
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


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
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


SCREAMING_SNAKE_CASE__   :			str												=    get_tests_dir("fixtures/dummy_feature_extractor_config.json")
SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    get_tests_dir("fixtures/vocab.json")
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    get_tests_dir("fixtures")

class    lowerCAmelCase__     (	unittest.TestCase			):
		a__       :						str        =		["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]






		def  __A       (		self		:							List[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	0






		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''  )
						self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							Union[str, Any]:
						with tempfile.TemporaryDirectory() as tmpdirname:
										__lowerCamelCase    					=	WavaVecaConfig()
										__lowerCamelCase    					=	AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''  )

										# save in new folder
										model_config.save_pretrained(SCREAMING_SNAKE_CASE__  )
										processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

										__lowerCamelCase    					=	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[Any]  )     ->							Any:
						with tempfile.TemporaryDirectory() as tmpdirname:
										# copy relevant files
										copyfile(SCREAMING_SNAKE_CASE__							,			os.path.join(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )  )
										copyfile(SCREAMING_SNAKE_CASE__							,			os.path.join(SCREAMING_SNAKE_CASE__							,			'''vocab.json'''  )  )

										__lowerCamelCase    					=	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							str  )     ->							Optional[Any]:
						with tempfile.TemporaryDirectory() as tmpdirname:
										__lowerCamelCase    					=	WavaVecaFeatureExtractor()
										__lowerCamelCase    					=	AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''  )

										__lowerCamelCase    					=	WavaVecaProcessor(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

										# save in new folder
										processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

										# drop `processor_class` in tokenizer
										with open(os.path.join(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )							,			'''r'''  ) as f:
														__lowerCamelCase    					=	json.load(SCREAMING_SNAKE_CASE__  )
														config_dict.pop('''processor_class'''  )

										with open(os.path.join(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )							,			'''w'''  ) as f:
														f.write(json.dumps(SCREAMING_SNAKE_CASE__  )  )

										__lowerCamelCase    					=	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							int  )     ->							str:
						with tempfile.TemporaryDirectory() as tmpdirname:
										__lowerCamelCase    					=	WavaVecaFeatureExtractor()
										__lowerCamelCase    					=	AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''  )

										__lowerCamelCase    					=	WavaVecaProcessor(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

										# save in new folder
										processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

										# drop `processor_class` in feature extractor
										with open(os.path.join(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )							,			'''r'''  ) as f:
														__lowerCamelCase    					=	json.load(SCREAMING_SNAKE_CASE__  )
														config_dict.pop('''processor_class'''  )

										with open(os.path.join(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )							,			'''w'''  ) as f:
														f.write(json.dumps(SCREAMING_SNAKE_CASE__  )  )

										__lowerCamelCase    					=	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[Any]  )     ->							Dict:
						with tempfile.TemporaryDirectory() as tmpdirname:
										__lowerCamelCase    					=	WavaVecaConfig(processor_class='''Wav2Vec2Processor'''  )
										model_config.save_pretrained(SCREAMING_SNAKE_CASE__  )
										# copy relevant files
										copyfile(SCREAMING_SNAKE_CASE__							,			os.path.join(SCREAMING_SNAKE_CASE__							,			'''vocab.json'''  )  )
										# create emtpy sample processor
										with open(os.path.join(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )							,			'''w'''  ) as f:
														f.write('''{}'''  )

										__lowerCamelCase    					=	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							str  )     ->							Any:
						# If remote code is not set, we will time out when asking whether to load the model.
						with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''  )
						# If remote code is disabled, we can't load this config.
						with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	AutoProcessor.from_pretrained(
										    '''hf-internal-testing/test_dynamic_processor'''							,			trust_remote_code=SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''							,			trust_remote_code=SCREAMING_SNAKE_CASE__  )
						self.assertTrue(processor.special_attribute_present  )
						self.assertEqual(processor.__class__.__name__							,			'''NewProcessor'''  )

