"""simple docstring"""




import inspect
import unittest

from transformers import ViTMSNConfig
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 torch import nn

    from transformers import ViTMSNForImageClassification, ViTMSNModel
    from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=13			,			__lowercase=30			,			__lowercase=2			,			__lowercase=3			,			__lowercase=True			,			__lowercase=True			,			__lowercase=32			,			__lowercase=5			,			__lowercase=4			,			__lowercase=37			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=10			,			__lowercase=0.02			,			__lowercase=None			,			)	->     str:
        __UpperCamelCase					:Union[str, Any]        =		parent
        __UpperCamelCase					:Dict        =		batch_size
        __UpperCamelCase					:Optional[int]        =		image_size
        __UpperCamelCase					:int        =		patch_size
        __UpperCamelCase					:List[str]        =		num_channels
        __UpperCamelCase					:Any        =		is_training
        __UpperCamelCase					:int        =		use_labels
        __UpperCamelCase					:str        =		hidden_size
        __UpperCamelCase					:Dict        =		num_hidden_layers
        __UpperCamelCase					:Union[str, Any]        =		num_attention_heads
        __UpperCamelCase					:str        =		intermediate_size
        __UpperCamelCase					:str        =		hidden_act
        __UpperCamelCase					:Union[str, Any]        =		hidden_dropout_prob
        __UpperCamelCase					:Tuple        =		attention_probs_dropout_prob
        __UpperCamelCase					:Optional[int]        =		type_sequence_label_size
        __UpperCamelCase					:Any        =		initializer_range
        __UpperCamelCase					:int        =		scope

        # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        __UpperCamelCase					:List[Any]        =		(image_size // patch_size) ** 2
        __UpperCamelCase					:Optional[Any]        =		num_patches + 1







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[str]        =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        __UpperCamelCase					:Any        =		None
        if self.use_labels:
            __UpperCamelCase					:Optional[Any]        =		ids_tensor([self.batch_size]			,			self.type_sequence_label_size)

        __UpperCamelCase					:Any        =		self.get_config()

        return config, pixel_values, labels







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        return ViTMSNConfig(
            image_size=self.image_size			,			patch_size=self.patch_size			,			num_channels=self.num_channels			,			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			,			initializer_range=self.initializer_range			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Optional[int]:
        __UpperCamelCase					:str        =		ViTMSNModel(config=lowercase__)
        model.to(lowercase__)
        model.eval()
        __UpperCamelCase					:List[str]        =		model(lowercase__)
        self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		self.type_sequence_label_size
        __UpperCamelCase					:str        =		ViTMSNForImageClassification(lowercase__)
        model.to(lowercase__)
        model.eval()
        __UpperCamelCase					:Tuple        =		model(lowercase__			,			labels=lowercase__)
        print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}''')
        print('''Labels: {labels}''')
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size))

        # test greyscale images
        __UpperCamelCase					:str        =		1
        __UpperCamelCase					:Dict        =		ViTMSNForImageClassification(lowercase__)
        model.to(lowercase__)
        model.eval()

        __UpperCamelCase					:List[str]        =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
        __UpperCamelCase					:Optional[int]        =		model(lowercase__)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.type_sequence_label_size))







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Union[str, Any]        =		self.prepare_config_and_inputs()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		config_and_inputs
        __UpperCamelCase					:Dict        =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_torch
class      lowerCamelCase_					(       lowerCamelCase__						,     lowerCamelCase__						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						List[Any]      =				(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
    a__     :						Optional[int]      =				(
        {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
        if is_torch_available()
        else {}
    )

    a__     :						Optional[Any]      =				False
    a__     :						Dict      =				False
    a__     :						Tuple      =				False
    a__     :						str      =				False







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:int        =		ViTMSNModelTester(self)
        __UpperCamelCase					:Optional[int]        =		ConfigTester(self			,			config_class=lowercase__			,			has_text_modality=lowercase__			,			hidden_size=37)







    def       UpperCamelCase__       (     self)	->     int:
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''ViTMSN does not use inputs_embeds''')
    def       UpperCamelCase__       (     self)	->     Tuple:
        pass







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase  ,	__UpperCamelCase					:Any        =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase					:Any        =		model_class(lowercase__)
            self.assertIsInstance(model.get_input_embeddings()			,			(nn.Module))
            __UpperCamelCase					:str        =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase__			,			nn.Linear))







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase  ,	__UpperCamelCase					:str        =		self.model_tester.prepare_config_and_inputs_for_common()

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

            __UpperCamelCase					:int        =		['''pixel_values''']
            self.assertListEqual(arg_names[:1]			,			lowercase__)







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:Any        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase__)







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[Any]        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase__)







    @slow
    def       UpperCamelCase__       (     self)	->     int:
        for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __UpperCamelCase					:Optional[Any]        =		ViTMSNModel.from_pretrained(lowercase__)
            self.assertIsNotNone(lowercase__)



def lowerCamelCase  (       ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image


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






    '''simple docstring'''






    @cached_property
    def       UpperCamelCase__       (     self)	->     int:
        return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small''') if is_vision_available() else None







