



"""simple docstring"""


import json
import os
from typing import List, Optional, Tuple

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


snake_case_         = logging.get_logger(__name__)

snake_case_         = {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

snake_case_         = {
    """vocab_file""": {
        """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json""",
    },
    """merges_file""": {"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"""},
}

snake_case_         = {
    """microsoft/biogpt""": 1024,
}

def 				_lowerCAmelCase   (     lowercase_		):
						UpperCAmelCase    =				set()
						UpperCAmelCase    =				word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)		)
												UpperCAmelCase    =				char
						return pairs







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						__UpperCamelCase	       =							VOCAB_FILES_NAMES
						__UpperCamelCase	       =							PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase	       =							["""input_ids""", """attention_mask"""]






						def __init__(      self				:str     ,						lowercase_				:Tuple     ,						lowercase_				:Dict     ,						lowercase_				:int="<unk>"     ,						lowercase_				:Optional[Any]="<s>"     ,						lowercase_				:Optional[Any]="</s>"     ,						lowercase_				:int="</s>"     ,						lowercase_				:Optional[int]="<pad>"     ,						**lowercase_				:str     ,						)       ->    Dict:
												super().__init__(
												    bos_token=lowercase_     ,						eos_token=lowercase_     ,						sep_token=lowercase_     ,						unk_token=lowercase_     ,						pad_token=lowercase_     ,						**lowercase_     ,						)

												try:
																		import sacremoses
												except ImportError:
																		raise ImportError(
																		    'You need to install sacremoses to use BioGptTokenizer. '
																		    'See https://pypi.org/project/sacremoses/ for installation.'      )

												UpperCAmelCase    =				'en'
												UpperCAmelCase    =				sacremoses
												# cache of sm.MosesTokenizer instance
												UpperCAmelCase    =				{}
												UpperCAmelCase    =				{}
												with open(lowercase_     ,						encoding='utf-8'      ) as vocab_handle:
																		UpperCAmelCase    =				json.load(lowercase_      )
												UpperCAmelCase    =				{v: k for k, v in self.encoder.items()}
												with open(lowercase_     ,						encoding='utf-8'      ) as merges_handle:
																		UpperCAmelCase    =				merges_handle.read().split('\n'      )[:-1]
												UpperCAmelCase    =				[tuple(merge.split()[:2]      ) for merge in merges]
												UpperCAmelCase    =				dict(zip(lowercase_     ,						range(len(lowercase_      )      )      )      )
												UpperCAmelCase    =				{}






						@property
						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    Union[str, Any]:
												return len(self.encoder      )






						def 			UpperCAmelCase__  (      self				:List[Any]      )       ->    Union[str, Any]:
												return dict(self.encoder     ,						**self.added_tokens_encoder      )






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:Tuple     ,						lowercase_				:List[str]      )       ->    str:
												if lang not in self.cache_moses_tokenizer:
																		UpperCAmelCase    =				self.sm.MosesTokenizer(lang=lowercase_      )
																		UpperCAmelCase    =				moses_tokenizer
												return self.cache_moses_tokenizer[lang].tokenize(
												    lowercase_     ,						aggressive_dash_splits=lowercase_     ,						return_str=lowercase_     ,						escape=lowercase_      )






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:Optional[int]     ,						lowercase_				:Tuple      )       ->    Dict:
												if lang not in self.cache_moses_detokenizer:
																		UpperCAmelCase    =				self.sm.MosesDetokenizer(lang=lowercase_      )
																		UpperCAmelCase    =				moses_detokenizer
												return self.cache_moses_detokenizer[lang].detokenize(lowercase_      )






						def 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:int      )       ->    Tuple:
												UpperCAmelCase    =				tuple(token[:-1]      ) + (token[-1] + '</w>',)
												if token in self.cache:
																		return self.cache[token]
												UpperCAmelCase    =				get_pairs(lowercase_      )

												if not pairs:
																		return token + "</w>"

												while True:
																		UpperCAmelCase    =				min(lowercase_     ,						key=lambda lowercase_      : self.bpe_ranks.get(lowercase_     ,						float('inf'      )      )      )
																		if bigram not in self.bpe_ranks:
																								break
																		UpperCAmelCase   ,					UpperCAmelCase    =				bigram
																		UpperCAmelCase    =				[]
																		UpperCAmelCase    =				0
																		while i < len(lowercase_      ):
																								try:
																														UpperCAmelCase    =				word.index(lowercase_     ,						lowercase_      )
																								except ValueError:
																														new_word.extend(word[i:]      )
																														break
																								else:
																														new_word.extend(word[i:j]      )
																														UpperCAmelCase    =				j

																								if word[i] == first and i < len(lowercase_      ) - 1 and word[i + 1] == second:
																														new_word.append(first + second      )
																														i += 2
																								else:
																														new_word.append(word[i]      )
																														i += 1
																		UpperCAmelCase    =				tuple(lowercase_      )
																		UpperCAmelCase    =				new_word
																		if len(lowercase_      ) == 1:
																								break
																		else:
																								UpperCAmelCase    =				get_pairs(lowercase_      )
												UpperCAmelCase    =				' '.join(lowercase_      )
												if word == "\n  </w>":
																		UpperCAmelCase    =				'\n</w>'
												UpperCAmelCase    =				word
												return word






						def 			UpperCAmelCase__  (      self				:str     ,						lowercase_				:List[Any]     ,						lowercase_				:List[str]=False      )       ->    int:
												if bypass_tokenizer:
																		UpperCAmelCase    =				text.split()
												else:
																		UpperCAmelCase    =				self.moses_tokenize(lowercase_     ,						self.lang      )

