
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__		:     Union[str, Any]     			=      get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCAmelCase__		:     Any     			=      get_tests_dir('fixtures/vocab.json')
UpperCAmelCase__		:     Dict     			=      get_tests_dir('fixtures')






class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  Dict					=       0


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''     )
      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							List[Any]     ):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  Optional[int]					=       WavaVecaConfig()
         _A:  int					=       AutoProcessor.from_pretrained('''facebook/wav2vec2-base-960h'''     )

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

         _A:  List[str]					=       AutoProcessor.from_pretrained(lowerCAmelCase_     )

      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int     ):

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

         _A:  List[str]					=       AutoProcessor.from_pretrained(lowerCAmelCase_     )

      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  Any					=       WavaVecaFeatureExtractor()
         _A:  Optional[Any]					=       AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''     )

         _A:  int					=       WavaVecaProcessor(lowerCAmelCase_			,			lowerCAmelCase_     )

         # save in new folder
         processor.save_pretrained(lowerCAmelCase_     )

         # drop `processor_class` in tokenizer
         with open(os.path.join(lowerCAmelCase_			,			lowerCAmelCase_     )			,			'''r'''     ) as f:
            _A:  Union[str, Any]					=       json.load(lowerCAmelCase_     )
            config_dict.pop('''processor_class'''     )

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

         _A:  Union[str, Any]					=       AutoProcessor.from_pretrained(lowerCAmelCase_     )

      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      with tempfile.TemporaryDirectory() as tmpdirname:
         _A:  Tuple					=       WavaVecaFeatureExtractor()
         _A:  Union[str, Any]					=       AutoTokenizer.from_pretrained('''facebook/wav2vec2-base-960h'''     )

         _A:  List[str]					=       WavaVecaProcessor(lowerCAmelCase_			,			lowerCAmelCase_     )

         # save in new folder
         processor.save_pretrained(lowerCAmelCase_     )

         # drop `processor_class` in feature extractor
         with open(os.path.join(lowerCAmelCase_			,			lowerCAmelCase_     )			,			'''r'''     ) as f:
            _A:  Union[str, Any]					=       json.load(lowerCAmelCase_     )
            config_dict.pop('''processor_class'''     )

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

         _A:  int					=       AutoProcessor.from_pretrained(lowerCAmelCase_     )

      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							Tuple     ):

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

         _A:  Tuple					=       AutoProcessor.from_pretrained(lowerCAmelCase_     )

      self.assertIsInstance(lowerCAmelCase_			,			lowerCAmelCase_     )


   def  __magic_name__				(					self     :							str     ):

      """simple docstring"""
      # If remote code is not set, we will time out when asking whether to load the model.
      with self.assertRaises(lowerCAmelCase_     ):
         _A:  Optional[Any]					=       AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''     )
      # If remote code is disabled, we can't load this config.
      with self.assertRaises(lowerCAmelCase_     ):
         _A:  Union[str, Any]					=       AutoProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowerCAmelCase_     )

      _A:  Union[str, Any]					=       AutoProcessor.from_pretrained('''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowerCAmelCase_     )
      self.assertTrue(processor.special_attribute_present     )
      self.assertEqual(processor.__class__.__name__			,			'''NewProcessor'''     )

      _A:  Tuple					=       processor.feature_extractor
      self.assertTrue(feature_extractor.special_attribute_present     )
      self.assertEqual(feature_extractor.__class__.__name__			,			'''NewFeatureExtractor'''     )

      _A:  List[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
         _A:  str					=       AutoProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowerCAmelCase_			,			use_fast=lowerCAmelCase_     )
         _A:  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  __magic_name__				(					self     :							str     ):

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

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

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

         _A:  Optional[int]					=       CustomProcessor(lowerCAmelCase_			,			lowerCAmelCase_     )

         with tempfile.TemporaryDirectory() as tmp_dir:
            processor.save_pretrained(lowerCAmelCase_     )
            _A:  int					=       AutoProcessor.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]
         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  __magic_name__				(					self     :							Any     ):

