


'''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 	a__		(     self							)    ->  int:
					lowercase						:		List[Any]   					=		0

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"							)
					self.assertIsInstance(a_							,      a_							)

				def 	a__		(     self							)    ->  List[str]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						lowercase						:		Optional[Any]   					=		Path(a_							) / "preprocessor_config.json"
						lowercase						:		str   					=		Path(a_							) / "config.json"
						json.dump(
						    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,      open(a_							,      "w"							)							,      )
						json.dump({"model_type": "clip"}							,      open(a_							,      "w"							)							)

						lowercase						:		int   					=		AutoImageProcessor.from_pretrained(a_							)
						self.assertIsInstance(a_							,      a_							)

				def 	a__		(     self							)    ->  Dict:
					# Ensure we can load the image processor from the feature extractor config
					with tempfile.TemporaryDirectory() as tmpdirname:
						lowercase						:		Tuple   					=		Path(a_							) / "preprocessor_config.json"
						lowercase						:		Tuple   					=		Path(a_							) / "config.json"
						json.dump(
						    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}							,      open(a_							,      "w"							)							,      )
						json.dump({"model_type": "clip"}							,      open(a_							,      "w"							)							)

						lowercase						:		Optional[Any]   					=		AutoImageProcessor.from_pretrained(a_							)
						self.assertIsInstance(a_							,      a_							)

				def 	a__		(     self							)    ->  List[Any]:
					with tempfile.TemporaryDirectory() as tmpdirname:
						lowercase						:		Optional[int]   					=		CLIPConfig()

						# Create a dummy config file with image_proceesor_type
						lowercase						:		List[str]   					=		Path(a_							) / "preprocessor_config.json"
						lowercase						:		Any   					=		Path(a_							) / "config.json"
						json.dump(
						    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,      open(a_							,      "w"							)							,      )
						json.dump({"model_type": "clip"}							,      open(a_							,      "w"							)							)

						# remove image_processor_type to make sure config.json alone is enough to load image processor locally
						lowercase						:		List[str]   					=		AutoImageProcessor.from_pretrained(a_							).to_dict()

						config_dict.pop("image_processor_type"							)
						lowercase						:		Optional[Any]   					=		CLIPImageProcessor(**a_							)

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

						lowercase						:		List[str]   					=		AutoImageProcessor.from_pretrained(a_							)

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

					self.assertIsInstance(a_							,      a_							)

				def 	a__		(     self							)    ->  int:
					with tempfile.TemporaryDirectory() as tmpdirname:
						lowercase						:		Optional[Any]   					=		Path(a_							) / "preprocessor_config.json"
						json.dump(
						    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}							,      open(a_							,      "w"							)							,      )

						lowercase						:		Optional[int]   					=		AutoImageProcessor.from_pretrained(a_							)
						self.assertIsInstance(a_							,      a_							)

				def 	a__		(     self							)    ->  Dict:
					with self.assertRaisesRegex(
					    a_							,      "clip-base is not a local folder and is not a valid model identifier"							):
						lowercase						:		List[Any]   					=		AutoImageProcessor.from_pretrained("clip-base"							)

				def 	a__		(     self							)    ->  Tuple:
					with self.assertRaisesRegex(
					    a_							,      R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"							):
						lowercase						:		Optional[Any]   					=		AutoImageProcessor.from_pretrained(a_							,      revision="aaaaaa"							)

				def 	a__		(     self							)    ->  Dict:
					with self.assertRaisesRegex(
					    a_							,      "hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."							,      ):
						lowercase						:		Optional[Any]   					=		AutoImageProcessor.from_pretrained("hf-internal-testing/config-no-model"							)

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

					lowercase						:		List[Any]   					=		AutoImageProcessor.from_pretrained(
					    "hf-internal-testing/test_dynamic_image_processor"							,      trust_remote_code=a_							)
					self.assertEqual(image_processor.__class__.__name__							,      "NewImageProcessor"							)

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

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

						with tempfile.TemporaryDirectory() as tmpdirname:
							lowercase						:		int   					=		Path(a_							) / "preprocessor_config.json"
							lowercase						:		Optional[int]   					=		Path(a_							) / "config.json"
							json.dump(
							    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}							,      open(a_							,      "w"							)							,      )
							json.dump({"model_type": "clip"}							,      open(a_							,      "w"							)							)

							lowercase						:		Optional[Any]   					=		CustomImageProcessor.from_pretrained(a_							)

						# 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(a_							)
							lowercase						:		List[str]   					=		AutoImageProcessor.from_pretrained(a_							)
							self.assertIsInstance(a_							,      a_							)

					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 	a__		(     self							)    ->  Optional[Any]:




					class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


									'''simple docstring'''


									_snake_case						       =				True

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

						# If remote is enabled, we load from the Hub
						lowercase						:		str   					=		AutoImageProcessor.from_pretrained(
						    "hf-internal-testing/test_dynamic_image_processor"							,      trust_remote_code=a_							)
						self.assertEqual(image_processor.__class__.__name__							,      "NewImageProcessor"							)
						self.assertTrue(not hasattr(a_							,      "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 ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .multicontrolnet import MultiControlNetModel
					from .pipeline_controlnet import StableDiffusionControlNetPipeline
					from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
					from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
					from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


