


'''simple docstring'''





import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Tuple					      =			MgpstrTokenizer
			A						:				Optional[Any]					      =			False
			A						:				int					      =			{}
			A						:				Dict					      =			False
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		List[Any]:
						super().setUp()

						# fmt: off
						__A=	['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
						# fmt: on
						__A=	dict(zip(lowerCAmelCase_			,    range(len(lowerCAmelCase_	)	)	)	)

						__A=	os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES['vocab_file']	)
						with open(self.vocab_file			,    'w'			,    encoding='utf-8'	) as fp:
									fp.write(json.dumps(lowerCAmelCase_	) + '\n'	)
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    **lowerCAmelCase_							:			Optional[int]	)      ->		List[Any]:
						return MgpstrTokenizer.from_pretrained(self.tmpdirname			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	'tester'
						__A=	'tester'
						return input_text, output_text
			@unittest.skip('MGP-STR always lower cases letters.'	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						pass
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Dict:
						__A=	self.get_tokenizers(do_lower_case=lowerCAmelCase_	)
						for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
												__A=	'[SPECIAL_TOKEN]'

												tokenizer.add_special_tokens({'cls_token': special_token}	)
												__A=	tokenizer.encode([special_token]			,    add_special_tokens=lowerCAmelCase_	)
												self.assertEqual(len(lowerCAmelCase_	)			,    1	)

												__A=	tokenizer.decode(lowerCAmelCase_			,    skip_special_tokens=lowerCAmelCase_	)
												self.assertTrue(special_token not in decoded	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Union[str, Any]:
						__A=	self.get_tokenizers()
						for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""	):
												__A,	__A=	self.get_input_output_texts(lowerCAmelCase_	)

												__A=	tokenizer.tokenize(lowerCAmelCase_	)
												__A=	tokenizer.convert_tokens_to_ids(lowerCAmelCase_	)
												__A=	tokenizer.encode(lowerCAmelCase_			,    add_special_tokens=lowerCAmelCase_	)
												self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)

												__A=	tokenizer.convert_ids_to_tokens(lowerCAmelCase_	)
												self.assertNotEqual(len(lowerCAmelCase_	)			,    0	)
												__A=	tokenizer.decode(lowerCAmelCase_	)
												self.assertIsInstance(lowerCAmelCase_			,    lowerCAmelCase_	)

												self.assertEqual(text_a.replace(' '			,    ''	)			,    lowerCAmelCase_	)
			@unittest.skip('MGP-STR tokenizer only handles one sequence.'	)
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Tuple:
						pass

			@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'	)
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		List[str]:
						pass







'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__  			=				logging.get_logger(__name__)

UpperCAmelCase__  			=				{
    '''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 						a__					(					a_   ):





			'''simple docstring'''

			A						:				Union[str, Any]					      =			'''roberta'''
			def __init__(  self							:			Any			,    lowerCAmelCase_							:			int=50_265			,    lowerCAmelCase_							:			int=768			,    lowerCAmelCase_							:			Optional[Any]=12			,    lowerCAmelCase_							:			Tuple=12			,    lowerCAmelCase_							:			Optional[int]=3_072			,    lowerCAmelCase_							:			List[str]="gelu"			,    lowerCAmelCase_							:			List[str]=0.1			,    lowerCAmelCase_							:			Tuple=0.1			,    lowerCAmelCase_							:			List[Any]=512			,    lowerCAmelCase_							:			Union[str, Any]=2			,    lowerCAmelCase_							:			List[Any]=0.02			,    lowerCAmelCase_							:			Optional[int]=1E-12			,    lowerCAmelCase_							:			str=1			,    lowerCAmelCase_							:			Dict=0			,    lowerCAmelCase_							:			Any=2			,    lowerCAmelCase_							:			List[str]="absolute"			,    lowerCAmelCase_							:			Any=True			,    lowerCAmelCase_							:			Dict=None			,    **lowerCAmelCase_							:			Any			,    )      ->		int:
						super().__init__(pad_token_id=lowerCAmelCase_			,    bos_token_id=lowerCAmelCase_			,    eos_token_id=lowerCAmelCase_			,    **lowerCAmelCase_	)

						__A=	vocab_size
						__A=	hidden_size
						__A=	num_hidden_layers
						__A=	num_attention_heads
						__A=	hidden_act
						__A=	intermediate_size
						__A=	hidden_dropout_prob
						__A=	attention_probs_dropout_prob
						__A=	max_position_embeddings
						__A=	type_vocab_size
						__A=	initializer_range
						__A=	layer_norm_eps
						__A=	position_embedding_type
						__A=	use_cache
						__A=	classifier_dropout


class 						a__					(					a_   ):





			'''simple docstring'''

			@property
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
									__A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
									__A=	{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						    ]	)



