



'''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     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Union[str, Any]    						=      0




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    List[str]    						=      AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""						)
          self.assertIsInstance(snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Dict						):
          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case__  :    Optional[int]    						=      Path(snake_case_						) / """preprocessor_config.json"""
                snake_case__  :    List[str]    						=      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"""						)						)

                snake_case__  :    Union[str, Any]    						=      AutoImageProcessor.from_pretrained(snake_case_						)
                self.assertIsInstance(snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          # Ensure we can load the image processor from the feature extractor config
          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case__  :    Union[str, Any]    						=      Path(snake_case_						) / """preprocessor_config.json"""
                snake_case__  :    Dict    						=      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"""						)						)

                snake_case__  :    Tuple    						=      AutoImageProcessor.from_pretrained(snake_case_						)
                self.assertIsInstance(snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case__  :    Optional[Any]    						=      CLIPConfig()

                # Create a dummy config file with image_proceesor_type
                snake_case__  :    List[str]    						=      Path(snake_case_						) / """preprocessor_config.json"""
                snake_case__  :    List[Any]    						=      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
                snake_case__  :    Any    						=      AutoImageProcessor.from_pretrained(snake_case_						).to_dict()

                config_dict.pop("""image_processor_type"""						)
                snake_case__  :    Tuple    						=      CLIPImageProcessor(**snake_case_						)

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

                snake_case__  :    Union[str, Any]    						=      AutoImageProcessor.from_pretrained(snake_case_						)

                # make sure private variable is not incorrectly saved
                snake_case__  :    Tuple    						=      json.loads(config.to_json_string()						)
                self.assertTrue("""_processor_class""" not in dict_as_saved						)

          self.assertIsInstance(snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Any						):
          with tempfile.TemporaryDirectory() as tmpdirname:
                snake_case__  :    Tuple    						=      Path(snake_case_						) / """preprocessor_config.json"""
                json.dump(
                    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} ,     open(snake_case_ ,     """w"""						) ,     )

                snake_case__  :    List[Any]    						=      AutoImageProcessor.from_pretrained(snake_case_						)
                self.assertIsInstance(snake_case_ ,     snake_case_						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          with self.assertRaisesRegex(
              snake_case_ ,     """clip-base is not a local folder and is not a valid model identifier"""						):
                snake_case__  :    Union[str, Any]    						=      AutoImageProcessor.from_pretrained("""clip-base"""						)




    def lowerCamelCase       (    self					:     Any						):
          with self.assertRaisesRegex(
              snake_case_ ,     r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""						):
                snake_case__  :    List[str]    						=      AutoImageProcessor.from_pretrained(snake_case_ ,     revision="""aaaaaa"""						)




    def lowerCamelCase       (    self					:     Any						):
          with self.assertRaisesRegex(
              snake_case_ ,     """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" ,     ):
                snake_case__  :    Optional[int]    						=      AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""						)




    def lowerCamelCase       (    self					:     List[str]						):
          # If remote code is not set, we will time out when asking whether to load the model.
          with self.assertRaises(snake_case_						):
                snake_case__  :    List[str]    						=      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_						):
                snake_case__  :    str    						=      AutoImageProcessor.from_pretrained(
                    """hf-internal-testing/test_dynamic_image_processor""" ,     trust_remote_code=snake_case_						)

          snake_case__  :    Tuple    						=      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_						)
                snake_case__  :    Tuple    						=      AutoImageProcessor.from_pretrained(snake_case_ ,     trust_remote_code=snake_case_						)
          self.assertEqual(reloaded_image_processor.__class__.__name__ ,     """NewImageProcessor"""						)




    def lowerCamelCase       (    self					:     str						):
          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:
                      snake_case__  :    int    						=      Path(snake_case_						) / """preprocessor_config.json"""
                      snake_case__  :    int    						=      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"""						)						)

                      snake_case__  :    Dict    						=      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_						)
                      snake_case__  :    int    						=      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 lowerCamelCase       (    self					:     Tuple						):







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







              lowercase					=		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
                snake_case__  :    int    						=      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.
                snake_case__  :    int    						=      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
                snake_case__  :    Dict    						=      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]





'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a          =		logging.get_logger(__name__)

__a          =		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







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







    lowercase					=		"glpn"




    def __init__(    self					:     Optional[Any] ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Dict=4 ,     snake_case_					:     List[Any]=[2, 2, 2, 2] ,     snake_case_					:     int=[8, 4, 2, 1] ,     snake_case_					:     List[str]=[32, 64, 160, 256] ,     snake_case_					:     Tuple=[7, 3, 3, 3] ,     snake_case_					:     List[Any]=[4, 2, 2, 2] ,     snake_case_					:     Tuple=[1, 2, 5, 8] ,     snake_case_					:     List[str]=[4, 4, 4, 4] ,     snake_case_					:     Optional[int]="gelu" ,     snake_case_					:     Dict=0.0 ,     snake_case_					:     Union[str, Any]=0.0 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Any=1E-6 ,     snake_case_					:     Dict=64 ,     snake_case_					:     Tuple=10 ,     snake_case_					:     List[Any]=-1 ,     **snake_case_					:     Optional[Any] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    Optional[Any]    						=      num_channels
          snake_case__  :    Dict    						=      num_encoder_blocks
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Union[str, Any]    						=      sr_ratios
          snake_case__  :    Tuple    						=      hidden_sizes
          snake_case__  :    Optional[Any]    						=      patch_sizes
          snake_case__  :    int    						=      strides
          snake_case__  :    List[Any]    						=      mlp_ratios
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      hidden_dropout_prob
          snake_case__  :    Optional[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      initializer_range
          snake_case__  :    List[str]    						=      drop_path_rate
          snake_case__  :    int    						=      layer_norm_eps
          snake_case__  :    Tuple    						=      decoder_hidden_size
          snake_case__  :    List[Any]    						=      max_depth
          snake_case__  :    Dict    						=      head_in_index
