





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

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


A			      =	logging.get_logger(__name__)

A			      =	{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 					__lowercase      (      __SCREAMING_SNAKE_CASE				):







					'''simple docstring'''




					__lowerCAmelCase	=	"""xlm-roberta-xl"""







					def __init__(    self						,      _UpperCAmelCase=250880						,      _UpperCAmelCase=2560						,      _UpperCAmelCase=36						,      _UpperCAmelCase=32						,      _UpperCAmelCase=10240						,      _UpperCAmelCase="gelu"						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=0.1						,      _UpperCAmelCase=514						,      _UpperCAmelCase=1						,      _UpperCAmelCase=0.0_2						,      _UpperCAmelCase=1e-0_5						,      _UpperCAmelCase=1						,      _UpperCAmelCase=0						,      _UpperCAmelCase=2						,      _UpperCAmelCase="absolute"						,      _UpperCAmelCase=True						,      _UpperCAmelCase=None						,      **_UpperCAmelCase						,      ):
									super().__init__(pad_token_id=A_						,      bos_token_id=A_						,      eos_token_id=A_						,      **A_  )
									__a			:    Union[str, Any]      							=	vocab_size
									__a			:    Optional[Any]      							=	hidden_size
									__a			:    str      							=	num_hidden_layers
									__a			:    List[Any]      							=	num_attention_heads
									__a			:    int      							=	hidden_act
									__a			:    Dict      							=	intermediate_size
									__a			:    str      							=	hidden_dropout_prob
									__a			:    int      							=	attention_probs_dropout_prob
									__a			:    Dict      							=	max_position_embeddings
									__a			:    str      							=	type_vocab_size
									__a			:    str      							=	initializer_range
									__a			:    int      							=	layer_norm_eps
									__a			:    str      							=	position_embedding_type
									__a			:    Optional[int]      							=	use_cache
									__a			:    int      							=	classifier_dropout






class 					__lowercase      (      __SCREAMING_SNAKE_CASE				):







					'''simple docstring'''




					@property
					def 			_lowerCamelCase		(    self  ):

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






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Dict				=	BioGptTokenizer
    lowerCamelCase__    :		List[Any]				=	False



    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        SCREAMING_SNAKE_CASE__											=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE__											=			dict(zip(A_   ,				range(len(A_     )     )     )     )
        SCREAMING_SNAKE_CASE__											=			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']     )
        SCREAMING_SNAKE_CASE__											=			os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']     )
        with open(self.vocab_file   ,				'''w'''     ) as fp:
            fp.write(json.dumps(A_     )     )
        with open(self.merges_file   ,				'''w'''     ) as fp:
            fp.write('''\n'''.join(A_     )     )



    def  lowercase_					(       self   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        SCREAMING_SNAKE_CASE__											=			'''lower newer'''
        return input_text, output_text



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer(self.vocab_file   ,				self.merges_file     )

        SCREAMING_SNAKE_CASE__											=			'''lower'''
        SCREAMING_SNAKE_CASE__											=			['''low''', '''er</w>''']
        SCREAMING_SNAKE_CASE__											=			tokenizer.tokenize(A_     )
        self.assertListEqual(A_   ,				A_     )

        SCREAMING_SNAKE_CASE__											=			tokens + ['''<unk>''']
        SCREAMING_SNAKE_CASE__											=			[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(A_     )   ,				A_     )




    @slow
    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BioGptTokenizer.from_pretrained('''microsoft/biogpt'''     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''sequence builders'''   ,				add_special_tokens=A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.encode('''multi-sequence build'''   ,				add_special_tokens=A_     )

        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_     )
        SCREAMING_SNAKE_CASE__											=			tokenizer.build_inputs_with_special_tokens(A_   ,				A_     )

        self.assertTrue(encoded_sentence == [2] + text     )
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a     )
