




import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu





class 					__lowerCamelCase			(       nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	List[str]  ) ->			List[Any]:
         super().__init__()
         lowerCAmelCase__            =					nn.Linear(3  ,      4  )
         lowerCAmelCase__            =					nn.BatchNormad(4  )
         lowerCAmelCase__            =					nn.Linear(4  ,      5  )


    def   a (			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	List[Any]  ) ->			Optional[int]:
         return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE__  )  )  )






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      *SCREAMING_SNAKE_CASE__	:	List[Any]  ,      **SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Optional[int]:
         return (args[0] + 1,) + args[1:], kwargs






class 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    def   a (			self	:	Optional[int]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			str:
         return output + 1






class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    def   a (			self	:	Union[str, Any]  ) ->			List[Any]:
         lowerCAmelCase__            =					ModelForTest()
         lowerCAmelCase__            =					ModelHook()

         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         self.assertEqual(test_model._hf_hook  ,      SCREAMING_SNAKE_CASE__  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "_old_forward"  )  )

         # Check adding the hook did not change the name or the signature
         self.assertEqual(test_model.forward.__name__  ,      "forward"  )
         self.assertListEqual(list(inspect.signature(test_model.forward  ).parameters  )  ,      ["x"]  )

         remove_hook_from_module(SCREAMING_SNAKE_CASE__  )
         self.assertFalse(hasattr(SCREAMING_SNAKE_CASE__  ,      "_hf_hook"  )  )
         self.assertFalse(hasattr(SCREAMING_SNAKE_CASE__  ,      "_old_forward"  )  )


    def   a (			self	:	int  ) ->			Optional[Any]:
         lowerCAmelCase__            =					ModelForTest()
         lowerCAmelCase__            =					ModelHook()

         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      append=SCREAMING_SNAKE_CASE__  )

         self.assertEqual(isinstance(test_model._hf_hook  ,      SCREAMING_SNAKE_CASE__  )  ,      SCREAMING_SNAKE_CASE__  )
         self.assertEqual(len(test_model._hf_hook.hooks  )  ,      2  )
         self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__  ,      "_old_forward"  )  )

         # Check adding the hook did not change the name or the signature
         self.assertEqual(test_model.forward.__name__  ,      "forward"  )
         self.assertListEqual(list(inspect.signature(test_model.forward  ).parameters  )  ,      ["x"]  )

         remove_hook_from_module(SCREAMING_SNAKE_CASE__  )
         self.assertFalse(hasattr(SCREAMING_SNAKE_CASE__  ,      "_hf_hook"  )  )
         self.assertFalse(hasattr(SCREAMING_SNAKE_CASE__  ,      "_old_forward"  )  )


    def   a (			self	:	List[str]  ) ->			Optional[int]:
         lowerCAmelCase__            =					ModelForTest()
         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					test_model(x + 1  )
         lowerCAmelCase__            =					test_model(x + 2  )

         lowerCAmelCase__            =					PreForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-5  )  )

         # Attaching a hook to a model when it already has one replaces, does not chain
         lowerCAmelCase__            =					PreForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-5  )  )

         # You need to use the sequential hook to chain two or more hooks
         lowerCAmelCase__            =					SequentialHook(PreForwardHook()  ,      PreForwardHook()  )
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         assert torch.allclose(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-5  )


    def   a (			self	:	List[Any]  ) ->			Dict:
         lowerCAmelCase__            =					ModelForTest()
         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					PostForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,      output + 1  ,      atol=1e-5  )  )

         # Attaching a hook to a model when it already has one replaces, does not chain
         lowerCAmelCase__            =					PostForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,      output + 1  ,      atol=1e-5  )  )

         # You need to use the sequential hook to chain two or more hooks
         lowerCAmelCase__            =					SequentialHook(PostForwardHook()  ,      PostForwardHook()  )
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         assert torch.allclose(SCREAMING_SNAKE_CASE__  ,      output + 2  ,      atol=1e-5  )


    def   a (			self	:	List[str]  ) ->			str:
         lowerCAmelCase__            =					ModelForTest()
         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					PostForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,      output + 1  )  )
         self.assertTrue(outputa.requires_grad  )

         lowerCAmelCase__            =					True
         lowerCAmelCase__            =					test_model(SCREAMING_SNAKE_CASE__  )
         self.assertFalse(outputa.requires_grad  )


    @require_multi_gpu
    def   a (			self	:	int  ) ->			Tuple:
         lowerCAmelCase__            =					ModelForTest()
         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # This will move each submodule on different devices
         add_hook_to_module(model.lineara  ,      AlignDevicesHook(execution_device=0  )  )
         add_hook_to_module(model.batchnorm  ,      AlignDevicesHook(execution_device=0  )  )
         add_hook_to_module(model.lineara  ,      AlignDevicesHook(execution_device=1  )  )

         self.assertEqual(model.lineara.weight.device  ,      torch.device(0  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device(0  )  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device(0  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device(1  )  )

         # We can still make a forward pass. The input does not need to be on any particular device
         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      torch.device(1  )  )

         # We can add a general hook to put back output on same device as input.
         add_hook_to_module(SCREAMING_SNAKE_CASE__  ,      AlignDevicesHook(io_same_device=SCREAMING_SNAKE_CASE__  )  )
         lowerCAmelCase__            =					torch.randn(2  ,      3  ).to(0  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      torch.device(0  )  )


    def   a (			self	:	List[str]  ) ->			List[Any]:
         lowerCAmelCase__            =					ModelForTest()

