"""simple docstring"""
import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, 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 MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
 from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor




class    __snake_case       (     __lowerCAmelCase				):



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	self.config_class(**self.inputs_dict)
        self.parent.assertTrue(hasattr(lowercase    ,			'hidden_sizes'))
        self.parent.assertTrue(hasattr(lowercase    ,			'neck_hidden_sizes'))
        self.parent.assertTrue(hasattr(lowercase    ,			'num_attention_heads'))







class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=13    ,			lowercase=32    ,			lowercase=2    ,			lowercase=3    ,			lowercase=6_40    ,			lowercase=4    ,			lowercase="silu"    ,			lowercase=3    ,			lowercase=32    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=10    ,			lowercase=None    ,			)	->			List[str]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	parent
        a__:     Any										=	batch_size
        a__:     int										=	image_size
        a__:     Any										=	patch_size
        a__:     str										=	num_channels
        a__:     Union[str, Any]										=	last_hidden_size
        a__:     int										=	num_attention_heads
        a__:     Any										=	hidden_act
        a__:     Union[str, Any]										=	conv_kernel_size
        a__:     Optional[int]										=	output_stride
        a__:     str										=	hidden_dropout_prob
        a__:     Union[str, Any]										=	attention_probs_dropout_prob
        a__:     List[str]										=	classifier_dropout_prob
        a__:     Any										=	use_labels
        a__:     Any										=	is_training
        a__:     Tuple										=	num_labels
        a__:     Tuple										=	initializer_range
        a__:     Optional[Any]										=	scope



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





        '''simple docstring'''





        a__:     List[Any]										=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

        a__:     Optional[Any]										=	self.get_config()

        return config, pixel_values, labels, pixel_labels



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





        '''simple docstring'''





        return MobileViTConfig(
            image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			num_attention_heads=self.num_attention_heads    ,			hidden_act=self.hidden_act    ,			conv_kernel_size=self.conv_kernel_size    ,			output_stride=self.output_stride    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			classifier_dropout_prob=self.classifier_dropout_prob    ,			initializer_range=self.initializer_range    ,			)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     List[str]										=	MobileViTModel(config=lowercase)
        model.to(lowercase)
        model.eval()
        a__:     int										=	model(lowercase)
        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    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.num_labels
        a__:     Union[str, Any]										=	MobileViTForImageClassification(lowercase)
        model.to(lowercase)
        model.eval()
        a__:     str										=	model(lowercase    ,			labels=lowercase)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels))



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			int:





        '''simple docstring'''





        a__:     int										=	self.num_labels
        a__:     Any										=	MobileViTForSemanticSegmentation(lowercase)
        model.to(lowercase)
        model.eval()
        a__:     Any										=	model(lowercase)
        self.parent.assertEqual(
            result.logits.shape    ,			(
                self.batch_size,
                self.num_labels,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,			)
        a__:     int										=	model(lowercase    ,			labels=lowercase)
        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)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	self.prepare_config_and_inputs()
        a__							,    a__							,    a__							,    a__:     Optional[int]										=	config_and_inputs
        a__:     Union[str, Any]										=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		(
          (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      a__        =		(
          {
              """feature-extraction""": MobileViTModel,
              """image-classification""": MobileViTForImageClassification,
              """image-segmentation""": MobileViTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      a__        =		False
      a__        =		False
      a__        =		False
      a__        =		False



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





        '''simple docstring'''





        a__:     Optional[Any]										=	MobileViTModelTester(self)
        a__:     Union[str, Any]										=	MobileViTConfigTester(self    ,			config_class=lowercase    ,			has_text_modality=lowercase)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.config_tester.run_common_tests()



      @unittest.skip(reason='MobileViT does not use inputs_embeds')
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        pass



      @unittest.skip(reason='MobileViT does not support input and output embeddings')
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        pass



      @unittest.skip(reason='MobileViT does not output attentions')
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        pass



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__							,    a__:     Dict										=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__:     Tuple										=	model_class(lowercase)
          a__:     List[Any]										=	inspect.signature(model.forward)
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          a__:     List[Any]										=	[*signature.parameters.keys()]

          a__:     Optional[Any]										=	['pixel_values']
          self.assertListEqual(arg_names[:1]    ,			lowercase)



      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        pass



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





        '''simple docstring'''





        a__:     int										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        def check_hidden_states_output(lowercase    ,			lowercase    ,			lowercase):
          a__:     Tuple										=	model_class(lowercase)
          model.to(lowercase)
          model.eval()

          with torch.no_grad():
            a__:     Dict										=	model(**self._prepare_for_class(lowercase    ,			lowercase))

          a__:     Dict										=	outputs.hidden_states

          a__:     Union[str, Any]										=	5
          self.assertEqual(len(lowercase)    ,			lowercase)

          # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
          # with the width and height being successively divided by 2.
          a__:     Union[str, Any]										=	2
          for i in range(len(lowercase)):
            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)

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

        for model_class in self.all_model_classes:
          a__:     Optional[Any]										=	True
          check_hidden_states_output(lowercase    ,			lowercase    ,			lowercase)

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          a__:     Optional[Any]										=	True

          check_hidden_states_output(lowercase    ,			lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Any										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase)



