

from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						=					{
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}

class  a     (		UpperCAmelCase ):
						_lowercase			  =	"flaubert"
						_lowercase			  =	{
						    "hidden_size": "emb_dim",
						    "num_attention_heads": "n_heads",
						    "num_hidden_layers": "n_layers",
						    "n_words": "vocab_size",  # For backward compatibility
						}


						def __init__(				self  ,    A_=False  ,    A_=0.0  ,    A_=30145  ,    A_=2048  ,    A_=12  ,    A_=16  ,    A_=0.1  ,    A_=0.1  ,    A_=True  ,    A_=False  ,    A_=False  ,    A_=False  ,    A_=1  ,    A_=True  ,    A_=512  ,    A_=2048**-0.5  ,    A_=1e-12  ,    A_=0.02  ,    A_=0  ,    A_=1  ,    A_=2  ,    A_=3  ,    A_=5  ,    A_=True  ,    A_="first"  ,    A_=True  ,    A_=None  ,    A_=True  ,    A_=0.1  ,    A_=5  ,    A_=5  ,    A_=0  ,    A_=0  ,    A_=2  ,    A_=0  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			pre_norm
										_UpperCAmelCase				:						Union[str, Any]       						=			layerdrop
										_UpperCAmelCase				:						List[str]       						=			vocab_size
										_UpperCAmelCase				:						Any       						=			emb_dim
										_UpperCAmelCase				:						List[str]       						=			n_layers
										_UpperCAmelCase				:						Tuple       						=			n_heads
										_UpperCAmelCase				:						Union[str, Any]       						=			dropout
										_UpperCAmelCase				:						Any       						=			attention_dropout
										_UpperCAmelCase				:						List[str]       						=			gelu_activation
										_UpperCAmelCase				:						Dict       						=			sinusoidal_embeddings
										_UpperCAmelCase				:						Any       						=			causal
										_UpperCAmelCase				:						str       						=			asm
										_UpperCAmelCase				:						List[Any]       						=			n_langs
										_UpperCAmelCase				:						List[str]       						=			use_lang_emb
										_UpperCAmelCase				:						Tuple       						=			layer_norm_eps
										_UpperCAmelCase				:						Dict       						=			bos_index
										_UpperCAmelCase				:						str       						=			eos_index
										_UpperCAmelCase				:						List[str]       						=			pad_index
										_UpperCAmelCase				:						List[str]       						=			unk_index
										_UpperCAmelCase				:						Tuple       						=			mask_index
										_UpperCAmelCase				:						Any       						=			is_encoder
										_UpperCAmelCase				:						Dict       						=			max_position_embeddings
										_UpperCAmelCase				:						List[str]       						=			embed_init_std
										_UpperCAmelCase				:						Optional[int]       						=			init_std
										_UpperCAmelCase				:						Optional[int]       						=			summary_type
										_UpperCAmelCase				:						Union[str, Any]       						=			summary_use_proj
										_UpperCAmelCase				:						int       						=			summary_activation
										_UpperCAmelCase				:						Dict       						=			summary_proj_to_labels
										_UpperCAmelCase				:						List[Any]       						=			summary_first_dropout
										_UpperCAmelCase				:						Optional[int]       						=			start_n_top
										_UpperCAmelCase				:						List[Any]       						=			end_n_top
										_UpperCAmelCase				:						Any       						=			mask_token_id
										_UpperCAmelCase				:						Any       						=			lang_id

										if "n_words" in kwargs:
														_UpperCAmelCase				:						Union[str, Any]       						=			kwargs["n_words"]

										super().__init__(pad_token_id=A_  ,    bos_token_id=A_  ,    **A_		)







class  a     (		UpperCAmelCase ):


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										if self.task == "multiple-choice":
														_UpperCAmelCase				:						Optional[Any]       						=			{0: "batch", 1: "choice", 2: "sequence"}
										else:
														_UpperCAmelCase				:						int       						=			{0: "batch", 1: "sequence"}
										return OrderedDict(
										    [
										        ("input_ids", dynamic_axis),
										        ("attention_mask", dynamic_axis),
										    ]		)








'''simple docstring'''


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_reformer import ReformerTokenizer
else:
				_snake_case      :      int   							=		None


_snake_case      :      Union[str, Any]   							=		logging.get_logger(__name__)


_snake_case      :      Union[str, Any]   							=		'▁'

_snake_case      :      Any   							=		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :      List[str]   							=		{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

_snake_case      :      Union[str, Any]   							=		{
    'google/reformer-crime-and-punishment': 524288,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']
							lowercase_							=				ReformerTokenizer







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Any="<unk>"							,			lowerCAmelCase_       :  List[str]=[]							,			**lowerCAmelCase_       :  List[Any]							,			)			->							Dict:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_							,			tokenizer_file=lowerCAmelCase_							,			eos_token=lowerCAmelCase_							,			unk_token=lowerCAmelCase_							,			additional_special_tokens=lowerCAmelCase_							,			**lowerCAmelCase_							,			)

														_a	     =				vocab_file
														_a	     =				False if not self.vocab_file else True







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = None					)			->							Tuple[str]:


														"""simple docstring"""


														if not self.can_save_slow_tokenizer:
																					raise ValueError(
																					    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																					    '''tokenizer.'''					)

														if not os.path.isdir(lowerCAmelCase_					):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
																					return
														_a	     =				os.path.join(
														    lowerCAmelCase_							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase_					):
																					copyfile(self.vocab_file							,			lowerCAmelCase_					)

														return (out_vocab_file,)

