






'''simple docstring'''







import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_A			:    int					  =get_tests_dir('''fixtures/test_sentencepiece.model''')

_A			:    Tuple					  ={'''target_lang''': '''fi''', '''source_lang''': '''en'''}
_A			:    int					  ='''>>zh<<'''
_A			:    Dict					  ='''Helsinki-NLP/'''

if is_torch_available():
			_A			:    List[Any]					  ='''pt'''
elif is_tf_available():
			_A			:    Optional[int]					  ='''tf'''
else:
			_A			:    Dict					  ='''jax'''

@require_sentencepiece
class _lowercase			(       _lowercase       ,   unittest.TestCase	):
							a          = MarianTokenizer
							a          = False
							a          = True




							def 	lowerCamelCase_       (							self:       List[str]  ):
														super().setUp()
														lowerCamelCase__      :			List[Any] 						= ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
														lowerCamelCase__      :			Optional[Any] 						= dict(zip(UpperCamelCase__    ,							range(len(UpperCamelCase__  )  )  )  )
														lowerCamelCase__      :			Optional[int] 						= Path(self.tmpdirname  )
														save_json(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]  )
														save_json(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]  )
														if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																					copyfile(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]  )
																					copyfile(UpperCamelCase__    ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]  )

														lowerCamelCase__      :			Dict 						= MarianTokenizer.from_pretrained(self.tmpdirname  )
														tokenizer.save_pretrained(self.tmpdirname  )




							def 	lowerCamelCase_       (							self:       Optional[Any]    ,							**UpperCamelCase__:       Any  ):
														return MarianTokenizer.from_pretrained(self.tmpdirname    ,							**UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       List[str]  ):
														return (
														    "This is a test",
														    "This is a test",
														)




							def 	lowerCamelCase_       (							self:       Optional[Any]  ):
														lowerCamelCase__      :			Any 						= """</s>"""
														lowerCamelCase__      :			List[Any] 						= 0

														self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__  )    ,							UpperCamelCase__  )
														self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__  )    ,							UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			int 						= list(self.get_tokenizer().get_vocab().keys()  )

														self.assertEqual(vocab_keys[0]    ,							"""</s>"""  )
														self.assertEqual(vocab_keys[1]    ,							"""<unk>"""  )
														self.assertEqual(vocab_keys[-1]    ,							"""<pad>"""  )
														self.assertEqual(len(UpperCamelCase__  )    ,							9  )




							def 	lowerCamelCase_       (							self:       int  ):
														self.assertEqual(self.get_tokenizer().vocab_size    ,							9  )




							def 	lowerCamelCase_       (							self:       int  ):
														lowerCamelCase__      :			List[Any] 						= MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''  )
														lowerCamelCase__      :			Optional[int] 						= en_de_tokenizer(["""I am a small frog"""]    ,							return_tensors=UpperCamelCase__  )
														self.assertIsInstance(UpperCamelCase__    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= [38, 121, 14, 697, 38_848, 0]
														self.assertListEqual(UpperCamelCase__    ,							batch.input_ids[0]  )

														lowerCamelCase__      :			List[str] 						= tempfile.mkdtemp()
														en_de_tokenizer.save_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= [x.name for x in Path(UpperCamelCase__  ).glob("""*"""  )]
														self.assertIn("""source.spm"""    ,							UpperCamelCase__  )
														MarianTokenizer.from_pretrained(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														lowerCamelCase__      :			List[Any] 						= self.get_tokenizer()

														lowerCamelCase__      :			Any 						= tok(
														    ["""I am a small frog""" * 1_000, """I am a small frog"""]    ,							padding=UpperCamelCase__    ,							truncation=UpperCamelCase__    ,							return_tensors=UpperCamelCase__  )
														self.assertIsInstance(UpperCamelCase__    ,							UpperCamelCase__  )
														self.assertEqual(batch.input_ids.shape    ,							(2, 512)  )




							def 	lowerCamelCase_       (							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= self.get_tokenizer()
														lowerCamelCase__      :			Dict 						= tok(["""I am a tiny frog""", """I am a small frog"""]    ,							padding=UpperCamelCase__    ,							return_tensors=UpperCamelCase__  )
														self.assertIsInstance(UpperCamelCase__    ,							UpperCamelCase__  )
														self.assertEqual(batch_smaller.input_ids.shape    ,							(2, 10)  )




							@slow
							def 	lowerCamelCase_       (							self:       List[str]  ):
														# fmt: off
														lowerCamelCase__      :			int 						= {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
														# fmt: on

														self.tokenizer_integration_test_util(
														    expected_encoding=UpperCamelCase__    ,							model_name="""Helsinki-NLP/opus-mt-en-de"""    ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""    ,							decode_kwargs={"""use_source_tokenizer""": True}    ,							)




							def 	lowerCamelCase_       (							self:       List[str]  ):
														lowerCamelCase__      :			Union[str, Any] 						= MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""  )

														lowerCamelCase__      :			str 						= """Tämä on testi"""
														lowerCamelCase__      :			Any 						= """This is a test"""

														lowerCamelCase__      :			int 						= [76, 7, 2_047, 2]
														lowerCamelCase__      :			List[str] 						= [69, 12, 11, 940, 2]

														lowerCamelCase__      :			Tuple 						= tokenizer(UpperCamelCase__  ).input_ids
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Union[str, Any] 						= tokenizer(text_target=UpperCamelCase__  ).input_ids
														self.assertListEqual(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Tuple 						= tokenizer.decode(UpperCamelCase__    ,							skip_special_tokens=UpperCamelCase__  )
														self.assertEqual(UpperCamelCase__    ,							UpperCamelCase__  )









'''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__  )