												UpperCAmelCase    =				[]
												for token in text:
																		if token:
																								split_tokens.extend(list(self.bpe(lowercase_      ).split(' '      )      )      )

												return split_tokens






						def 			UpperCAmelCase__  (      self				:List[str]     ,						lowercase_				:Dict      )       ->    Dict:
												return self.encoder.get(lowercase_     ,						self.encoder.get(self.unk_token      )      )






						def 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:Optional[int]      )       ->    Dict:
												return self.decoder.get(lowercase_     ,						self.unk_token      )






						def 			UpperCAmelCase__  (      self				:int     ,						lowercase_				:Tuple      )       ->    Any:
												UpperCAmelCase    =				[t.replace(' '     ,						''      ).replace('</w>'     ,						' '      ) for t in tokens]
												UpperCAmelCase    =				''.join(lowercase_      ).split()
												# detokenize
												UpperCAmelCase    =				self.moses_detokenize(lowercase_     ,						self.lang      )
												return text






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:List[int]     ,						lowercase_				:Optional[List[int]] = None      )       ->    List[int]:
												if token_ids_a is None:
																		return [self.sep_token_id] + token_ids_a
												UpperCAmelCase    =				[self.sep_token_id]
												return sep + token_ids_a + sep + token_ids_a






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:List[int]     ,						lowercase_				:Optional[List[int]] = None     ,						lowercase_				:bool = False      )       ->    List[int]:
												if already_has_special_tokens:
																		return super().get_special_tokens_mask(
																		    token_ids_a=lowercase_     ,						token_ids_a=lowercase_     ,						already_has_special_tokens=lowercase_      )
												# no bos used in fairseq
												if token_ids_a is not None:
																		return [1] + ([0] * len(lowercase_      )) + [1] + ([0] * len(lowercase_      ))
												return [1] + ([0] * len(lowercase_      ))






						def 			UpperCAmelCase__  (      self				:Any     ,						lowercase_				:List[int]     ,						lowercase_				:Optional[List[int]] = None      )       ->    List[int]:
												UpperCAmelCase    =				[self.sep_token_id]

												# no bos used in fairseq
												if token_ids_a is None:
																		return len(token_ids_a + sep      ) * [0]
												return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






						def 			UpperCAmelCase__  (      self				:Tuple     ,						lowercase_				:str     ,						lowercase_				:Optional[str] = None      )       ->    Tuple[str]:
												if not os.path.isdir(lowercase_      ):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																		return
												UpperCAmelCase    =				os.path.join(
												    lowercase_     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
												UpperCAmelCase    =				os.path.join(
												    lowercase_     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

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

												UpperCAmelCase    =				0
												with open(lowercase_     ,						'w'     ,						encoding='utf-8'      ) as writer:
																		for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,						key=lambda lowercase_      : kv[1]      ):
																								if index != token_index:
																														logger.warning(
																														    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
																														    ' Please check that the tokenizer is not corrupted!'      )
																														UpperCAmelCase    =				token_index
																								writer.write(' '.join(lowercase_      ) + '\n'      )
																								index += 1

												return vocab_file, merge_file






						def __getstate__(      self				:Tuple      )       ->    Any:
												UpperCAmelCase    =				self.__dict__.copy()
												UpperCAmelCase    =				None
												return state






						def __setstate__(      self				:str     ,						lowercase_				:Optional[int]      )       ->    str:
												UpperCAmelCase    =				d

												try:
																		import sacremoses
												except ImportError:
																		raise ImportError(
																		    'You need to install sacremoses to use XLMTokenizer. '
																		    'See https://pypi.org/project/sacremoses/ for installation.'      )

												UpperCAmelCase    =				sacremoses










"""simple docstring"""


import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class    A_    (  nn.Linear       ):



						"""simple docstring"""






						def __init__(      self				:List[str]     ,						lowercase_				:int     ,						lowercase_				:int     ,						lowercase_				:bool = True     ,						lowercase_				:str = "constant"     ,						lowercase_				:float = 0.0     ,						lowercase_				:str = "topK"     ,						)       ->    Union[str, Any]:
												super(lowercase_     ,						self      ).__init__(in_features=lowercase_     ,						out_features=lowercase_     ,						bias=lowercase_      )
												assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
												UpperCAmelCase    =				pruning_method

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






						def 			UpperCAmelCase__  (      self				:Optional[Any]      )       ->    Dict:
												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 			UpperCAmelCase__  (      self				:Dict     ,						lowercase_				:torch.tensor     ,						lowercase_				:float      )       ->    List[str]:
												# Get the mask
												if self.pruning_method == "topK":
																		UpperCAmelCase    =				TopKBinarizer.apply(self.mask_scores     ,						lowercase_      )
												elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
																		UpperCAmelCase    =				'sigmoied' in self.pruning_method
																		UpperCAmelCase    =				ThresholdBinarizer.apply(self.mask_scores     ,						lowercase_     ,						lowercase_      )
												elif self.pruning_method == "magnitude":
																		UpperCAmelCase    =				MagnitudeBinarizer.apply(self.weight     ,						lowercase_      )
												elif self.pruning_method == "l0":
																		UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase    =				-0.1, 1.1, 2 / 3
																		if self.training:
																								UpperCAmelCase    =				torch.zeros_like(self.mask_scores      ).uniform_().clamp(0.0001     ,						0.9999      )
																								UpperCAmelCase    =				torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b      )
																		else:
																								UpperCAmelCase    =				torch.sigmoid(self.mask_scores      )
																		UpperCAmelCase    =				s * (r - l) + l
																		UpperCAmelCase    =				s_bar.clamp(min=0.0     ,						max=1.0      )
												# Mask weights with computed mask
												UpperCAmelCase    =				mask * self.weight
												# Compute output (linear layer) with masked weights
												return nn.functional.linear(lowercase_     ,						lowercase_     ,						self.bias      )





