
"""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 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=13     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=99     , SCREAMING_SNAKE_CASE__=32     , SCREAMING_SNAKE_CASE__=5     , SCREAMING_SNAKE_CASE__=4     , SCREAMING_SNAKE_CASE__=37     , SCREAMING_SNAKE_CASE__="gelu"     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=0.1     , SCREAMING_SNAKE_CASE__=5_12     , SCREAMING_SNAKE_CASE__=16     , SCREAMING_SNAKE_CASE__=2     , SCREAMING_SNAKE_CASE__=0.02     , SCREAMING_SNAKE_CASE__=4     , )      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     parent
											SCREAMING_SNAKE_CASE__		: str     =     batch_size
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     seq_length
											SCREAMING_SNAKE_CASE__		: int     =     is_training
											SCREAMING_SNAKE_CASE__		: Dict     =     use_attention_mask
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     use_token_type_ids
											SCREAMING_SNAKE_CASE__		: Any     =     use_labels
											SCREAMING_SNAKE_CASE__		: List[str]     =     vocab_size
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     hidden_size
											SCREAMING_SNAKE_CASE__		: str     =     num_hidden_layers
											SCREAMING_SNAKE_CASE__		: str     =     num_attention_heads
											SCREAMING_SNAKE_CASE__		: List[str]     =     intermediate_size
											SCREAMING_SNAKE_CASE__		: List[str]     =     hidden_act
											SCREAMING_SNAKE_CASE__		: List[Any]     =     hidden_dropout_prob
											SCREAMING_SNAKE_CASE__		: List[Any]     =     attention_probs_dropout_prob
											SCREAMING_SNAKE_CASE__		: int     =     max_position_embeddings
											SCREAMING_SNAKE_CASE__		: Any     =     type_vocab_size
											SCREAMING_SNAKE_CASE__		: Dict     =     type_sequence_label_size
											SCREAMING_SNAKE_CASE__		: List[Any]     =     initializer_range
											SCREAMING_SNAKE_CASE__		: List[Any]     =     num_choices



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     None
											if self.use_attention_mask:
																		SCREAMING_SNAKE_CASE__		: List[str]     =     random_attention_mask([self.batch_size, self.seq_length]					)

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

											SCREAMING_SNAKE_CASE__		: str     =     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=SCREAMING_SNAKE_CASE__     , initializer_range=self.initializer_range     , )

											return config, input_ids, token_type_ids, attention_mask



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Optional[Any]     =     config_and_inputs
											SCREAMING_SNAKE_CASE__		: List[str]     =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Any     =     config_and_inputs

											SCREAMING_SNAKE_CASE__		: Any     =     True
											SCREAMING_SNAKE_CASE__		: Any     =     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
											SCREAMING_SNAKE_CASE__		: Any     =     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 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		Any       =     True

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



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     FlaxRobertaPreLayerNormModelTester(self					)



				@slow
				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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






@require_flax
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				@slow
				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""     , from_pt=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Dict     =     np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     , dtype=jnp.intaa					)
											SCREAMING_SNAKE_CASE__		: str     =     model(SCREAMING_SNAKE_CASE__					)[0]
											SCREAMING_SNAKE_CASE__		: int     =     [1, 11, 5_02_65]
											self.assertEqual(list(output.shape					)     , SCREAMING_SNAKE_CASE__					)
											# compare the actual values for a slice.
											SCREAMING_SNAKE_CASE__		: str     =     np.array(
											    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     , dtype=np.floataa					)
											self.assertTrue(np.allclose(output[:, :3, :3]     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)					)



				@slow
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""     , from_pt=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     , dtype=jnp.intaa					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     model(SCREAMING_SNAKE_CASE__					)[0]
											# compare the actual values for a slice.
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     np.array(
											    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     , dtype=np.floataa					)
											self.assertTrue(np.allclose(output[:, :3, :3]     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)					)






"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor



class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__=7     , SCREAMING_SNAKE_CASE__=3     , SCREAMING_SNAKE_CASE__=18     , SCREAMING_SNAKE_CASE__=30     , SCREAMING_SNAKE_CASE__=4_00     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , SCREAMING_SNAKE_CASE__=None     , SCREAMING_SNAKE_CASE__=True     , )      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     size if size is not None else {"""shortest_edge""": 20}
											SCREAMING_SNAKE_CASE__		: List[Any]     =     crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											SCREAMING_SNAKE_CASE__		: Any     =     parent
											SCREAMING_SNAKE_CASE__		: List[str]     =     batch_size
											SCREAMING_SNAKE_CASE__		: Dict     =     num_channels
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_size
											SCREAMING_SNAKE_CASE__		: Dict     =     min_resolution
											SCREAMING_SNAKE_CASE__		: Dict     =     max_resolution
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     do_resize
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     size
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     do_center_crop
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     crop_size
											SCREAMING_SNAKE_CASE__		: List[str]     =     do_flip_channel_order



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_flip_channel_order": self.do_flip_channel_order,
											}






@require_torch
@require_vision
class 			lowerCAmelCase_		(a__				,      unittest.TestCase						):


				"""simple docstring"""



				__UpperCamelCase   :		str       =     MobileViTImageProcessor if is_vision_available() else None



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     MobileViTImageProcessingTester(self					)



				@property
				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_resize"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """size"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """center_crop"""					)					)
											self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__     , """do_flip_channel_order"""					)					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.image_processing_class.from_dict(self.image_processor_dict					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 20}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 18, """width""": 18}					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.image_processing_class.from_dict(self.image_processor_dict     , size=42     , crop_size=84					)
											self.assertEqual(image_processor.size     , {"""shortest_edge""": 42}					)
											self.assertEqual(image_processor.crop_size     , {"""height""": 84, """width""": 84}					)



				def 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											pass



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PIL images
											SCREAMING_SNAKE_CASE__		: List[str]     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , Image.Image					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Dict     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random numpy tensors
											SCREAMING_SNAKE_CASE__		: Tuple     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__     , numpify=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , np.ndarray					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )



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



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     self.image_processing_class(**self.image_processor_dict					)
											# create random PyTorch tensors
											SCREAMING_SNAKE_CASE__		: Any     =     prepare_image_inputs(self.image_processor_tester     , equal_resolution=SCREAMING_SNAKE_CASE__     , torchify=SCREAMING_SNAKE_CASE__					)
											for image in image_inputs:
																		self.assertIsInstance(SCREAMING_SNAKE_CASE__     , torch.Tensor					)

											# Test not batched input
											SCREAMING_SNAKE_CASE__		: List[str]     =     image_processing(image_inputs[0]     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )

											# Test batched
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processing(SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).pixel_values
											self.assertEqual(
											    encoded_images.shape     , (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size["""height"""],
											        self.image_processor_tester.crop_size["""width"""],
											    )     , )




