






'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

_A			:    int					  ={
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}

class _lowercase			(       _lowercase	):
							a          = """roberta"""




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]=50_265    ,							UpperCamelCase__:       Any=768    ,							UpperCamelCase__:       Optional[int]=12    ,							UpperCamelCase__:       Any=12    ,							UpperCamelCase__:       Optional[Any]=3_072    ,							UpperCamelCase__:       Tuple="gelu"    ,							UpperCamelCase__:       int=0.1    ,							UpperCamelCase__:       str=0.1    ,							UpperCamelCase__:       Optional[int]=512    ,							UpperCamelCase__:       List[Any]=2    ,							UpperCamelCase__:       Tuple=0.02    ,							UpperCamelCase__:       Optional[Any]=1e-12    ,							UpperCamelCase__:       Union[str, Any]=1    ,							UpperCamelCase__:       str=0    ,							UpperCamelCase__:       List[str]=2    ,							UpperCamelCase__:       Optional[Any]="absolute"    ,							UpperCamelCase__:       Optional[Any]=True    ,							UpperCamelCase__:       Tuple=None    ,							**UpperCamelCase__:       Union[str, Any]    ,							):
														super().__init__(pad_token_id=UpperCamelCase__    ,							bos_token_id=UpperCamelCase__    ,							eos_token_id=UpperCamelCase__    ,							**UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= vocab_size
														lowerCamelCase__      :			List[str] 						= hidden_size
														lowerCamelCase__      :			Any 						= num_hidden_layers
														lowerCamelCase__      :			Union[str, Any] 						= num_attention_heads
														lowerCamelCase__      :			Any 						= hidden_act
														lowerCamelCase__      :			Optional[int] 						= intermediate_size
														lowerCamelCase__      :			Optional[int] 						= hidden_dropout_prob
														lowerCamelCase__      :			Dict 						= attention_probs_dropout_prob
														lowerCamelCase__      :			Optional[int] 						= max_position_embeddings
														lowerCamelCase__      :			Tuple 						= type_vocab_size
														lowerCamelCase__      :			Union[str, Any] 						= initializer_range
														lowerCamelCase__      :			List[str] 						= layer_norm_eps
														lowerCamelCase__      :			str 						= position_embedding_type
														lowerCamelCase__      :			Union[str, Any] 						= use_cache
														lowerCamelCase__      :			Any 						= classifier_dropout



class _lowercase			(       _lowercase	):




							@property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														if self.task == "multiple-choice":
																					lowerCamelCase__      :			int 						= {0: """batch""", 1: """choice""", 2: """sequence"""}
														else:
																					lowerCamelCase__      :			Union[str, Any] 						= {0: """batch""", 1: """sequence"""}
														return OrderedDict(
														    [
														        ("""input_ids""", dynamic_axis),
														        ("""attention_mask""", dynamic_axis),
														    ]  )







'''simple docstring'''







import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
			from transformers.models.mobilevitva.modeling_mobilevitva import (
			    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
			    make_divisible,
			)


if is_vision_available():
			from PIL import Image

			from transformers import MobileViTImageProcessor

class _lowercase			(       _lowercase	):




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Dict 						= self.config_class(**self.inputs_dict  )
														self.parent.assertTrue(hasattr(UpperCamelCase__    ,							"""width_multiplier"""  )  )



class _lowercase			:




							def __init__(							self:       str    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       str=13    ,							UpperCamelCase__:       Any=64    ,							UpperCamelCase__:       Optional[Any]=2    ,							UpperCamelCase__:       str=3    ,							UpperCamelCase__:       List[str]="swish"    ,							UpperCamelCase__:       Any=3    ,							UpperCamelCase__:       Optional[int]=32    ,							UpperCamelCase__:       Union[str, Any]=0.1    ,							UpperCamelCase__:       int=0.02    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Dict=True    ,							UpperCamelCase__:       Any=10    ,							UpperCamelCase__:       int=None    ,							UpperCamelCase__:       List[Any]=0.25    ,							UpperCamelCase__:       str=0.0    ,							UpperCamelCase__:       Optional[int]=0.0    ,							):
														lowerCamelCase__      :			Any 						= parent
														lowerCamelCase__      :			Optional[Any] 						= batch_size
														lowerCamelCase__      :			Optional[int] 						= image_size
														lowerCamelCase__      :			str 						= patch_size
														lowerCamelCase__      :			Optional[int] 						= num_channels
														lowerCamelCase__      :			Optional[Any] 						= make_divisible(512 * width_multiplier    ,							divisor=8  )
														lowerCamelCase__      :			List[str] 						= hidden_act
														lowerCamelCase__      :			Any 						= conv_kernel_size
														lowerCamelCase__      :			Any 						= output_stride
														lowerCamelCase__      :			Union[str, Any] 						= classifier_dropout_prob
														lowerCamelCase__      :			List[str] 						= use_labels
														lowerCamelCase__      :			Optional[Any] 						= is_training
														lowerCamelCase__      :			List[str] 						= num_labels
														lowerCamelCase__      :			Dict 						= initializer_range
														lowerCamelCase__      :			List[Any] 						= scope
														lowerCamelCase__      :			Tuple 						= width_multiplier
														lowerCamelCase__      :			List[Any] 						= ffn_dropout
														lowerCamelCase__      :			Any 						= attn_dropout




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

														lowerCamelCase__      :			Tuple 						= None
														lowerCamelCase__      :			Optional[Any] 						= None
														if self.use_labels:
																					lowerCamelCase__      :			List[Any] 						= ids_tensor([self.batch_size]    ,							self.num_labels  )
																					lowerCamelCase__      :			str 						= ids_tensor([self.batch_size, self.image_size, self.image_size]    ,							self.num_labels  )

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

														return config, pixel_values, labels, pixel_labels




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														return MobileViTVaConfig(
														    image_size=self.image_size    ,							patch_size=self.patch_size    ,							num_channels=self.num_channels    ,							hidden_act=self.hidden_act    ,							conv_kernel_size=self.conv_kernel_size    ,							output_stride=self.output_stride    ,							classifier_dropout_prob=self.classifier_dropout_prob    ,							initializer_range=self.initializer_range    ,							width_multiplier=self.width_multiplier    ,							ffn_dropout=self.ffn_dropout_prob    ,							attn_dropout=self.attn_dropout_prob    ,							)




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel(config=UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			str 						= model(UpperCamelCase__  )
														self.parent.assertEqual(
														    result.last_hidden_state.shape    ,							(
														        self.batch_size,
														        self.last_hidden_size,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )    ,							)




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Tuple  ):
														lowerCamelCase__      :			Tuple 						= self.num_labels
														lowerCamelCase__      :			Dict 						= MobileViTVaForImageClassification(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			int 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)  )




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			List[str] 						= self.num_labels
														lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaForSemanticSegmentation(UpperCamelCase__  )
														model.to(UpperCamelCase__  )
														model.eval()
														lowerCamelCase__      :			Tuple 						= model(UpperCamelCase__  )
														self.parent.assertEqual(
														    result.logits.shape    ,							(
														        self.batch_size,
														        self.num_labels,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )    ,							)
														lowerCamelCase__      :			List[Any] 						= model(UpperCamelCase__    ,							labels=UpperCamelCase__  )
														self.parent.assertEqual(
														    result.logits.shape    ,							(
														        self.batch_size,
														        self.num_labels,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )    ,							)




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



@require_torch
class _lowercase			(       _lowercase       ,   _lowercase       ,   unittest.TestCase	):
							a          = (
							    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)

							a          = (
							    {
							        """feature-extraction""": MobileViTVaModel,
							        """image-classification""": MobileViTVaForImageClassification,
							        """image-segmentation""": MobileViTVaForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							a          = False
							a          = False
							a          = False
							a          = False




							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= MobileViTVaModelTester(self  )
														lowerCamelCase__      :			List[str] 						= MobileViTVaConfigTester(self    ,							config_class=UpperCamelCase__    ,							has_text_modality=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""  )
							def 	lowerCamelCase_       (							self:       List[str]  ):
														pass




							@unittest.skip(reason="""MobileViTV2 does not output attentions"""  )
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														pass




							@require_torch_multi_gpu
							@unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""  )
							def 	lowerCamelCase_       (							self:       int  ):
														pass




							@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
							def 	lowerCamelCase_       (							self:       Tuple  ):
														pass