						__lowerCamelCase    					=	processor.feature_extractor
						self.assertTrue(feature_extractor.special_attribute_present  )
						self.assertEqual(feature_extractor.__class__.__name__							,			'''NewFeatureExtractor'''  )

						__lowerCamelCase    					=	processor.tokenizer
						self.assertTrue(tokenizer.special_attribute_present  )
						if is_tokenizers_available():
										self.assertEqual(tokenizer.__class__.__name__							,			'''NewTokenizerFast'''  )

										# Test we can also load the slow version
										__lowerCamelCase    					=	AutoProcessor.from_pretrained(
										    '''hf-internal-testing/test_dynamic_processor'''							,			trust_remote_code=SCREAMING_SNAKE_CASE__							,			use_fast=SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase    					=	new_processor.tokenizer
										self.assertTrue(new_tokenizer.special_attribute_present  )
										self.assertEqual(new_tokenizer.__class__.__name__							,			'''NewTokenizer'''  )
						else:
										self.assertEqual(tokenizer.__class__.__name__							,			'''NewTokenizer'''  )






		def  __A       (		self		:							Any  )     ->							Tuple:
						try:
										AutoConfig.register('''custom'''							,			SCREAMING_SNAKE_CASE__  )
										AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										AutoTokenizer.register(SCREAMING_SNAKE_CASE__							,			slow_tokenizer_class=SCREAMING_SNAKE_CASE__  )
										AutoProcessor.register(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										# Trying to register something existing in the Transformers library will raise an error
										with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
														AutoProcessor.register(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

										# Now that the config is registered, it can be used as any other config with the auto-API
										__lowerCamelCase    					=	CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )

										with tempfile.TemporaryDirectory() as tmp_dir:
														__lowerCamelCase    					=	os.path.join(SCREAMING_SNAKE_CASE__							,			'''vocab.txt'''  )
														with open(SCREAMING_SNAKE_CASE__							,			'''w'''							,			encoding='''utf-8'''  ) as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]  )  )
														__lowerCamelCase    					=	CustomTokenizer(SCREAMING_SNAKE_CASE__  )

										__lowerCamelCase    					=	CustomProcessor(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

										with tempfile.TemporaryDirectory() as tmp_dir:
														processor.save_pretrained(SCREAMING_SNAKE_CASE__  )
														__lowerCamelCase    					=	AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
														self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						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]
										if CustomConfig in TOKENIZER_MAPPING._extra_content:
														del TOKENIZER_MAPPING._extra_content[CustomConfig]
										if CustomConfig in PROCESSOR_MAPPING._extra_content:
														del PROCESSOR_MAPPING._extra_content[CustomConfig]






		def  __A       (		self		:							Dict  )     ->							Dict:

						class    lowerCAmelCase__     (	__lowercase			):
								a__       :						List[Any]        =		False





						class    lowerCAmelCase__     (	__lowercase			):
								a__       :						List[str]        =		False





						class    lowerCAmelCase__     (	__lowercase			):
								a__       :						Union[str, Any]        =		"""AutoFeatureExtractor"""
								a__       :						int        =		"""AutoTokenizer"""
								a__       :						int        =		False

						try:
										AutoConfig.register('''custom'''							,			SCREAMING_SNAKE_CASE__  )
										AutoFeatureExtractor.register(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										AutoTokenizer.register(SCREAMING_SNAKE_CASE__							,			slow_tokenizer_class=SCREAMING_SNAKE_CASE__  )
										AutoProcessor.register(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										# If remote code is not set, the default is to use local classes.
										__lowerCamelCase    					=	AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''  )
										self.assertEqual(processor.__class__.__name__							,			'''NewProcessor'''  )
										self.assertFalse(processor.special_attribute_present  )
										self.assertFalse(processor.feature_extractor.special_attribute_present  )
										self.assertFalse(processor.tokenizer.special_attribute_present  )