         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # This will move each submodule on different devices
         lowerCAmelCase__            =					{"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

         add_hook_to_module(model.lineara  ,      AlignDevicesHook(**SCREAMING_SNAKE_CASE__  )  )
         add_hook_to_module(model.batchnorm  ,      AlignDevicesHook(**SCREAMING_SNAKE_CASE__  )  )
         add_hook_to_module(model.lineara  ,      AlignDevicesHook(**SCREAMING_SNAKE_CASE__  )  )

         # Parameters have been offloaded, so on the meta device
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         # Buffers are not included in the offload by default, so are on the execution device
         lowerCAmelCase__            =					torch.device(hook_kwargs["execution_device"]  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      SCREAMING_SNAKE_CASE__  )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_module(model.lineara  )
         remove_hook_from_module(model.batchnorm  )
         remove_hook_from_module(model.lineara  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # Now test with buffers included in the offload
         lowerCAmelCase__            =					{
             "execution_device": 0 if torch.cuda.is_available() else "cpu",
             "offload": True,
             "offload_buffers": True,
         }

         add_hook_to_module(model.lineara  ,      AlignDevicesHook(**SCREAMING_SNAKE_CASE__  )  )
         add_hook_to_module(model.batchnorm  ,      AlignDevicesHook(**SCREAMING_SNAKE_CASE__  )  )
         add_hook_to_module(model.lineara  ,      AlignDevicesHook(**SCREAMING_SNAKE_CASE__  )  )

         # Parameters have been offloaded, so on the meta device, buffers included
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device("meta"  )  )

         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      SCREAMING_SNAKE_CASE__  )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_module(model.lineara  )
         remove_hook_from_module(model.batchnorm  )
         remove_hook_from_module(model.lineara  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )


    def   a (			self	:	str  ) ->			List[Any]:
         lowerCAmelCase__            =					ModelForTest()

         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # This will move each submodule on different devices
         lowerCAmelCase__            =					0 if torch.cuda.is_available() else "cpu"
         attach_align_device_hook(SCREAMING_SNAKE_CASE__  ,      execution_device=SCREAMING_SNAKE_CASE__  ,      offload=SCREAMING_SNAKE_CASE__  )

         # Parameters have been offloaded, so on the meta device
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         # Buffers are not included in the offload by default, so are on the execution device
         lowerCAmelCase__            =					torch.device(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      SCREAMING_SNAKE_CASE__  )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # Now test with buffers included in the offload
         attach_align_device_hook(SCREAMING_SNAKE_CASE__  ,      execution_device=SCREAMING_SNAKE_CASE__  ,      offload=SCREAMING_SNAKE_CASE__  ,      offload_buffers=SCREAMING_SNAKE_CASE__  )

         # Parameters have been offloaded, so on the meta device, buffers included
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device("meta"  )  )

         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      SCREAMING_SNAKE_CASE__  )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )


    def   a (			self	:	Optional[Any]  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					ModelForTest()

         # Everything is on CPU
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # This will move each submodule on different devices
         lowerCAmelCase__            =					0 if torch.cuda.is_available() else "cpu"
         attach_align_device_hook(
             SCREAMING_SNAKE_CASE__  ,      execution_device=SCREAMING_SNAKE_CASE__  ,      offload=SCREAMING_SNAKE_CASE__  ,      weights_map=model.state_dict()  )

         # Parameters have been offloaded, so on the meta device
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         # Buffers are not included in the offload by default, so are on the execution device
         lowerCAmelCase__            =					torch.device(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      SCREAMING_SNAKE_CASE__  )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )

         # Now test with buffers included in the offload
         attach_align_device_hook(
             SCREAMING_SNAKE_CASE__  ,      execution_device=SCREAMING_SNAKE_CASE__  ,      offload=SCREAMING_SNAKE_CASE__  ,      weights_map=model.state_dict()  ,      offload_buffers=SCREAMING_SNAKE_CASE__  ,      )

         # Parameters have been offloaded, so on the meta device, buffers included
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("meta"  )  )
         self.assertEqual(model.batchnorm.running_mean.device  ,      torch.device("meta"  )  )

         lowerCAmelCase__            =					torch.randn(2  ,      3  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(output.device  ,      SCREAMING_SNAKE_CASE__  )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(SCREAMING_SNAKE_CASE__  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.batchnorm.weight.device  ,      torch.device("cpu"  )  )
         self.assertEqual(model.lineara.weight.device  ,      torch.device("cpu"  )  )







'''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 				__snake_case     (					unittest.TestCase							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=7      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=99      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=512      ,       UpperCamelCase_=16      ,       UpperCamelCase_=2      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=4      ,       )      ->		List[Any]:
		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   _snake_case   (		self		)      ->		Any:
		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=UpperCamelCase_      ,       initializer_range=self.initializer_range      ,       )

		return config, input_ids, token_type_ids, attention_mask





	def   _snake_case   (		self		)      ->		Tuple:
		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   _snake_case   (		self		)      ->		Union[str, Any]:
		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 				__snake_case     (					__magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  True

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





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   FlaxRobertaModelTester(self		)





	@slow
	def   _snake_case   (		self		)      ->		Tuple:
		for model_class_name in self.all_model_classes:
			snake_case__	       =   model_class_name.from_pretrained('roberta-base'      ,       from_pt=UpperCamelCase_		)
			snake_case__	       =   model(np.ones((1, 1)		)		)
			self.assertIsNotNone(UpperCamelCase_		)



