

'''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 					_snake_case		(unittest.TestCase):






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  0






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  AutoImageProcessor.from_pretrained("openai/clip-vit-base-patch32"     )
				self.assertIsInstance(_a			,_a     )






			def 		UpperCamelCase__							(  self     ):
				with tempfile.TemporaryDirectory() as tmpdirname:
					UpperCAmelCase_     :      Tuple						=  Path(_a     ) / '''preprocessor_config.json'''
					UpperCAmelCase_     :      Optional[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"     )     )

					UpperCAmelCase_     :      List[str]						=  AutoImageProcessor.from_pretrained(_a     )
					self.assertIsInstance(_a			,_a     )






			def 		UpperCamelCase__							(  self     ):
				# Ensure we can load the image processor from the feature extractor config
				with tempfile.TemporaryDirectory() as tmpdirname:
					UpperCAmelCase_     :      Optional[int]						=  Path(_a     ) / '''preprocessor_config.json'''
					UpperCAmelCase_     :      Any						=  Path(_a     ) / '''config.json'''
					json.dump(
					    {"feature_extractor_type": "CLIPFeatureExtractor", "processor_class": "CLIPProcessor"}			,open(_a			,"w"     )			,)
					json.dump({"model_type": "clip"}			,open(_a			,"w"     )     )

					UpperCAmelCase_     :      Optional[Any]						=  AutoImageProcessor.from_pretrained(_a     )
					self.assertIsInstance(_a			,_a     )






			def 		UpperCamelCase__							(  self     ):
				with tempfile.TemporaryDirectory() as tmpdirname:
					UpperCAmelCase_     :      Dict						=  CLIPConfig()

					# Create a dummy config file with image_proceesor_type
					UpperCAmelCase_     :      Tuple						=  Path(_a     ) / '''preprocessor_config.json'''
					UpperCAmelCase_     :      List[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"     )     )

					# remove image_processor_type to make sure config.json alone is enough to load image processor locally
					UpperCAmelCase_     :      Tuple						=  AutoImageProcessor.from_pretrained(_a     ).to_dict()

					config_dict.pop("image_processor_type"     )
					UpperCAmelCase_     :      Tuple						=  CLIPImageProcessor(**_a     )

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

					UpperCAmelCase_     :      List[str]						=  AutoImageProcessor.from_pretrained(_a     )

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

				self.assertIsInstance(_a			,_a     )






			def 		UpperCamelCase__							(  self     ):
				with tempfile.TemporaryDirectory() as tmpdirname:
					UpperCAmelCase_     :      Optional[int]						=  Path(_a     ) / '''preprocessor_config.json'''
					json.dump(
					    {"image_processor_type": "CLIPImageProcessor", "processor_class": "CLIPProcessor"}			,open(_a			,"w"     )			,)

					UpperCAmelCase_     :      List[str]						=  AutoImageProcessor.from_pretrained(_a     )
					self.assertIsInstance(_a			,_a     )






			def 		UpperCamelCase__							(  self     ):
				with self.assertRaisesRegex(
				    _a			,"clip-base is not a local folder and is not a valid model identifier"     ):
					UpperCAmelCase_     :      Dict						=  AutoImageProcessor.from_pretrained("clip-base"     )






			def 		UpperCamelCase__							(  self     ):
				with self.assertRaisesRegex(
				    _a			,R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"     ):
					UpperCAmelCase_     :      List[str]						=  AutoImageProcessor.from_pretrained(_a			,revision="aaaaaa"     )






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






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

				UpperCAmelCase_     :      Union[str, 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     )
					UpperCAmelCase_     :      Optional[Any]						=  AutoImageProcessor.from_pretrained(_a			,trust_remote_code=_a     )
				self.assertEqual(reloaded_image_processor.__class__.__name__			,"NewImageProcessor"     )






			def 		UpperCamelCase__							(  self     ):
				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:
						UpperCAmelCase_     :      int						=  Path(_a     ) / '''preprocessor_config.json'''
						UpperCAmelCase_     :      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"     )     )

						UpperCAmelCase_     :      int						=  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     )
						UpperCAmelCase_     :      Optional[Any]						=  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 		UpperCamelCase__							(  self     ):
				class 					_snake_case		(__lowercase):
							__A  :	Tuple        =True

				try:
					AutoConfig.register("custom"			,_a     )
					AutoImageProcessor.register(_a			,_a     )
					# If remote code is not set, the default is to use local
					UpperCAmelCase_     :      str						=  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.
					UpperCAmelCase_     :      int						=  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
					UpperCAmelCase_     :      Dict						=  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]







import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[Any]             =       MgpstrTokenizer
							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Union[str, Any]             =       {}
							UpperCAmelCase__			:		List[Any]             =       False


							def 							__lowercase							(     self			)       ->       Any:
												super().setUp()

												# fmt: off
												_a      :       Tuple   =						['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
												# fmt: on
												_a      :       Optional[int]   =						dict(zip(_a			,				range(len(_a			)			)			)			)

												_a      :       Any   =						os.path.join(self.tmpdirname			,				VOCAB_FILES_NAMES['''vocab_file''']			)
												with open(self.vocab_file			,				'''w'''			,				encoding='''utf-8'''			) as fp:
																	fp.write(json.dumps(_a			) + '''\n'''			)


							def 							__lowercase							(     self			,				**_a			)       ->       Dict:
												return MgpstrTokenizer.from_pretrained(self.tmpdirname			,				**_a			)


							def 							__lowercase							(     self			,				_a			)       ->       Tuple:
												_a      :       List[str]   =						'''tester'''
												_a      :       Optional[Any]   =						'''tester'''
												return input_text, output_text


							@unittest.skip('''MGP-STR always lower cases letters.'''			)
							def 							__lowercase							(     self			)       ->       Any:
												pass


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Union[str, Any]   =						self.get_tokenizers(do_lower_case=_a			)
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a      :       int   =						'''[SPECIAL_TOKEN]'''

																						tokenizer.add_special_tokens({'''cls_token''': special_token}			)
																						_a      :       Tuple   =						tokenizer.encode([special_token]			,				add_special_tokens=_a			)
																						self.assertEqual(len(_a			)			,				1			)

																						_a      :       Tuple   =						tokenizer.decode(_a			,				skip_special_tokens=_a			)
																						self.assertTrue(special_token not in decoded			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						self.get_tokenizers()
												for tokenizer in tokenizers:
																	with self.subTest(F"""{tokenizer.__class__.__name__}"""			):
																						_a ,   _a      :       int   =						self.get_input_output_texts(_a			)

																						_a      :       List[str]   =						tokenizer.tokenize(_a			)
																						_a      :       Optional[int]   =						tokenizer.convert_tokens_to_ids(_a			)
																						_a      :       Tuple   =						tokenizer.encode(_a			,				add_special_tokens=_a			)
																						self.assertListEqual(_a			,				_a			)

																						_a      :       Optional[int]   =						tokenizer.convert_ids_to_tokens(_a			)
																						self.assertNotEqual(len(_a			)			,				0			)
																						_a      :       int   =						tokenizer.decode(_a			)
																						self.assertIsInstance(_a			,				_a			)

																						self.assertEqual(text_a.replace(''' '''			,				''''''			)			,				_a			)


							@unittest.skip('''MGP-STR tokenizer only handles one sequence.'''			)
							def 							__lowercase							(     self			)       ->       List[str]:
												pass




							@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer'''			)
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												pass