										# If remote code is disabled, we load the local ones.
										__lowerCamelCase    					=	AutoProcessor.from_pretrained(
										    '''hf-internal-testing/test_dynamic_processor'''							,			trust_remote_code=SCREAMING_SNAKE_CASE__  )
										self.assertEqual(processor.__class__.__name__							,			'''NewProcessor'''  )
										self.assertFalse(processor.special_attribute_present  )
										self.assertFalse(processor.feature_extractor.special_attribute_present  )
										self.assertFalse(processor.tokenizer.special_attribute_present  )

										# If remote is enabled, we load from the Hub.
										__lowerCamelCase    					=	AutoProcessor.from_pretrained(
										    '''hf-internal-testing/test_dynamic_processor'''							,			trust_remote_code=SCREAMING_SNAKE_CASE__  )
										self.assertEqual(processor.__class__.__name__							,			'''NewProcessor'''  )
										self.assertTrue(processor.special_attribute_present  )
										self.assertTrue(processor.feature_extractor.special_attribute_present  )
										self.assertTrue(processor.tokenizer.special_attribute_present  )

						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]
										if CustomConfig in TOKENIZER_MAPPING._extra_content:
														del TOKENIZER_MAPPING._extra_content[CustomConfig]
										if CustomConfig in PROCESSOR_MAPPING._extra_content:
														del PROCESSOR_MAPPING._extra_content[CustomConfig]






		def  __A       (		self		:							Optional[int]  )     ->							Any:
						__lowerCamelCase    					=	AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''  )
						self.assertEqual(processor.__class__.__name__							,			'''BertTokenizerFast'''  )







		def  __A       (		self		:							int  )     ->							Dict:
						__lowerCamelCase    					=	AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''  )
						self.assertEqual(processor.__class__.__name__							,			'''ConvNextImageProcessor'''  )





@is_staging_test
class    lowerCAmelCase__     (	unittest.TestCase			):
		a__       :						Dict        =		["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]






		@classmethod
		def  __A       (		cls		:							str  )     ->							Dict:
						__lowerCamelCase    					=	TOKEN
						HfFolder.save_token(SCREAMING_SNAKE_CASE__  )






		@classmethod
		def  __A       (		cls		:							List[Any]  )     ->							Union[str, Any]:
						try:
										delete_repo(token=cls._token							,			repo_id='''test-processor'''  )
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token							,			repo_id='''valid_org/test-processor-org'''  )
						except HTTPError:
										pass

						try:
										delete_repo(token=cls._token							,			repo_id='''test-dynamic-processor'''  )
						except HTTPError:
										pass






		def  __A       (		self		:							List[Any]  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
						with tempfile.TemporaryDirectory() as tmp_dir:
										processor.save_pretrained(
										    os.path.join(SCREAMING_SNAKE_CASE__							,			'''test-processor'''  )							,			push_to_hub=SCREAMING_SNAKE_CASE__							,			use_auth_token=self._token  )

										__lowerCamelCase    					=	WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''  )
										for k, v in processor.feature_extractor.__dict__.items():
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			getattr(new_processor.feature_extractor							,			SCREAMING_SNAKE_CASE__  )  )
										self.assertDictEqual(new_processor.tokenizer.get_vocab()							,			processor.tokenizer.get_vocab()  )






		def  __A       (		self		:							Union[str, Any]  )     ->							Dict:
						__lowerCamelCase    					=	WavaVecaProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						with tempfile.TemporaryDirectory() as tmp_dir:
										processor.save_pretrained(
										    os.path.join(SCREAMING_SNAKE_CASE__							,			'''test-processor-org'''  )							,			push_to_hub=SCREAMING_SNAKE_CASE__							,			use_auth_token=self._token							,			organization='''valid_org'''							,			)

