patch_size
             lowerCamelCase__		:				str    =  num_channels
             lowerCamelCase__		:				Union[str, Any]    =  is_training
             lowerCamelCase__		:				List[str]    =  use_labels
             lowerCamelCase__		:				Any    =  hidden_size
             lowerCamelCase__		:				Union[str, Any]    =  num_hidden_layers
             lowerCamelCase__		:				int    =  num_attention_heads
             lowerCamelCase__		:				Tuple    =  intermediate_size
             lowerCamelCase__		:				Optional[int]    =  hidden_act
             lowerCamelCase__		:				Tuple    =  hidden_dropout_prob
             lowerCamelCase__		:				Optional[Any]    =  attention_probs_dropout_prob
             lowerCamelCase__		:				Any    =  type_sequence_label_size
             lowerCamelCase__		:				Union[str, Any]    =  initializer_range
             lowerCamelCase__		:				Union[str, Any]    =  mask_ratio
             lowerCamelCase__		:				Optional[Any]    =  scope

             # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
             # (we add 1 for the [CLS] token)
             lowerCamelCase__		:				str    =  (image_size // patch_size) ** 2
             lowerCamelCase__		:				Tuple    =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)       )       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

             lowerCamelCase__		:				Optional[Any]    =  None
             if self.use_labels:
                   lowerCamelCase__		:				Any    =  ids_tensor([self.batch_size]					,      self.type_sequence_label_size       )

             lowerCamelCase__		:				str    =  self.get_config()

             return config, pixel_values, labels





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             return ViTMAEConfig(
                 image_size=self.image_size					,      patch_size=self.patch_size					,      num_channels=self.num_channels					,      hidden_size=self.hidden_size					,      num_hidden_layers=self.num_hidden_layers					,      num_attention_heads=self.num_attention_heads					,      intermediate_size=self.intermediate_size					,      decoder_hidden_size=self.hidden_size					,      decoder_num_hidden_layers=self.num_hidden_layers					,      decoder_num_attention_heads=self.num_attention_heads					,      decoder_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					,      is_decoder=__lowerCamelCase					,      initializer_range=self.initializer_range					,      mask_ratio=self.mask_ratio					,      )





       def 						lowerCAmelCase     (   self							:			Tuple					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  TFViTMAEModel(config=__lowerCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  model(__lowerCamelCase					,      training=__lowerCamelCase       )
             self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			List[Any]					,      __lowerCamelCase							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  TFViTMAEForPreTraining(__lowerCamelCase       )
             lowerCamelCase__		:				List[str]    =  model(__lowerCamelCase					,      training=__lowerCamelCase       )
             # expected sequence length = num_patches
             lowerCamelCase__		:				Optional[Any]    =  (self.image_size // self.patch_size) ** 2
             lowerCamelCase__		:				str    =  self.patch_size**2 * self.num_channels
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)       )

             # test greyscale images
             lowerCamelCase__		:				Union[str, Any]    =  1
             lowerCamelCase__		:				Optional[Any]    =  TFViTMAEForPreTraining(__lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
             lowerCamelCase__		:				List[str]    =  model(__lowerCamelCase					,      training=__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  self.patch_size**2
             self.parent.assertEqual(result.logits.shape					,      (self.batch_size, num_patches, expected_num_channels)       )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.prepare_config_and_inputs()
             ((lowerCamelCase__)    ,   (lowerCamelCase__)    ,   (lowerCamelCase__))		:				Any    =  config_and_inputs
             lowerCamelCase__		:				Union[str, Any]    =  {"pixel_values": pixel_values}
             return config, inputs_dict







@require_tf
class 			_lowercase (							snake_case_							,  snake_case_							,  unittest.TestCase):


       """simple docstring"""




       A__											=			(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
       A__											=			{"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

       A__											=			False
       A__											=			False
       A__											=			False
       A__											=			False





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  TFViTMAEModelTester(self       )
             lowerCamelCase__		:				Optional[Any]    =  ConfigTester(self					,      config_class=__lowerCamelCase					,      has_text_modality=__lowerCamelCase					,      hidden_size=37       )





       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       @unittest.skip(reason="ViTMAE does not use inputs_embeds"       )
       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Union[str, Any]    =  model_class(__lowerCamelCase       )
                   self.assertIsInstance(model.get_input_embeddings()					,      (tf.keras.layers.Layer)       )
                   lowerCamelCase__		:				List[Any]    =  model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(__lowerCamelCase					,      tf.keras.layers.Layer       )       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Optional[int]    =  model_class(__lowerCamelCase       )
                   lowerCamelCase__		:				Optional[Any]    =  inspect.signature(model.call       )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   lowerCamelCase__		:				List[Any]    =  [*signature.parameters.keys()]

