"""simple docstring"""
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


_UpperCAmelCase     =			get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_UpperCAmelCase     =			get_tests_dir("""fixtures/vocab.json""")
_UpperCAmelCase     =			get_tests_dir("""fixtures""")
class 					a	(  unittest.TestCase       ):
  UpperCamelCase	:				Optional[Any]     						=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=0



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)
     self.assertIsInstance(snake_case_       ,		snake_case_		)



  def 	lowerCamelCase__       (						self :					Any		)     ->					Union[str, Any]:


     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=WavaVecaConfig()
        SCREAMING_SNAKE_CASE_: Optional[int]     			=AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""		)

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

        SCREAMING_SNAKE_CASE_: List[Any]     			=AutoProcessor.from_pretrained(snake_case_		)

     self.assertIsInstance(snake_case_       ,		snake_case_		)



  def 	lowerCamelCase__       (						self :					Dict		)     ->					int:


     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
        # copy relevant files
        copyfile(snake_case_       ,		os.path.join(snake_case_       ,		snake_case_		)		)
        copyfile(snake_case_       ,		os.path.join(snake_case_       ,		"""vocab.json"""		)		)

        SCREAMING_SNAKE_CASE_: Dict     			=AutoProcessor.from_pretrained(snake_case_		)

     self.assertIsInstance(snake_case_       ,		snake_case_		)



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					List[str]:


     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
        SCREAMING_SNAKE_CASE_: List[Any]     			=WavaVecaFeatureExtractor()
        SCREAMING_SNAKE_CASE_: Optional[int]     			=AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

        SCREAMING_SNAKE_CASE_: Optional[Any]     			=WavaVecaProcessor(snake_case_       ,		snake_case_		)

        # save in new folder
        processor.save_pretrained(snake_case_		)

        # drop `processor_class` in tokenizer
        with open(os.path.join(snake_case_       ,		snake_case_		)       ,		"""r"""		) as f:
           SCREAMING_SNAKE_CASE_: Any     			=json.load(snake_case_		)
           config_dict.pop("""processor_class"""		)

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

        SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoProcessor.from_pretrained(snake_case_		)

     self.assertIsInstance(snake_case_       ,		snake_case_		)



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					int:


     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=WavaVecaFeatureExtractor()
        SCREAMING_SNAKE_CASE_: Tuple     			=AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""		)

        SCREAMING_SNAKE_CASE_: str     			=WavaVecaProcessor(snake_case_       ,		snake_case_		)

        # save in new folder
        processor.save_pretrained(snake_case_		)

        # drop `processor_class` in feature extractor
        with open(os.path.join(snake_case_       ,		snake_case_		)       ,		"""r"""		) as f:
           SCREAMING_SNAKE_CASE_: str     			=json.load(snake_case_		)
           config_dict.pop("""processor_class"""		)

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

        SCREAMING_SNAKE_CASE_: Tuple     			=AutoProcessor.from_pretrained(snake_case_		)

     self.assertIsInstance(snake_case_       ,		snake_case_		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[Any]:


     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmpdirname:
        SCREAMING_SNAKE_CASE_: int     			=WavaVecaConfig(processor_class="""Wav2Vec2Processor"""		)
        model_config.save_pretrained(snake_case_		)
        # copy relevant files
        copyfile(snake_case_       ,		os.path.join(snake_case_       ,		"""vocab.json"""		)		)
        # create emtpy sample processor
        with open(os.path.join(snake_case_       ,		snake_case_		)       ,		"""w"""		) as f:
           f.write("""{}"""		)

        SCREAMING_SNAKE_CASE_: Tuple     			=AutoProcessor.from_pretrained(snake_case_		)

     self.assertIsInstance(snake_case_       ,		snake_case_		)



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Optional[Any]:


     '''simple docstring'''




     with self.assertRaises(snake_case_		):
        SCREAMING_SNAKE_CASE_: List[str]     			=AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""		)
     # If remote code is disabled, we can't load this config.
     with self.assertRaises(snake_case_		):
        SCREAMING_SNAKE_CASE_: Dict     			=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""       ,		trust_remote_code=snake_case_		)

     SCREAMING_SNAKE_CASE_: int     			=AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""       ,		trust_remote_code=snake_case_		)
     self.assertTrue(processor.special_attribute_present		)
     self.assertEqual(processor.__class__.__name__       ,		"""NewProcessor"""		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=processor.feature_extractor
     self.assertTrue(feature_extractor.special_attribute_present		)
     self.assertEqual(feature_extractor.__class__.__name__       ,		"""NewFeatureExtractor"""		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=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
        SCREAMING_SNAKE_CASE_: Dict     			=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""       ,		trust_remote_code=snake_case_       ,		use_fast=snake_case_		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=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 	lowerCamelCase__       (						self :					Any		)     ->					Optional[int]:


     '''simple docstring'''




     try:
        AutoConfig.register("""custom"""       ,		snake_case_		)
        AutoFeatureExtractor.register(snake_case_       ,		snake_case_		)
        AutoTokenizer.register(snake_case_       ,		slow_tokenizer_class=snake_case_		)
        AutoProcessor.register(snake_case_       ,		snake_case_		)
        # Trying to register something existing in the Transformers library will raise an error
        with self.assertRaises(snake_case_		):
           AutoProcessor.register(snake_case_       ,		snake_case_		)

