





"""simple docstring"""


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       __a  (    unittest.TestCase						):



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   0



     def 		snake_case_     (			self  ):
            _lowerCamelCase             =   AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'  )
            self.assertIsInstance(_UpperCamelCase   ,	_UpperCamelCase  )



     def 		snake_case_     (			self  ):


            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'preprocessor_config.json'
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'config.json'
                   json.dump(
                       {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}   ,	open(_UpperCamelCase   ,	'w'  )   ,	)
                   json.dump({'model_type': 'clip'}   ,	open(_UpperCamelCase   ,	'w'  )  )

                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase  )
                   self.assertIsInstance(_UpperCamelCase   ,	_UpperCamelCase  )



     def 		snake_case_     (			self  ):


            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'preprocessor_config.json'
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'config.json'
                   json.dump(
                       {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}   ,	open(_UpperCamelCase   ,	'w'  )   ,	)
                   json.dump({'model_type': 'clip'}   ,	open(_UpperCamelCase   ,	'w'  )  )

                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase  )
                   self.assertIsInstance(_UpperCamelCase   ,	_UpperCamelCase  )



     def 		snake_case_     (			self  ):


            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowerCamelCase             =   CLIPConfig()

                   # Create a dummy config file with image_proceesor_type
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'preprocessor_config.json'
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'config.json'
                   json.dump(
                       {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}   ,	open(_UpperCamelCase   ,	'w'  )   ,	)
                   json.dump({'model_type': 'clip'}   ,	open(_UpperCamelCase   ,	'w'  )  )

                   # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase  ).to_dict()

                   config_dict.pop('image_processor_type'  )
                   _lowerCamelCase             =   CLIPImageProcessor(**_UpperCamelCase  )

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

                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase  )

                   # make sure private variable is not incorrectly saved
                   _lowerCamelCase             =   json.loads(config.to_json_string()  )
                   self.assertTrue('_processor_class' not in dict_as_saved  )

            self.assertIsInstance(_UpperCamelCase   ,	_UpperCamelCase  )



     def 		snake_case_     (			self  ):


            with tempfile.TemporaryDirectory() as tmpdirname:
                   _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'preprocessor_config.json'
                   json.dump(
                       {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}   ,	open(_UpperCamelCase   ,	'w'  )   ,	)

                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase  )
                   self.assertIsInstance(_UpperCamelCase   ,	_UpperCamelCase  )



     def 		snake_case_     (			self  ):


            with self.assertRaisesRegex(
                _UpperCamelCase   ,	'clip-base is not a local folder and is not a valid model identifier'  ):
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained('clip-base'  )



     def 		snake_case_     (			self  ):


            with self.assertRaisesRegex(
                _UpperCamelCase   ,	r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'  ):
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase   ,	revision='aaaaaa'  )



     def 		snake_case_     (			self  ):


            with self.assertRaisesRegex(
                _UpperCamelCase   ,	'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'   ,	):
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'  )



     def 		snake_case_     (			self  ):


            with self.assertRaises(_UpperCamelCase  ):
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained('hf-internal-testing/test_dynamic_image_processor'  )
            # If remote code is disabled, we can't load this config.
            with self.assertRaises(_UpperCamelCase  ):
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(
                       'hf-internal-testing/test_dynamic_image_processor'   ,	trust_remote_code=_UpperCamelCase  )

            _lowerCamelCase             =   AutoImageProcessor.from_pretrained(
                'hf-internal-testing/test_dynamic_image_processor'   ,	trust_remote_code=_UpperCamelCase  )
            self.assertEqual(image_processor.__class__.__name__   ,	'NewImageProcessor'  )

            # Test image processor can be reloaded.
            with tempfile.TemporaryDirectory() as tmp_dir:
                   image_processor.save_pretrained(_UpperCamelCase  )
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase   ,	trust_remote_code=_UpperCamelCase  )
            self.assertEqual(reloaded_image_processor.__class__.__name__   ,	'NewImageProcessor'  )



     def 		snake_case_     (			self  ):


            try:
                   AutoConfig.register('custom'   ,	_UpperCamelCase  )
                   AutoImageProcessor.register(_UpperCamelCase   ,	_UpperCamelCase  )
                   # Trying to register something existing in the Transformers library will raise an error
                   with self.assertRaises(_UpperCamelCase  ):
                          AutoImageProcessor.register(_UpperCamelCase   ,	_UpperCamelCase  )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                          _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'preprocessor_config.json'
                          _lowerCamelCase             =   Path(_UpperCamelCase  ) / 'config.json'
                          json.dump(
                              {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}   ,	open(_UpperCamelCase   ,	'w'  )   ,	)
                          json.dump({'model_type': 'clip'}   ,	open(_UpperCamelCase   ,	'w'  )  )

                          _lowerCamelCase             =   CustomImageProcessor.from_pretrained(_UpperCamelCase  )

                   # 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(_UpperCamelCase  )
                          _lowerCamelCase             =   AutoImageProcessor.from_pretrained(_UpperCamelCase  )
                          self.assertIsInstance(_UpperCamelCase   ,	_UpperCamelCase  )

            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 		snake_case_     (			self  ):







            class       __a  (    snake_case__						):
                 SCREAMING_SNAKE_CASE__					: Optional[Any]									=     True

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

                   # If remote is enabled, we load from the Hub
                   _lowerCamelCase             =   AutoImageProcessor.from_pretrained(
                       'hf-internal-testing/test_dynamic_image_processor'   ,	trust_remote_code=_UpperCamelCase  )
                   self.assertEqual(image_processor.__class__.__name__   ,	'NewImageProcessor'  )
                   self.assertTrue(not hasattr(_UpperCamelCase   ,	'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]









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCamelCase__		 =     {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     ['''ConvNextFeatureExtractor''']
    UpperCamelCase__		 =     ['''ConvNextImageProcessor''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextForImageClassification''',
        '''ConvNextModel''',
        '''ConvNextPreTrainedModel''',
        '''ConvNextBackbone''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCamelCase__		 =     [
        '''TFConvNextForImageClassification''',
        '''TFConvNextModel''',
        '''TFConvNextPreTrainedModel''',
    ]

if TYPE_CHECKING:
    from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_convnext import ConvNextFeatureExtractor
        from .image_processing_convnext import ConvNextImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnext import (
            CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextBackbone,
            ConvNextForImageClassification,
            ConvNextModel,
            ConvNextPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
    import sys

    UpperCamelCase__		 =     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

