

'''simple docstring'''


import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {"vocab_file": "vocab.json"}

lowerCAmelCase__										= {
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase__										= {"mgp-str": 27}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      str=   VOCAB_FILES_NAMES
							_lowerCamelCase		:      Union[str, Any]=   PRETRAINED_VOCAB_FILES_MAP
							_lowerCamelCase		:      int=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


							def __init__(			self    ,			_snake_case    ,			_snake_case="[GO]"    ,			_snake_case="[GO]"    ,			_snake_case="[s]"    ,			_snake_case="[GO]"    ,			**_snake_case)							->				str:
								super().__init__(
								    unk_token=_snake_case    ,			bos_token=_snake_case    ,			eos_token=_snake_case    ,			pad_token=_snake_case    ,			**_snake_case    ,			)

								with open(_snake_case    ,			encoding='utf-8') as vocab_handle:
									UpperCAmelCase_		:      int       	=					json.load(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					{v: k for k, v in self.vocab.items()}


							@property
							def 						_snake_case							(			self)							->				List[str]:
								return len(self.vocab)


							def 						_snake_case							(			self)							->				Union[str, Any]:
								return dict(self.vocab    ,			**self.added_tokens_encoder)


							def 						_snake_case							(			self    ,			_snake_case)							->				str:
								UpperCAmelCase_		:      Dict       	=					[]
								for s in text:
									char_tokens.extend(_snake_case)
								return char_tokens


							def 						_snake_case							(			self    ,			_snake_case)							->				Any:
								return self.vocab.get(_snake_case    ,			self.vocab.get(self.unk_token))


							def 						_snake_case							(			self    ,			_snake_case)							->				List[Any]:
								return self.decoder.get(_snake_case)





							def 						_snake_case							(			self    ,			_snake_case    ,			_snake_case = None)							->				Tuple[str]:
								if not os.path.isdir(_snake_case):
									logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case))
									return
								UpperCAmelCase_		:      Tuple       	=					os.path.join(
								    _snake_case    ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

								with open(_snake_case    ,			'w'    ,			encoding='utf-8') as f:
									f.write(json.dumps(self.vocab    ,			indent=2    ,			sort_keys=_snake_case    ,			ensure_ascii=_snake_case) + '\n')

								return (vocab_file,)









'''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),
								    ])