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

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

        SCREAMING_SNAKE_CASE_: List[Any]     			=CustomProcessor(snake_case_       ,		snake_case_		)

        with tempfile.TemporaryDirectory() as tmp_dir:
           processor.save_pretrained(snake_case_		)
           SCREAMING_SNAKE_CASE_: str     			=AutoProcessor.from_pretrained(snake_case_		)
           self.assertIsInstance(snake_case_       ,		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 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     class 					a	(  _snake_case       ):
       UpperCamelCase	:				Dict     						=					False







     class 					a	(  _snake_case       ):
       UpperCamelCase	:				Tuple     						=					False







     class 					a	(  _snake_case       ):
       UpperCamelCase	:				Dict     						=					"""AutoFeatureExtractor"""
       UpperCamelCase	:				Any     						=					"""AutoTokenizer"""
       UpperCamelCase	:				Union[str, Any]     						=					False

     try:
        AutoConfig.register("""custom"""       ,		snake_case_		)
        AutoFeatureExtractor.register(snake_case_       ,		snake_case_		)
        AutoTokenizer.register(snake_case_       ,		slow_tokenizer_class=snake_case_		)
        AutoProcessor.register(snake_case_       ,		snake_case_		)
        # If remote code is not set, the default is to use local classes.
        SCREAMING_SNAKE_CASE_: str     			=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.
        SCREAMING_SNAKE_CASE_: str     			=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""       ,		trust_remote_code=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.
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=AutoProcessor.from_pretrained(
            """hf-internal-testing/test_dynamic_processor"""       ,		trust_remote_code=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 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""		)
     self.assertEqual(processor.__class__.__name__       ,		"""BertTokenizerFast"""		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""		)
     self.assertEqual(processor.__class__.__name__       ,		"""ConvNextImageProcessor"""		)







@is_staging_test
class 					a	(  unittest.TestCase       ):
  UpperCamelCase	:				int     						=					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]



  @classmethod
  def 	lowerCamelCase__       (						cls :					Dict		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=TOKEN
     HfFolder.save_token(snake_case_		)



  @classmethod
  def 	lowerCamelCase__       (						cls :					str		)     ->					List[Any]:


     '''simple docstring'''




     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 	lowerCamelCase__       (						self :					Any		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=WavaVecaProcessor.from_pretrained(snake_case_		)
     with tempfile.TemporaryDirectory() as tmp_dir:
        processor.save_pretrained(
            os.path.join(snake_case_       ,		"""test-processor"""		)       ,		push_to_hub=snake_case_       ,		use_auth_token=self._token		)

        SCREAMING_SNAKE_CASE_: Optional[Any]     			=WavaVecaProcessor.from_pretrained(f'''{USER}/test-processor'''		)
        for k, v in processor.feature_extractor.__dict__.items():
           self.assertEqual(snake_case_       ,		getattr(new_processor.feature_extractor       ,		snake_case_		)		)
        self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,		processor.tokenizer.get_vocab()		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=WavaVecaProcessor.from_pretrained(snake_case_		)

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

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





  def 	lowerCamelCase__       (						self :					Any		)     ->					Dict:


     '''simple docstring'''




     CustomFeatureExtractor.register_for_auto_class()
     CustomTokenizer.register_for_auto_class()
     CustomProcessor.register_for_auto_class()

     SCREAMING_SNAKE_CASE_: Tuple     			=CustomFeatureExtractor.from_pretrained(snake_case_		)

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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=CustomProcessor(snake_case_       ,		snake_case_		)

     with tempfile.TemporaryDirectory() as tmp_dir:
        create_repo(f'''{USER}/test-dynamic-processor'''       ,		token=self._token		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=Repository(snake_case_       ,		clone_from=f'''{USER}/test-dynamic-processor'''       ,		token=self._token		)
        processor.save_pretrained(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(snake_case_       ,		"""tokenizer_config.json"""		)		) as f:
           SCREAMING_SNAKE_CASE_: str     			=json.load(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(snake_case_       ,		"""custom_feature_extraction.py"""		)		)		)
        self.assertTrue(os.path.isfile(os.path.join(snake_case_       ,		"""custom_tokenization.py"""		)		)		)
        self.assertTrue(os.path.isfile(os.path.join(snake_case_       ,		"""custom_processing.py"""		)		)		)

        repo.push_to_hub()

     SCREAMING_SNAKE_CASE_: Tuple     			=AutoProcessor.from_pretrained(f'''{USER}/test-dynamic-processor'''       ,		trust_remote_code=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"""		)








"""simple docstring"""
def      __magic_name__				(							lowercase ):
   if upper_limit < 0:
      raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )

   SCREAMING_SNAKE_CASE_: Tuple     			=[0] * (upper_limit + 1)

   # Base case: C(0) = C(1) = 1
   SCREAMING_SNAKE_CASE_: Any     			=1
   if upper_limit > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			=1

   # Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
   for i in range(2							,			upper_limit + 1 ):
      for j in range(lowercase ):
         catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]

   return catalan_list


if __name__ == "__main__":
   print("""\n********* Catalan Numbers Using Dynamic Programming ************\n""")
   print("""\n*** Enter -1 at any time to quit ***""")
   print("""\nEnter the upper limit (≥ 0) for the Catalan number sequence: """, end="""""")
   try:
      while True:
         _UpperCAmelCase     =			int(input().strip())
         if N < 0:
            print("""\n********* Goodbye!! ************""")
            break
         else:
            print(f"""The Catalan numbers from 0 through {N} are:""")
            print(catalan_numbers(N))
            print("""Try another upper limit for the sequence: """, end="""""")
   except (NameError, ValueError):
      print("""\n********* Invalid input, goodbye! ************\n""")

   import doctest

   doctest.testmod()



