






"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase	:	Tuple				=      logging.get_logger(__name__)

_lowerCAmelCase	:	List[Any]				=      {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class 			lowerCAmelCase__			(    lowerCamelCase_   ):
   SCREAMING_SNAKE_CASE_    ="roberta"
   def __init__(		self      :	Optional[int]							,			snake_case__      :	Dict=5_0_2_6_5							,			snake_case__      :	str=7_6_8							,			snake_case__      :	Dict=1_2							,			snake_case__      :	Optional[int]=1_2							,			snake_case__      :	Optional[Any]=3_0_7_2							,			snake_case__      :	Dict="gelu"							,			snake_case__      :	Optional[int]=0.1							,			snake_case__      :	List[str]=0.1							,			snake_case__      :	Optional[Any]=5_1_2							,			snake_case__      :	Tuple=2							,			snake_case__      :	Optional[Any]=0.02							,			snake_case__      :	Tuple=1e-12							,			snake_case__      :	Optional[int]=1							,			snake_case__      :	List[str]=0							,			snake_case__      :	List[Any]=2							,			snake_case__      :	str="absolute"							,			snake_case__      :	List[str]=True							,			snake_case__      :	Union[str, Any]=None							,			**snake_case__      :	Tuple							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Tuple							=							num_hidden_layers
        UpperCAmelCase__						:    Optional[Any]							=							num_attention_heads
        UpperCAmelCase__						:    Any							=							hidden_act
        UpperCAmelCase__						:    Union[str, Any]							=							intermediate_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_dropout_prob
        UpperCAmelCase__						:    Tuple							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Tuple							=							max_position_embeddings
        UpperCAmelCase__						:    Optional[Any]							=							type_vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							initializer_range
        UpperCAmelCase__						:    Tuple							=							layer_norm_eps
        UpperCAmelCase__						:    str							=							position_embedding_type
        UpperCAmelCase__						:    Union[str, Any]							=							use_cache
        UpperCAmelCase__						:    Tuple							=							classifier_dropout




class 			lowerCAmelCase__			(    lowerCamelCase_   ):
   @property
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        if self.task == "multiple-choice":
             UpperCAmelCase__						:    Union[str, Any]							=							{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             UpperCAmelCase__						:    str							=							{0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
            ]							)








import os
import sys
import unittest


UpperCamelCase__ :   Optional[Any]						=							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
UpperCamelCase__ :   Tuple						=							os.path.join(git_repo_path, '''src''', '''transformers''')

UpperCamelCase__ :   List[Any]						=							'''
{0} = None
'''

UpperCamelCase__ :   str						=							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCamelCase__ :   List[Any]						=							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
							self.assertIsNone(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      find_backend('    if not is_tokenizers_available():' )
							self.assertEqual(snake_case__      ,'tokenizers' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      find_backend('    if not is_tensorflow_text_available():' )
							self.assertEqual(snake_case__      ,'tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers' )

							SCREAMING_SNAKE_CASE_   : str					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers_and_vision' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'      ,snake_case__ )
							self.assertIn('tensorflow_text'      ,snake_case__ )
							self.assertIn('sentencepiece_and_tokenizers'      ,snake_case__ )

							# Likewise, we can't assert on the exact content of a key
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertModel'      ,objects['tf'] )
							self.assertIn('FlaxBertModel'      ,objects['flax'] )
							self.assertIn('BertModel'      ,objects['torch'] )
							self.assertIn('TFBertTokenizer'      ,objects['tensorflow_text'] )
							self.assertIn('convert_slow_tokenizer'      ,objects['sentencepiece_and_tokenizers'] )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      create_dummy_object('CONSTANT'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,'\nCONSTANT = None\n' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('function'      ,'\'torch\'' )
							self.assertEqual(
							    snake_case__      ,'\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('FakeClass'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
							self.assertEqual(dummy_files['torch']      ,snake_case__ )