      """simple docstring"""






      class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





         '''simple docstring'''
         __UpperCamelCase		:    int 				=    False






      class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





         '''simple docstring'''
         __UpperCamelCase		:    Union[str, Any] 				=    False






      class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





         '''simple docstring'''
         __UpperCamelCase		:    str 				=    '''AutoFeatureExtractor'''
         __UpperCamelCase		:    Optional[int] 				=    '''AutoTokenizer'''
         __UpperCamelCase		:    Optional[int] 				=    False

      try:
         AutoConfig.register('''custom'''			,			lowerCAmelCase_     )
         AutoFeatureExtractor.register(lowerCAmelCase_			,			lowerCAmelCase_     )
         AutoTokenizer.register(lowerCAmelCase_			,			slow_tokenizer_class=lowerCAmelCase_     )
         AutoProcessor.register(lowerCAmelCase_			,			lowerCAmelCase_     )
         # If remote code is not set, the default is to use local classes.
         _A:  Tuple					=       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.
         _A:  int					=       AutoProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowerCAmelCase_     )
         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.
         _A:  Any					=       AutoProcessor.from_pretrained(
             '''hf-internal-testing/test_dynamic_processor'''			,			trust_remote_code=lowerCAmelCase_     )
         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  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Tuple					=       AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-bert'''     )
      self.assertEqual(processor.__class__.__name__			,			'''BertTokenizerFast'''     )


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      _A:  int					=       AutoProcessor.from_pretrained('''hf-internal-testing/tiny-random-convnext'''     )
      self.assertEqual(processor.__class__.__name__			,			'''ConvNextImageProcessor'''     )






@is_staging_test
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    str 				=    ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']


   @classmethod
   def  __magic_name__				(					cls     :							Any     ):

      """simple docstring"""
      _A:  Optional[Any]					=       TOKEN
      HfFolder.save_token(lowerCAmelCase_     )


   @classmethod
   def  __magic_name__				(					cls     :							Optional[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  __magic_name__				(					self     :							str     ):

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

         _A:  Optional[int]					=       WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor"""     )
         for k, v in processor.feature_extractor.__dict__.items():
            self.assertEqual(lowerCAmelCase_			,			getattr(new_processor.feature_extractor			,			lowerCAmelCase_     )     )
         self.assertDictEqual(new_processor.tokenizer.get_vocab()			,			processor.tokenizer.get_vocab()     )


   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      _A:  Optional[Any]					=       WavaVecaProcessor.from_pretrained(lowerCAmelCase_     )

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

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


   def  __magic_name__				(					self     :							Dict     ):

      """simple docstring"""
      CustomFeatureExtractor.register_for_auto_class()
      CustomTokenizer.register_for_auto_class()
      CustomProcessor.register_for_auto_class()

      _A:  str					=       CustomFeatureExtractor.from_pretrained(lowerCAmelCase_     )

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

      _A:  Dict					=       CustomProcessor(lowerCAmelCase_			,			lowerCAmelCase_     )

      with tempfile.TemporaryDirectory() as tmp_dir:
         create_repo(F"""{USER}/test-dynamic-processor"""			,			token=self._token     )
         _A:  Dict					=       Repository(lowerCAmelCase_			,			clone_from=F"""{USER}/test-dynamic-processor"""			,			token=self._token     )
         processor.save_pretrained(lowerCAmelCase_     )

         # 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(lowerCAmelCase_			,			'''tokenizer_config.json'''     )     ) as f:
            _A:  Optional[int]					=       json.load(lowerCAmelCase_     )
         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(lowerCAmelCase_			,			'''custom_feature_extraction.py'''     )     )     )
         self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase_			,			'''custom_tokenization.py'''     )     )     )
         self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase_			,			'''custom_processing.py'''     )     )     )

         repo.push_to_hub()

