



import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
class      A__       (						nn.Linear    ):





							def __init__(			self  :       Tuple					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       int					,			_UpperCAmelCase  :       bool = True					,			_UpperCAmelCase  :       str = "constant"					,			_UpperCAmelCase  :       float = 0.0					,			_UpperCAmelCase  :       str = "topK"					,			)						->		str:



											"""simple docstring"""


											super(_A					,			self    ).__init__(in_features=_A					,			out_features=_A					,			bias=_A    )
											assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
											__lowercase								= pruning_method

											if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
															__lowercase								= mask_scale
															__lowercase								= mask_init
															__lowercase								= nn.Parameter(torch.empty(self.weight.size()    )    )
															self.init_mask()





							def       a__			(			self  :       Union[str, Any]    )						->		Union[str, Any]:



											"""simple docstring"""


											if self.mask_init == "constant":
															init.constant_(self.mask_scores					,			val=self.mask_scale    )
											elif self.mask_init == "uniform":
															init.uniform_(self.mask_scores					,			a=-self.mask_scale					,			b=self.mask_scale    )
											elif self.mask_init == "kaiming":
															init.kaiming_uniform_(self.mask_scores					,			a=math.sqrt(5    )    )





							def       a__			(			self  :       List[Any]					,			_UpperCAmelCase  :       torch.tensor					,			_UpperCAmelCase  :       float    )						->		Dict:



											"""simple docstring"""


											if self.pruning_method == "topK":
															__lowercase								= TopKBinarizer.apply(self.mask_scores					,			_A    )
											elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
															__lowercase								= '''sigmoied''' in self.pruning_method
															__lowercase								= ThresholdBinarizer.apply(self.mask_scores					,			_A					,			_A    )
											elif self.pruning_method == "magnitude":
															__lowercase								= MagnitudeBinarizer.apply(self.weight					,			_A    )
											elif self.pruning_method == "l0":
															__lowercase								= -0.1, 1.1, 2 / 3
															if self.training:
																			__lowercase								= torch.zeros_like(self.mask_scores    ).uniform_().clamp(0.0_001					,			0.9_999    )
																			__lowercase								= torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b    )
															else:
																			__lowercase								= torch.sigmoid(self.mask_scores    )
															__lowercase								= s * (r - l) + l
															__lowercase								= s_bar.clamp(min=0.0					,			max=1.0    )
											# Mask weights with computed mask
											__lowercase								= mask * self.weight
											# Compute output (linear layer) with masked weights
											return nn.functional.linear(_A					,			_A					,			self.bias    )







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

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


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							SCREAMING_SNAKE_CASE__							=    None


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__							=    {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__							=    {
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							VOCAB_FILES_NAMES
							lowerCAmelCase__		:						List[str]         =							PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__		:						List[Any]         =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__		:						Dict         =							["input_ids", "attention_mask"]
							lowerCAmelCase__		:						List[str]         =							XLMRobertaTokenizer





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



											"""simple docstring"""


											__lowercase								= AddedToken(_UpperCAmelCase					,			lstrip=_UpperCAmelCase					,			rstrip=_UpperCAmelCase    ) if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ) else mask_token

											super().__init__(
											    _UpperCAmelCase					,			tokenizer_file=_UpperCAmelCase					,			bos_token=_UpperCAmelCase					,			eos_token=_UpperCAmelCase					,			sep_token=_UpperCAmelCase					,			cls_token=_UpperCAmelCase					,			unk_token=_UpperCAmelCase					,			pad_token=_UpperCAmelCase					,			mask_token=_UpperCAmelCase					,			**_UpperCAmelCase					,			)

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





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowercase								= [self.cls_token_id]
											__lowercase								= [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep





							def       a__			(			self  :       Union[str, Any]					,			_UpperCAmelCase  :       List[int]					,			_UpperCAmelCase  :       Optional[List[int]] = None    )						->		List[int]:



											"""simple docstring"""


											__lowercase								= [self.sep_token_id]
											__lowercase								= [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep    ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]





							def       a__			(			self  :       str					,			_UpperCAmelCase  :       str					,			_UpperCAmelCase  :       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(_UpperCAmelCase    ):
															logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
															return
											__lowercase								= os.path.join(
											    _UpperCAmelCase					,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

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

											return (out_vocab_file,)