                   lowerCamelCase__		:				List[Any]    =  ["pixel_values"]
                   self.assertListEqual(arg_names[:1]					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Dict       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__    ,   lowerCamelCase__		:				Union[str, Any]    =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:				List[str]    =  int((config.image_size // config.patch_size) ** 2       )
             lowerCamelCase__		:				Union[str, Any]    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Optional[int]    =  model_class(__lowerCamelCase       )
                   lowerCamelCase__		:				Dict    =  self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				Dict    =  model(__lowerCamelCase					,      noise=__lowerCamelCase       )

                   lowerCamelCase__		:				Optional[Any]    =  copy.deepcopy(self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )       )
                   lowerCamelCase__		:				Any    =  model(**__lowerCamelCase					,      noise=__lowerCamelCase       )
                   lowerCamelCase__		:				Tuple    =  outputs_dict[0].numpy()
                   lowerCamelCase__		:				str    =  outputs_keywords[0].numpy()

                   self.assertLess(np.sum(np.abs(output_dict - output_keywords       )       )					,      1E-6       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__    ,   lowerCamelCase__		:				Any    =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:				Any    =  int((config.image_size // config.patch_size) ** 2       )
             lowerCamelCase__		:				Any    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

             def prepare_numpy_arrays(__lowerCamelCase							:			Tuple       ):
                   lowerCamelCase__		:				Dict    =  {}
                   for k, v in inputs_dict.items():
                         if tf.is_tensor(__lowerCamelCase       ):
                               lowerCamelCase__		:				int    =  v.numpy()
                         else:
                               lowerCamelCase__		:				Union[str, Any]    =  np.array(__lowerCamelCase       )

                   return inputs_np_dict

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				str    =  model_class(__lowerCamelCase       )

                   lowerCamelCase__		:				Tuple    =  self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )
                   lowerCamelCase__		:				int    =  prepare_numpy_arrays(__lowerCamelCase       )

                   lowerCamelCase__		:				str    =  model(__lowerCamelCase					,      noise=__lowerCamelCase       )
                   lowerCamelCase__		:				List[Any]    =  model(**__lowerCamelCase					,      noise=__lowerCamelCase       )
                   self.assert_outputs_same(__lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      __lowerCamelCase							:			Optional[int]					,      __lowerCamelCase							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__		:				int    =  int((tf_model.config.image_size // tf_model.config.patch_size) ** 2       )
             lowerCamelCase__		:				Union[str, Any]    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
             lowerCamelCase__		:				List[Any]    =  tf.constant(__lowerCamelCase       )

             # Add `noise` argument.
             # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
             lowerCamelCase__		:				Optional[Any]    =  tf_noise

             super().check_pt_tf_models(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:				Union[str, Any]    =  {
                 module_member
                 for model_class in self.all_model_classes
                 for module in (import_module(model_class.__module__       ),)
                 for module_member_name in dir(__lowerCamelCase       )
                 if module_member_name.endswith("MainLayer"       )
                 # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
                 and module_member_name[: -len("MainLayer"       )] == model_class.__name__[: -len("Model"       )]
                 for module_member in (getattr(__lowerCamelCase					,      __lowerCamelCase       ),)
                 if isinstance(__lowerCamelCase					,      __lowerCamelCase       )
                 and tf.keras.layers.Layer in module_member.__bases__
                 and getattr(__lowerCamelCase					,      "_keras_serializable"					,      __lowerCamelCase       )
             }

             lowerCamelCase__		:				Any    =  int((config.image_size // config.patch_size) ** 2       )
             lowerCamelCase__		:				Tuple    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )
             lowerCamelCase__		:				List[Any]    =  tf.convert_to_tensor(__lowerCamelCase       )
             inputs_dict.update({"noise": noise}       )

             for main_layer_class in tf_main_layer_classes:
                   lowerCamelCase__		:				int    =  main_layer_class(__lowerCamelCase       )

                   lowerCamelCase__		:				str    =  {
                       name: tf.keras.Input(tensor.shape[1:]					,      dtype=tensor.dtype       ) for name, tensor in inputs_dict.items()
                   }

                   lowerCamelCase__		:				int    =  tf.keras.Model(__lowerCamelCase					,      outputs=main_layer(__lowerCamelCase       )       )
                   lowerCamelCase__		:				Optional[Any]    =  model(__lowerCamelCase       )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         lowerCamelCase__		:				int    =  os.path.join(__lowerCamelCase					,      "keras_model.h5"       )
                         model.save(__lowerCamelCase       )
                         lowerCamelCase__		:				Union[str, Any]    =  tf.keras.models.load_model(
                             __lowerCamelCase					,      custom_objects={main_layer_class.__name__: main_layer_class}       )
                         assert isinstance(__lowerCamelCase					,      tf.keras.Model       )
                         lowerCamelCase__		:				Any    =  model(__lowerCamelCase       )
                         self.assert_outputs_same(__lowerCamelCase					,      __lowerCamelCase       )