      _A:  Optional[Any]					=       AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor"""			,			trust_remote_code=lowerCAmelCase_     )
      # 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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase__		:     List[str]     			=      logging.get_logger(__name__)

UpperCAmelCase__		:     Union[str, Any]     			=      {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    int 				=    '''xlm'''
   __UpperCamelCase		:    Optional[Any] 				=    {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(					self     :							List[Any]			,			lowerCAmelCase_     :							Dict=3_0_1_4_5			,			lowerCAmelCase_     :							Union[str, Any]=2_0_4_8			,			lowerCAmelCase_     :							List[str]=1_2			,			lowerCAmelCase_     :							Optional[Any]=1_6			,			lowerCAmelCase_     :							Union[str, Any]=0.1			,			lowerCAmelCase_     :							List[str]=0.1			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Tuple=False			,			lowerCAmelCase_     :							List[str]=False			,			lowerCAmelCase_     :							Optional[Any]=1			,			lowerCAmelCase_     :							List[str]=True			,			lowerCAmelCase_     :							Tuple=5_1_2			,			lowerCAmelCase_     :							Tuple=2_0_4_8**-0.5			,			lowerCAmelCase_     :							List[str]=1e-12			,			lowerCAmelCase_     :							Any=0.02			,			lowerCAmelCase_     :							int=0			,			lowerCAmelCase_     :							int=1			,			lowerCAmelCase_     :							Dict=2			,			lowerCAmelCase_     :							List[Any]=3			,			lowerCAmelCase_     :							int=5			,			lowerCAmelCase_     :							Optional[int]=True			,			lowerCAmelCase_     :							Union[str, Any]="first"			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							List[Any]=None			,			lowerCAmelCase_     :							Any=True			,			lowerCAmelCase_     :							int=0.1			,			lowerCAmelCase_     :							Tuple=5			,			lowerCAmelCase_     :							Tuple=5			,			lowerCAmelCase_     :							Optional[int]=0			,			lowerCAmelCase_     :							Optional[Any]=0			,			lowerCAmelCase_     :							str=2			,			lowerCAmelCase_     :							Dict=0			,			**lowerCAmelCase_     :							List[Any]			,			):

      """simple docstring"""
      _A:  Dict					=       vocab_size
      _A:  Dict					=       emb_dim
      _A:  Optional[Any]					=       n_layers
      _A:  str					=       n_heads
      _A:  Optional[int]					=       dropout
      _A:  Union[str, Any]					=       attention_dropout
      _A:  Any					=       gelu_activation
      _A:  Optional[Any]					=       sinusoidal_embeddings
      _A:  Any					=       causal
      _A:  Optional[int]					=       asm
      _A:  List[str]					=       n_langs
      _A:  int					=       use_lang_emb
      _A:  Any					=       layer_norm_eps
      _A:  Tuple					=       bos_index
      _A:  Any					=       eos_index
      _A:  List[str]					=       pad_index
      _A:  str					=       unk_index
      _A:  str					=       mask_index
      _A:  List[Any]					=       is_encoder
      _A:  List[Any]					=       max_position_embeddings
      _A:  str					=       embed_init_std
      _A:  int					=       init_std
      _A:  List[str]					=       summary_type
      _A:  List[str]					=       summary_use_proj
      _A:  Tuple					=       summary_activation
      _A:  Dict					=       summary_proj_to_labels
      _A:  Tuple					=       summary_first_dropout
      _A:  str					=       start_n_top
      _A:  str					=       end_n_top
      _A:  Optional[Any]					=       mask_token_id
      _A:  Any					=       lang_id

      if "n_words" in kwargs:
         _A:  Union[str, Any]					=       kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_			,			bos_token_id=lowerCAmelCase_			,			**lowerCAmelCase_     )






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:  int					=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:  Tuple					=       {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
              ('''token_type_ids''', dynamic_axis),
          ]     )