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





        '''simple docstring'''





        a__:     Tuple										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     Tuple										=	MobileViTModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)







def 						__a   (	)	->Optional[Any]:
  a__:     Any										=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
  return image




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



      @cached_property
      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small') if is_vision_available() else None



      @slow
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	MobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small').to(lowercase)

        a__:     str										=	self.default_image_processor
        a__:     Optional[Any]										=	prepare_img()
        a__:     Optional[int]										=	image_processor(images=lowercase    ,			return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:     int										=	model(**lowercase)

        # verify the logits
        a__:     Optional[int]										=	torch.Size((1, 10_00))
        self.assertEqual(outputs.logits.shape    ,			lowercase)

        a__:     Optional[Any]										=	torch.tensor([-1.9364, -1.2327, -0.4653]).to(lowercase)

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



      @slow
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     str										=	MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
        a__:     Union[str, Any]										=	model.to(lowercase)

        a__:     str										=	MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

        a__:     Any										=	prepare_img()
        a__:     Optional[Any]										=	image_processor(images=lowercase    ,			return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:     Union[str, Any]										=	model(**lowercase)
        a__:     int										=	outputs.logits

        # verify the logits
        a__:     int										=	torch.Size((1, 21, 32, 32))
        self.assertEqual(logits.shape    ,			lowercase)

        a__:     Tuple										=	torch.tensor(
            [
                [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
            ]    ,			device=lowercase    ,			)

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



      @slow
      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	MobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small')
        a__:     Tuple										=	model.to(lowercase)

        a__:     str										=	MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small')

        a__:     Union[str, Any]										=	prepare_img()
        a__:     Optional[Any]										=	image_processor(images=lowercase    ,			return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:     Any										=	model(**lowercase)

        a__:     str										=	outputs.logits.detach().cpu()

        a__:     Optional[Any]										=	image_processor.post_process_semantic_segmentation(outputs=lowercase    ,			target_sizes=[(50, 60)])
        a__:     List[Any]										=	torch.Size((50, 60))
        self.assertEqual(segmentation[0].shape    ,			lowercase)

        a__:     str										=	image_processor.post_process_semantic_segmentation(outputs=lowercase)
        a__:     Any										=	torch.Size((32, 32))
        self.assertEqual(segmentation[0].shape    ,			lowercase)



"""simple docstring"""
import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece.model')
lowercase__					       =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowercase__					       =	'pt' if is_torch_available() else 'tf'




@require_sentencepiece
@require_tokenizers
class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		CamembertTokenizer
      a__        =		CamembertTokenizerFast
      a__        =		True
      a__        =		True



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





        '''simple docstring'''





        super().setUp()

        # We have a SentencePiece fixture for testing
        a__:     Tuple										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'<pad>'
        a__:     List[Any]										=	1

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



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     str										=	list(self.get_tokenizer().get_vocab().keys())

        self.assertEqual(vocab_keys[0]    ,			'<s>NOTUSED')
        self.assertEqual(vocab_keys[1]    ,			'<pad>')
        self.assertEqual(vocab_keys[-1]    ,			'<mask>')
        self.assertEqual(len(lowercase)    ,			10_04)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        self.assertEqual(self.get_tokenizer().vocab_size    ,			10_05)



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





        '''simple docstring'''





        a__:     Optional[Any]										=	CamembertTokenizer(lowercase)
        tokenizer.save_pretrained(self.tmpdirname)
        a__:     List[Any]										=	CamembertTokenizerFast.from_pretrained(self.tmpdirname)

        a__:     Dict										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[int]										=	tokenizer.encode(lowercase)
        a__:     Any										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Optional[Any]										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        # <unk> tokens are not the same for `rust` than for `slow`.
        # Because spm gives back raw token instead of `unk` in EncodeAsPieces
        # tokens = tokenizer.tokenize(sequence)
        a__:     Tuple										=	tokenizer.convert_ids_to_tokens(lowercase)
        a__:     Tuple										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        if not self.test_rust_tokenizer:
          return

        a__:     Dict										=	self.get_tokenizer()
        a__:     str										=	self.get_rust_tokenizer()

        a__:     int										=	'I was born in 92000, and this is falsé.'

        a__:     Optional[Any]										=	tokenizer.tokenize(lowercase)
        a__:     List[Any]										=	rust_tokenizer.tokenize(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     str										=	tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        a__:     str										=	rust_tokenizer.encode(lowercase    ,			add_special_tokens=lowercase)
        self.assertListEqual(lowercase    ,			lowercase)

        a__:     Tuple										=	self.get_rust_tokenizer()
        a__:     Union[str, Any]										=	tokenizer.encode(lowercase)
        a__:     List[Any]										=	rust_tokenizer.encode(lowercase)
        self.assertListEqual(lowercase    ,			lowercase)



      @slow
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Union[str, Any]										=	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '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, 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, 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]]}  # noqa: E501
        # fmt: on

        # camembert is a french model. So we also use french texts.
        a__:     int										=	[
            'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
            'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
            'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
            'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
            'telles que la traduction et la synthèse de texte.',
        ]

        self.tokenizer_integration_test_util(
            expected_encoding=lowercase    ,			model_name='camembert-base'    ,			revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'    ,			sequences=lowercase    ,			)


