
"""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 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							A						   =   0


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Optional[int]:
							A						   =   AutoImageProcessor.from_pretrained('openai/clip-vit-base-patch32'      )
							self.assertIsInstance(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Any:
							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   Path(A_      ) / 'preprocessor_config.json'
								A						   =   Path(A_      ) / 'config.json'
								json.dump(
								    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}  ,open(A_  ,'w'      )  ,)
								json.dump({'model_type': 'clip'}  ,open(A_  ,'w'      )      )

								A						   =   AutoImageProcessor.from_pretrained(A_      )
								self.assertIsInstance(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							# Ensure we can load the image processor from the feature extractor config
							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   Path(A_      ) / 'preprocessor_config.json'
								A						   =   Path(A_      ) / 'config.json'
								json.dump(
								    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}  ,open(A_  ,'w'      )  ,)
								json.dump({'model_type': 'clip'}  ,open(A_  ,'w'      )      )

								A						   =   AutoImageProcessor.from_pretrained(A_      )
								self.assertIsInstance(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   CLIPConfig()

								# Create a dummy config file with image_proceesor_type
								A						   =   Path(A_      ) / 'preprocessor_config.json'
								A						   =   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
								A						   =   AutoImageProcessor.from_pretrained(A_      ).to_dict()

								config_dict.pop('image_processor_type'      )
								A						   =   CLIPImageProcessor(**A_      )

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

								A						   =   AutoImageProcessor.from_pretrained(A_      )

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

							self.assertIsInstance(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Union[str, Any]:
							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   Path(A_      ) / 'preprocessor_config.json'
								json.dump(
								    {'image_processor_type': 'CLIPImageProcessor', 'processor_class': 'CLIPProcessor'}  ,open(A_  ,'w'      )  ,)

								A						   =   AutoImageProcessor.from_pretrained(A_      )
								self.assertIsInstance(A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[Any]:
							with self.assertRaisesRegex(
							    A_  ,'clip-base is not a local folder and is not a valid model identifier'      ):
								A						   =   AutoImageProcessor.from_pretrained('clip-base'      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Dict:
							with self.assertRaisesRegex(
							    A_  ,R'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'      ):
								A						   =   AutoImageProcessor.from_pretrained(A_  ,revision='aaaaaa'      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   int:
							with self.assertRaisesRegex(
							    A_  ,'hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'  ,):
								A						   =   AutoImageProcessor.from_pretrained('hf-internal-testing/config-no-model'      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							# If remote code is not set, we will time out when asking whether to load the model.
							with self.assertRaises(A_      ):
								A						   =   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_      ):
								A						   =   AutoImageProcessor.from_pretrained(
								    'hf-internal-testing/test_dynamic_image_processor'  ,trust_remote_code=A_      )

							A						   =   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_      )
								A						   =   AutoImageProcessor.from_pretrained(A_  ,trust_remote_code=A_      )
							self.assertEqual(reloaded_image_processor.__class__.__name__  ,'NewImageProcessor'      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   List[str]:
							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:
									A						   =   Path(A_      ) / 'preprocessor_config.json'
									A						   =   Path(A_      ) / 'config.json'
									json.dump(
									    {'feature_extractor_type': 'CLIPFeatureExtractor', 'processor_class': 'CLIPProcessor'}  ,open(A_  ,'w'      )  ,)
									json.dump({'model_type': 'clip'}  ,open(A_  ,'w'      )      )

									A						   =   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_      )
									A						   =   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 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:







							class 				lowerCAmelCase_				(      _lowercase    ):



													'''simple docstring'''


													_lowerCamelCase:    Optional[Any]      =    True

							try:
								AutoConfig.register('custom'  ,A_      )
								AutoImageProcessor.register(A_  ,A_      )
								# If remote code is not set, the default is to use local
								A						   =   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.
								A						   =   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
								A						   =   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"""


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_lowercase							   =				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
_lowercase							   =				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

_lowercase							   =				'''|'''.join(sys.argv[1:])
_lowercase							   =				re.compile(rF"""^({joined_dirs}).*?\.py$""")

_lowercase							   =				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')