										__lowerCamelCase    					=	WavaVecaProcessor.from_pretrained('''valid_org/test-processor-org'''  )
										for k, v in processor.feature_extractor.__dict__.items():
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			getattr(new_processor.feature_extractor							,			SCREAMING_SNAKE_CASE__  )  )
										self.assertDictEqual(new_processor.tokenizer.get_vocab()							,			processor.tokenizer.get_vocab()  )







		def  __A       (		self		:							int  )     ->							str:
						CustomFeatureExtractor.register_for_auto_class()
						CustomTokenizer.register_for_auto_class()
						CustomProcessor.register_for_auto_class()

						__lowerCamelCase    					=	CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )

						with tempfile.TemporaryDirectory() as tmp_dir:
										__lowerCamelCase    					=	os.path.join(SCREAMING_SNAKE_CASE__							,			'''vocab.txt'''  )
										with open(SCREAMING_SNAKE_CASE__							,			'''w'''							,			encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]  )  )
										__lowerCamelCase    					=	CustomTokenizer(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	CustomProcessor(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						with tempfile.TemporaryDirectory() as tmp_dir:
										create_repo(f'''{USER}/test-dynamic-processor'''							,			token=self._token  )
										__lowerCamelCase    					=	Repository(SCREAMING_SNAKE_CASE__							,			clone_from=f'''{USER}/test-dynamic-processor'''							,			token=self._token  )
										processor.save_pretrained(SCREAMING_SNAKE_CASE__  )

										# This has added the proper auto_map field to the feature extractor config
										self.assertDictEqual(
										    processor.feature_extractor.auto_map							,			{
										        '''AutoFeatureExtractor''': '''custom_feature_extraction.CustomFeatureExtractor''',
										        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
										    }							,			)

										# This has added the proper auto_map field to the tokenizer config
										with open(os.path.join(SCREAMING_SNAKE_CASE__							,			'''tokenizer_config.json'''  )  ) as f:
														__lowerCamelCase    					=	json.load(SCREAMING_SNAKE_CASE__  )
										self.assertDictEqual(
										    tokenizer_config['''auto_map''']							,			{
										        '''AutoTokenizer''': ['''custom_tokenization.CustomTokenizer''', None],
										        '''AutoProcessor''': '''custom_processing.CustomProcessor''',
										    }							,			)

										# The code has been copied from fixtures
										self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__							,			'''custom_feature_extraction.py'''  )  )  )
										self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__							,			'''custom_tokenization.py'''  )  )  )
										self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__							,			'''custom_processing.py'''  )  )  )

										repo.push_to_hub()

						__lowerCamelCase    					=	AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''							,			trust_remote_code=SCREAMING_SNAKE_CASE__  )
						# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
						self.assertEqual(new_processor.__class__.__name__							,			'''CustomProcessor'''  )





from __future__ import annotations

from fractions import Fraction




def  __magic_name__ (   __lowerCAmelCase	:						int		,  __lowerCAmelCase	:						int							)			->   bool:
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   list[str]:
				__lowerCamelCase    					=	[]
				__lowerCamelCase    					=	11
				__lowerCamelCase    					=	int('''1''' + '''0''' * digit_len							)
				for num in range(__lowerCAmelCase		,  __lowerCAmelCase							):
								while den <= 99:
												if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																if is_digit_cancelling(__lowerCAmelCase		,  __lowerCAmelCase							):
																				solutions.append(f'''{num}/{den}'''							)
												den += 1
								num += 1
								__lowerCamelCase    					=	10
				return solutions




def  __magic_name__ (   __lowerCAmelCase	:						int = 2							)			->   int:
				__lowerCamelCase    					=	1.0
				for fraction in fraction_list(__lowerCAmelCase							):
								__lowerCamelCase    					=	Fraction(__lowerCAmelCase							)
								result *= frac.denominator / frac.numerator
				return int(__lowerCAmelCase							)


if __name__ == "__main__":
					print(solution())



