

'''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 					lowercase						(  a_,					unittest.TestCase ):
							_lowerCamelCase		:      List[Any]=   MgpstrTokenizer
							_lowerCamelCase		:      Dict=   False
							_lowerCamelCase		:      Union[str, Any]=   {}
							_lowerCamelCase		:      Any=   False


							def 						_snake_case							(			self)							->				List[str]:
								super().setUp()

								# fmt: off
								UpperCAmelCase_		:      Any       	=					['[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
								UpperCAmelCase_		:      List[str]       	=					dict(zip(_snake_case    ,			range(len(_snake_case))))

								UpperCAmelCase_		:      List[Any]       	=					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(_snake_case) + '\n')


							def 						_snake_case							(			self    ,			**_snake_case)							->				Dict:
								return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,			**_snake_case)


							def 						_snake_case							(			self    ,			_snake_case)							->				Optional[int]:
								UpperCAmelCase_		:      List[str]       	=					'tester'
								UpperCAmelCase_		:      Optional[Any]       	=					'tester'
								return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.')
							def 						_snake_case							(			self)							->				Dict:
								pass


							def 						_snake_case							(			self)							->				List[str]:
								UpperCAmelCase_		:      Union[str, Any]       	=					self.get_tokenizers(do_lower_case=_snake_case)
								for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										UpperCAmelCase_		:      Union[str, Any]       	=					'[SPECIAL_TOKEN]'

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

										UpperCAmelCase_		:      Dict       	=					tokenizer.decode(_snake_case    ,			skip_special_tokens=_snake_case)
										self.assertTrue(special_token not in decoded)


							def 						_snake_case							(			self)							->				Optional[Any]:
								UpperCAmelCase_		:      str       	=					self.get_tokenizers()
								for tokenizer in tokenizers:
									with self.subTest(F"""{tokenizer.__class__.__name__}"""):
										UpperCAmelCase_		:      Any       	=					self.get_input_output_texts(_snake_case)

										UpperCAmelCase_		:      str       	=					tokenizer.tokenize(_snake_case)
										UpperCAmelCase_		:      str       	=					tokenizer.convert_tokens_to_ids(_snake_case)
										UpperCAmelCase_		:      List[str]       	=					tokenizer.encode(_snake_case    ,			add_special_tokens=_snake_case)
										self.assertListEqual(_snake_case    ,			_snake_case)

										UpperCAmelCase_		:      Dict       	=					tokenizer.convert_ids_to_tokens(_snake_case)
										self.assertNotEqual(len(_snake_case)    ,			0)
										UpperCAmelCase_		:      Tuple       	=					tokenizer.decode(_snake_case)
										self.assertIsInstance(_snake_case    ,			_snake_case)

										self.assertEqual(text_a.replace(' '    ,			'')    ,			_snake_case)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.')
							def 						_snake_case							(			self)							->				List[str]:
								pass





							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer')
							def 						_snake_case							(			self)							->				Optional[int]:
								pass









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "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						(  a_ ):
							_lowerCamelCase		:      Any=   "xlm-roberta-xl"


							def __init__(			self    ,			_snake_case=25_0880    ,			_snake_case=2560    ,			_snake_case=36    ,			_snake_case=32    ,			_snake_case=1_0240    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=514    ,			_snake_case=1    ,			_snake_case=0.02    ,			_snake_case=1e-05    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=2    ,			_snake_case="absolute"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Optional[Any]:
								super().__init__(pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      str       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps
								UpperCAmelCase_		:      Dict       	=					position_embedding_type
								UpperCAmelCase_		:      int       	=					use_cache
								UpperCAmelCase_		:      List[Any]       	=					classifier_dropout



class 					lowercase						(  a_ ):


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






