"""simple docstring"""






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

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir


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

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402


lowerCAmelCase__  :  str						     =	get_tests_dir('fixtures')
lowerCAmelCase__  :  List[str]						     =	get_tests_dir('fixtures/dummy_feature_extractor_config.json')
lowerCAmelCase__  :  List[Any]						     =	get_tests_dir('fixtures/dummy-config.json')
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							0







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-base-960h'			)
									self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__			)
									self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									with tempfile.TemporaryDirectory() as tmpdirname:
													UpperCAmelCase__             =							WavaVecaConfig()

													# remove feature_extractor_type to make sure config.json alone is enough to load feature processor locally
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__			).to_dict()

													config_dict.pop('feature_extractor_type'			)
													UpperCAmelCase__             =							WavaVecaFeatureExtractor(**lowerCamelCase__			)

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

													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__			)

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

									self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__			)
									self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									with self.assertRaisesRegex(
									    lowerCamelCase__    ,'bert-base is not a local folder and is not a valid model identifier'			):
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained('bert-base'			)







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									with self.assertRaisesRegex(
									    lowerCamelCase__    ,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'			):
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__    ,revision='aaaaaa'			)







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									with self.assertRaisesRegex(
									    lowerCamelCase__    ,'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'    ,):
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained('hf-internal-testing/config-no-model'			)







					def 		__lowerCAmelCase						(				self   :	int			):
									# If remote code is not set, we will time out when asking whether to load the model.
									with self.assertRaises(lowerCamelCase__			):
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(
													    'hf-internal-testing/test_dynamic_feature_extractor'			)
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(lowerCamelCase__			):
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(
													    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=lowerCamelCase__			)

									UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(
									    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=lowerCamelCase__			)
									self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'			)

									# Test feature extractor can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
													feature_extractor.save_pretrained(lowerCamelCase__			)
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__    ,trust_remote_code=lowerCamelCase__			)
									self.assertEqual(reloaded_feature_extractor.__class__.__name__    ,'NewFeatureExtractor'			)







					def 		__lowerCAmelCase						(				self   :	str			):
									try:
													AutoConfig.register('custom'    ,lowerCamelCase__			)
													AutoFeatureExtractor.register(lowerCamelCase__    ,lowerCamelCase__			)
													# Trying to register something existing in the Transformers library will raise an error
													with self.assertRaises(lowerCamelCase__			):
																	AutoFeatureExtractor.register(lowerCamelCase__    ,lowerCamelCase__			)

													# Now that the config is registered, it can be used as any other config with the auto-API
													UpperCAmelCase__             =							CustomFeatureExtractor.from_pretrained(lowerCamelCase__			)
													with tempfile.TemporaryDirectory() as tmp_dir:
																	feature_extractor.save_pretrained(lowerCamelCase__			)
																	UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(lowerCamelCase__			)
																	self.assertIsInstance(lowerCamelCase__    ,lowerCamelCase__			)

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







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									class 				snake_case       ( __UpperCAmelCase				):




														"""simple docstring"""

														snake_case__				    = True

									try:
													AutoConfig.register('custom'    ,lowerCamelCase__			)
													AutoFeatureExtractor.register(lowerCamelCase__    ,lowerCamelCase__			)
													# If remote code is not set, the default is to use local
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(
													    'hf-internal-testing/test_dynamic_feature_extractor'			)
													self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'			)
													self.assertTrue(feature_extractor.is_local			)

													# If remote code is disabled, we load the local one.
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(
													    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=lowerCamelCase__			)
													self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'			)
													self.assertTrue(feature_extractor.is_local			)

													# If remote is enabled, we load from the Hub
													UpperCAmelCase__             =							AutoFeatureExtractor.from_pretrained(
													    'hf-internal-testing/test_dynamic_feature_extractor'    ,trust_remote_code=lowerCamelCase__			)
													self.assertEqual(feature_extractor.__class__.__name__    ,'NewFeatureExtractor'			)
													self.assertTrue(not hasattr(lowerCamelCase__    ,'is_local'			)			)

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






'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()

 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

 parser.add_argument(
     '''--txt2img_unclip''',
     default='''kakaobrain/karlo-v1-alpha''',
     type=str,
     required=False,
     help='''The pretrained txt2img unclip.''',
 )

 lowerCAmelCase__											=	parser.parse_args()

 lowerCAmelCase__											=	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

 lowerCAmelCase__											=	CLIPImageProcessor()
 lowerCAmelCase__											=	CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

 lowerCAmelCase__											=	UnCLIPImageVariationPipeline(
     decoder=txtaimg.decoder,
     text_encoder=txtaimg.text_encoder,
     tokenizer=txtaimg.tokenizer,
     text_proj=txtaimg.text_proj,
     feature_extractor=feature_extractor,
     image_encoder=image_encoder,
     super_res_first=txtaimg.super_res_first,
     super_res_last=txtaimg.super_res_last,
     decoder_scheduler=txtaimg.decoder_scheduler,
     super_res_scheduler=txtaimg.super_res_scheduler,
 )

 imgaimg.save_pretrained(args.dump_path)