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__						,	lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			Union[str, Any] 						= model_class(UpperCamelCase__  )
																					lowerCamelCase__      :			int 						= inspect.signature(model.forward  )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__      :			Tuple 						= [*signature.parameters.keys()]

																					lowerCamelCase__      :			str 						= ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       List[str]  ):
														def check_hidden_states_output(UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[Any]  ):
																					lowerCamelCase__      :			List[Any] 						= model_class(UpperCamelCase__  )
																					model.to(UpperCamelCase__  )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__      :			Tuple 						= model(**self._prepare_for_class(UpperCamelCase__    ,							UpperCamelCase__  )  )

																					lowerCamelCase__      :			Optional[int] 						= outputs.hidden_states

																					lowerCamelCase__      :			List[Any] 						= 5
																					self.assertEqual(len(UpperCamelCase__  )    ,							UpperCamelCase__  )

																					# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
																					# with the width and height being successively divided by 2.
																					lowerCamelCase__      :			int 						= 2
																					for i in range(len(UpperCamelCase__  )  ):
																												self.assertListEqual(
																												    list(hidden_states[i].shape[-2:]  )    ,							[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]    ,							)
																												divisor *= 2

																					self.assertEqual(self.model_tester.output_stride    ,							divisor // 2  )

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

														for model_class in self.all_model_classes:
																					lowerCamelCase__      :			int 						= True
																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__      :			str 						= True

																					check_hidden_states_output(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			int 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Dict  ):
														lowerCamelCase__      :			str 						= self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__  )




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaModel.from_pretrained(UpperCamelCase__  )
																					self.assertIsNotNone(UpperCamelCase__  )





def 				SCREAMING_SNAKE_CASE_ ()   -> Optional[int]:
							lowerCamelCase__      :			str 						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
							return image



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




							@cached_property
							def 	lowerCamelCase_       (							self:       Tuple  ):
														return (
														    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  )
														    if is_vision_available()
														    else None
														)




							@slow
							def 	lowerCamelCase_       (							self:       Any  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""  ).to(
														    UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= self.default_image_processor
														lowerCamelCase__      :			List[Any] 						= prepare_img()
														lowerCamelCase__      :			Any 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			int 						= model(**UpperCamelCase__  )

														# verify the logits
														lowerCamelCase__      :			str 						= torch.Size((1, 1_000)  )
														self.assertEqual(outputs.logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[int] 						= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]  ).to(UpperCamelCase__  )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,							UpperCamelCase__    ,							atol=1e-4  )  )




							@slow
							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			Optional[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Union[str, Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Optional[Any] 						= model(**UpperCamelCase__  )
														lowerCamelCase__      :			str 						= outputs.logits

														# verify the logits
														lowerCamelCase__      :			List[str] 						= torch.Size((1, 21, 32, 32)  )
														self.assertEqual(logits.shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= torch.tensor(
														    [
														        [[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
														        [[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
														        [[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
														    ]    ,							device=UpperCamelCase__    ,							)

														self.assertTrue(torch.allclose(logits[0, :3, :3, :3]    ,							UpperCamelCase__    ,							atol=1e-4  )  )




							@slow
							def 	lowerCamelCase_       (							self:       Optional[int]  ):
														lowerCamelCase__      :			Optional[Any] 						= MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )
														lowerCamelCase__      :			List[Any] 						= model.to(UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""  )

														lowerCamelCase__      :			Optional[Any] 						= prepare_img()
														lowerCamelCase__      :			Dict 						= image_processor(images=UpperCamelCase__    ,							return_tensors="""pt"""  ).to(UpperCamelCase__  )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__      :			Dict 						= model(**UpperCamelCase__  )

														lowerCamelCase__      :			List[str] 						= outputs.logits.detach().cpu()

														lowerCamelCase__      :			List[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__    ,							target_sizes=[(50, 60)]  )
														lowerCamelCase__      :			int 						= torch.Size((50, 60)  )
														self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= image_processor.post_process_semantic_segmentation(outputs=UpperCamelCase__  )
														lowerCamelCase__      :			int 						= torch.Size((32, 32)  )
														self.assertEqual(segmentation[0].shape    ,							UpperCamelCase__  )

