






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_SCREAMING_SNAKE_CASE   =  {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Dict					='''maskformer'''
       __lowerCAmelCase    :		List[str]					={'''hidden_size''': '''mask_feature_size'''}
       __lowerCAmelCase    :		Tuple					=['''resnet''', '''swin''']
       __lowerCAmelCase    :		Tuple					=['''detr''']





       def __init__(				self	:Optional[Any],      snake_case	:int = 256,      snake_case	:int = 256,      snake_case	:float = 0.1,      snake_case	:bool = False,      snake_case	:Optional[Dict] = None,      snake_case	:Optional[Dict] = None,      snake_case	:float = 0.0_2,      snake_case	:float = 1.0,      snake_case	:float = 1.0,      snake_case	:float = 1.0,      snake_case	:float = 2_0.0,      snake_case	:Optional[bool] = None,      **snake_case	:Any,      ):



          """simple docstring"""

          if backbone_config is None:
             # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
             _lowercase              =SwinConfig(
                 image_size=384,      in_channels=3,      patch_size=4,      embed_dim=128,      depths=[2, 2, 18, 2],      num_heads=[4, 8, 16, 32],      window_size=12,      drop_path_rate=0.3,      out_features=['stage1', 'stage2', 'stage3', 'stage4'],      )

          if isinstance(snake_case,      snake_case):
             _lowercase              =backbone_config.pop('model_type')
             _lowercase              =CONFIG_MAPPING[backbone_model_type]
             _lowercase              =config_class.from_dict(snake_case)

          # verify that the backbone is supported
          if backbone_config.model_type not in self.backbones_supported:
             logger.warning_once(
                 f'''Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '''
                 f'''Supported model types: {",".join(self.backbones_supported)}''')

          if decoder_config is None:
             # fall back to https://huggingface.co/facebook/detr-resnet-50
             _lowercase              =DetrConfig()
          else:
             # verify that the decoder is supported
             _lowercase              =(
                 decoder_config.pop('model_type') if isinstance(snake_case,      snake_case) else decoder_config.model_type
             )
             if decoder_type not in self.decoders_supported:
                raise ValueError(
                    f'''Transformer Decoder {decoder_type} not supported, please use one of'''
                    f''' {",".join(self.decoders_supported)}''')
             if isinstance(snake_case,      snake_case):
                _lowercase              =CONFIG_MAPPING[decoder_type]
                _lowercase              =config_class.from_dict(snake_case)

          _lowercase              =backbone_config
          _lowercase              =decoder_config
          # main feature dimension for the model
          _lowercase              =fpn_feature_size
          _lowercase              =mask_feature_size
          # initializer
          _lowercase              =init_std
          _lowercase              =init_xavier_std
          # Hungarian matcher && loss
          _lowercase              =cross_entropy_weight
          _lowercase              =dice_weight
          _lowercase              =mask_weight
          _lowercase              =use_auxiliary_loss
          _lowercase              =no_object_weight
          _lowercase              =output_auxiliary_logits

          _lowercase              =self.decoder_config.encoder_attention_heads
          _lowercase              =self.decoder_config.num_hidden_layers
          super().__init__(**snake_case)





       @classmethod
       def 				UpperCamelCase__						(				cls	:Tuple,      snake_case	:PretrainedConfig,      snake_case	:PretrainedConfig,      **snake_case	:Optional[Any]):



          """simple docstring"""

          return cls(
              backbone_config=snake_case,      decoder_config=snake_case,      **snake_case,      )





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =copy.deepcopy(self.__dict__)
          _lowercase              =self.backbone_config.to_dict()
          _lowercase              =self.decoder_config.to_dict()
          _lowercase              =self.__class__.model_type
          return output










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 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =0





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32')
          self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

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

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

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

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

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

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

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

             config_dict.pop('image_processor_type')
             _lowercase              =CLIPImageProcessor(**snake_case)

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

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)

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

          self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

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

             _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
             self.assertIsInstance(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Dict):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[Any]):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

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

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





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

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

                _lowercase              =CustomImageProcessor.from_pretrained(snake_case)

             # 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(snake_case)
                _lowercase              =AutoImageProcessor.from_pretrained(snake_case)
                self.assertIsInstance(snake_case,      snake_case)

          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	:str):



          """simple docstring"""

          class 					SCREAMING_SNAKE_CASE_  (       _a ):
                 """simple docstring"""


                 __lowerCAmelCase    :		Union[str, Any]					=True

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

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