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


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:				Union[str, Any]    =  int((config.image_size // config.patch_size) ** 2       )
             lowerCamelCase__		:				Any    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				Union[str, Any]    =  model_class(__lowerCamelCase       )
                   lowerCamelCase__		:				Optional[Any]    =  self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )
                   lowerCamelCase__		:				Optional[Any]    =  model(__lowerCamelCase					,      noise=__lowerCamelCase       )

                   if model_class.__name__ == "TFViTMAEModel":
                         lowerCamelCase__		:				Dict    =  outputs.last_hidden_state.numpy()
                         lowerCamelCase__		:				List[str]    =  0
                   else:
                         lowerCamelCase__		:				Union[str, Any]    =  outputs.logits.numpy()
                         lowerCamelCase__		:				Union[str, Any]    =  0

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         model.save_pretrained(__lowerCamelCase					,      saved_model=__lowerCamelCase       )
                         lowerCamelCase__		:				Any    =  model_class.from_pretrained(__lowerCamelCase       )
                         lowerCamelCase__		:				Dict    =  model(__lowerCamelCase					,      noise=__lowerCamelCase       )

                         if model_class.__name__ == "TFViTMAEModel":
                               lowerCamelCase__		:				Any    =  after_outputs["last_hidden_state"].numpy()
                               lowerCamelCase__		:				List[str]    =  0
                         else:
                               lowerCamelCase__		:				List[Any]    =  after_outputs["logits"].numpy()
                               lowerCamelCase__		:				int    =  0

                         lowerCamelCase__		:				Union[str, Any]    =  np.amax(np.abs(out_a - out_a       )       )
                         self.assertLessEqual(__lowerCamelCase					,      1E-5       )





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  self.model_tester.prepare_config_and_inputs_for_common()

             lowerCamelCase__		:				Optional[Any]    =  int((config.image_size // config.patch_size) ** 2       )
             lowerCamelCase__		:				Optional[Any]    =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)       )

             for model_class in self.all_model_classes:
                   lowerCamelCase__		:				List[Any]    =  model_class(__lowerCamelCase       )
                   lowerCamelCase__		:				Optional[Any]    =  self._prepare_for_class(__lowerCamelCase					,      __lowerCamelCase       )

                   lowerCamelCase__		:				str    =  model(__lowerCamelCase					,      noise=__lowerCamelCase       )
                   lowerCamelCase__		:				int    =  model.get_config()
                   # make sure that returned config is jsonifiable, which is required by keras
                   json.dumps(__lowerCamelCase       )
                   lowerCamelCase__		:				List[Any]    =  model_class.from_config(model.get_config()       )
                   # make sure it also accepts a normal config
                   lowerCamelCase__		:				Any    =  model_class.from_config(model.config       )
                   lowerCamelCase__		:				Union[str, Any]    =  new_model(__lowerCamelCase       )  # Build model
                   new_model.set_weights(model.get_weights()       )
                   lowerCamelCase__		:				List[str]    =  new_model(__lowerCamelCase					,      noise=__lowerCamelCase       )

                   self.assert_outputs_same(__lowerCamelCase					,      __lowerCamelCase       )





       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."       )
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             pass





       @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"       )
       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             pass





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


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"       )
             self.assertIsNotNone(__lowerCamelCase       )



def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
      return image







@require_tf
@require_vision
class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       @cached_property
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"       ) if is_vision_available() else None





       @slow
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             np.random.seed(2       )

             lowerCamelCase__		:				str    =  TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"       )

             lowerCamelCase__		:				Tuple    =  self.default_image_processor
             lowerCamelCase__		:				Dict    =  prepare_img()
             lowerCamelCase__		:				Union[str, Any]    =  image_processor(images=__lowerCamelCase					,      return_tensors="tf"       )

             # prepare a noise vector that will be also used for testing the TF model
             # (this way we can ensure that the PT and TF models operate on the same inputs)
             lowerCamelCase__		:				Dict    =  ViTMAEConfig()
             lowerCamelCase__		:				List[Any]    =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2       )
             lowerCamelCase__		:				Tuple    =  np.random.uniform(size=(1, num_patches)       )

             # forward pass
             lowerCamelCase__		:				List[str]    =  model(**__lowerCamelCase					,      noise=__lowerCamelCase       )

             # verify the logits
             lowerCamelCase__		:				Optional[int]    =  tf.convert_to_tensor([1, 196, 768]       )
             self.assertEqual(outputs.logits.shape					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  tf.convert_to_tensor(
                 [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]]       )

             tf.debugging.assert_near(outputs.logits[0, :3, :3]					,      __lowerCamelCase					,      atol=1E-4       )










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				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=4       ,			)       ->    int:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_attention_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_choices




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

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

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

								snake_case_				=							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=a       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, token_type_ids, attention_mask




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
								return config, inputs_dict






	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs

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



	'''simple docstring'''




	lowerCAmelCase													= True

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




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							FlaxRobertaModelTester(self					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    str:
								for model_class_name in self.all_model_classes:
															snake_case_				=							model_class_name.from_pretrained('roberta-base'       ,			from_pt=a					)
															snake_case_				=							model(np.ones((1, 1)					)					)
															self.assertIsNotNone(a					)