    @slow
    def       UpperCamelCase__       (     self)	->     Tuple:
        torch.manual_seed(2)
        __UpperCamelCase					:Tuple        =		ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small''').to(lowercase__)

        __UpperCamelCase					:Dict        =		self.default_image_processor
        __UpperCamelCase					:Any        =		prepare_img()
        __UpperCamelCase					:Optional[Any]        =		image_processor(images=lowercase__			,			return_tensors='''pt''').to(lowercase__)

        # forward pass
        with torch.no_grad():
            __UpperCamelCase					:int        =		model(**lowercase__)

        # verify the logits
        __UpperCamelCase					:List[str]        =		torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape			,			lowercase__)

        __UpperCamelCase					:Optional[int]        =		torch.tensor([-0.08_03, -0.44_54, -0.23_75]).to(lowercase__)

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






import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:str        =		0







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''')
        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Dict        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Union[str, Any]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        # Ensure we can load the image processor from the feature extractor config
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:str        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Union[str, Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:int        =		CLIPConfig()

            # Create a dummy config file with image_proceesor_type
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            __UpperCamelCase					:Optional[Any]        =		Path(__lowercase) / '''config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
            json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

            # remove image_processor_type to make sure config.json alone is enough to load image processor locally
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(__lowercase).to_dict()

            config_dict.pop('''image_processor_type''')
            __UpperCamelCase					:List[str]        =		CLIPImageProcessor(**__lowercase)

            # save in new folder
            model_config.save_pretrained(__lowercase)
            config.save_pretrained(__lowercase)

            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained(__lowercase)

            # make sure private variable is not incorrectly saved
            __UpperCamelCase					:Union[str, Any]        =		json.loads(config.to_json_string())
            self.assertTrue('''_processor_class''' not in dict_as_saved)

        self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        with tempfile.TemporaryDirectory() as tmpdirname:
            __UpperCamelCase					:Tuple        =		Path(__lowercase) / '''preprocessor_config.json'''
            json.dump(
                {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)

            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(__lowercase)
            self.assertIsInstance(__lowercase			,			__lowercase)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        with self.assertRaisesRegex(
            __lowercase			,			'''clip-base is not a local folder and is not a valid model identifier'''):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''clip-base''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        with self.assertRaisesRegex(
            __lowercase			,			r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''):
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained(__lowercase			,			revision='''aaaaaa''')







    def       UpperCamelCase__       (     self)	->     List[str]:
        with self.assertRaisesRegex(
            __lowercase			,			'''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''			,			):
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''')







    def       UpperCamelCase__       (     self)	->     str:
        # If remote code is not set, we will time out when asking whether to load the model.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Dict        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
        # If remote code is disabled, we can't load this config.
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)

        __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
            '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
        self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')

        # Test image processor can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
            image_processor.save_pretrained(__lowercase)
            __UpperCamelCase					:List[Any]        =		AutoImageProcessor.from_pretrained(__lowercase			,			trust_remote_code=__lowercase)
        self.assertEqual(reloaded_image_processor.__class__.__name__			,			'''NewImageProcessor''')







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # Trying to register something existing in the Transformers library will raise an error
            with self.assertRaises(__lowercase):
                AutoImageProcessor.register(__lowercase			,			__lowercase)

            with tempfile.TemporaryDirectory() as tmpdirname:
                __UpperCamelCase					:int        =		Path(__lowercase) / '''preprocessor_config.json'''
                __UpperCamelCase					:List[str]        =		Path(__lowercase) / '''config.json'''
                json.dump(
                    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}			,			open(__lowercase			,			'''w''')			,			)
                json.dump({'''model_type''': '''clip'''}			,			open(__lowercase			,			'''w'''))

                __UpperCamelCase					:int        =		CustomImageProcessor.from_pretrained(__lowercase)

            # Now that the config is registered, it can be used as any other config with the auto-API
            with tempfile.TemporaryDirectory() as tmp_dir:
                image_processor.save_pretrained(__lowercase)
                __UpperCamelCase					:int        =		AutoImageProcessor.from_pretrained(__lowercase)
                self.assertIsInstance(__lowercase			,			__lowercase)

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







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


        class      lowerCamelCase_					(       UpperCAmelCase_			):






            '''simple docstring'''






            a__     :						List[str]      =				True

        try:
            AutoConfig.register('''custom'''			,			__lowercase)
            AutoImageProcessor.register(__lowercase			,			__lowercase)
            # If remote code is not set, the default is to use local
            __UpperCamelCase					:str        =		AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''')
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

            # If remote code is disabled, we load the local one.
            __UpperCamelCase					:Optional[Any]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(image_processor.is_local)

            # If remote is enabled, we load from the Hub
            __UpperCamelCase					:List[str]        =		AutoImageProcessor.from_pretrained(
                '''hf-internal-testing/test_dynamic_image_processor'''			,			trust_remote_code=__lowercase)
            self.assertEqual(image_processor.__class__.__name__			,			'''NewImageProcessor''')
            self.assertTrue(not hasattr(__lowercase			,			'''is_local'''))

        finally:
            if "custom" in CONFIG_MAPPING._extra_content:
                del CONFIG_MAPPING._extra_content["custom"]
            if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





