
'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation

UpperCAmelCase            = logging.get_logger(__name__)

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

UpperCAmelCase            = {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

UpperCAmelCase            = {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

@lru_cache()
def   A			(							):
						snake_case							:  Dict			=     (
						    list(range(ord('''!'''							)     ,				ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)     ,				ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)     ,				ord('''ÿ'''							) + 1							)							)
						)
						snake_case							:  Tuple			=     bs[:]
						snake_case							:  int			=     0
						for b in range(2**8							):
												if b not in bs:
																		bs.append(A_							)
																		cs.append(2**8 + n							)
																		n += 1
						snake_case							:  Union[str, Any]			=     [chr(A_							) for n in cs]
						return dict(zip(A_     ,				A_							)							)

def   A			(							A_				:       Any							):
						snake_case							:  List[str]			=     set()
						snake_case							:  int			=     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)							)
												snake_case							:  List[str]			=     char
						return pairs





class     a (					__magic_name__     ):
				_snake_case		     =			VOCAB_FILES_NAMES
				_snake_case		     =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case		     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		     =			['''input_ids''', '''attention_mask''']




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Tuple="replace",     SCREAMING_SNAKE_CASE_						:					Dict="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					str="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					Any="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					List[str]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										snake_case							:  Dict			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else bos_token
										snake_case							:  Union[str, Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else eos_token
										snake_case							:  List[Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else unk_token
										snake_case							:  Tuple			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else pad_token
										super().__init__(
										    errors=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     add_prefix_space=SCREAMING_SNAKE_CASE_,     add_bos_token=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )
										snake_case							:  int			=     add_bos_token

										with open(SCREAMING_SNAKE_CASE_,     encoding='''utf-8'''   ) as vocab_handle:
																snake_case							:  Optional[int]			=     json.load(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     {v: k for k, v in self.encoder.items()}
										snake_case							:  Tuple			=     errors  # how to handle errors in decoding
										snake_case							:  List[str]			=     bytes_to_unicode()
										snake_case							:  Optional[int]			=     {v: k for k, v in self.byte_encoder.items()}
										with open(SCREAMING_SNAKE_CASE_,     encoding='''utf-8'''   ) as merges_handle:
																snake_case							:  str			=     merges_handle.read().split('''\n'''   )[1:-1]
										snake_case							:  Optional[Any]			=     [tuple(merge.split()   ) for merge in bpe_merges]
										snake_case							:  Tuple			=     dict(zip(SCREAMING_SNAKE_CASE_,     range(len(SCREAMING_SNAKE_CASE_   )   )   )   )
										snake_case							:  str			=     {}
										snake_case							:  Dict			=     add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case							:  Union[str, Any]			=     re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''   )




				@property
				def 						__snake_case				(				self						:					Tuple   ):
										return len(self.encoder   )




				def 						__snake_case				(				self						:					List[Any]   ):
										return dict(self.encoder,     **self.added_tokens_encoder   )




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if token in self.cache:
																return self.cache[token]
										snake_case							:  List[Any]			=     tuple(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     get_pairs(SCREAMING_SNAKE_CASE_   )

										if not pairs:
																return token

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

																						if word[i] == first and i < len(SCREAMING_SNAKE_CASE_   ) - 1 and word[i + 1] == second:
																												new_word.append(first + second   )
																												i += 2
																						else:
																												new_word.append(word[i]   )
																												i += 1
																snake_case							:  Tuple			=     tuple(SCREAMING_SNAKE_CASE_   )
																snake_case							:  Tuple			=     new_word
																if len(SCREAMING_SNAKE_CASE_   ) == 1:
																						break
																else:
																						snake_case							:  List[str]			=     get_pairs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     ''' '''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     word
										return word




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Optional[Any]=None   ):
										if self.add_bos_token:
																snake_case							:  int			=     [self.bos_token_id]
										else:
																snake_case							:  Dict			=     []

										snake_case							:  List[str]			=     bos_token_ids + token_ids_a

										if token_ids_a is None:
																return output

										return output + bos_token_ids + token_ids_a




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					List[int],     SCREAMING_SNAKE_CASE_						:					Optional[List[int]] = None,     SCREAMING_SNAKE_CASE_						:					bool = False   ):
										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

										if not self.add_bos_token:
																return super().get_special_tokens_mask(
																    token_ids_a=SCREAMING_SNAKE_CASE_,     token_ids_a=SCREAMING_SNAKE_CASE_,     already_has_special_tokens=SCREAMING_SNAKE_CASE_   )

										if token_ids_a is None:
																return [1] + ([0] * len(SCREAMING_SNAKE_CASE_   ))
										return [1] + ([0] * len(SCREAMING_SNAKE_CASE_   )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_   ))




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										snake_case							:  Optional[int]			=     []
										for token in re.findall(self.pat,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  Dict			=     ''''''.join(
																    self.byte_encoder[b] for b in token.encode('''utf-8'''   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE_   ).split(''' '''   )   )
										return bpe_tokens




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return self.encoder.get(SCREAMING_SNAKE_CASE_,     self.encoder.get(self.unk_token   )   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										return self.decoder.get(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict   ):
										snake_case							:  Union[str, Any]			=     ''''''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8''',     errors=self.errors   )
										return text




				def 						__snake_case				(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Optional[str] = None   ):
										if not os.path.isdir(SCREAMING_SNAKE_CASE_   ):
																logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""   )
																return
										snake_case							:  int			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
										snake_case							:  List[Any]			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

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

										snake_case							:  Optional[int]			=     0
										with open(SCREAMING_SNAKE_CASE_,     '''w''',     encoding='''utf-8'''   ) as writer:
																writer.write('''#version: 0.2\n'''   )
																for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),     key=lambda SCREAMING_SNAKE_CASE_   : 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!'''   )
																												snake_case							:  Union[str, Any]			=     token_index
																						writer.write(''' '''.join(SCREAMING_SNAKE_CASE_   ) + '''\n'''   )
																						index += 1

										return vocab_file, merge_file




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict=False,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  Any			=     kwargs.pop('''add_prefix_space''',     self.add_prefix_space   )
										if is_split_into_words or add_prefix_space:
																snake_case							:  Dict			=     ''' ''' + text
										return (text, kwargs)




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					"Conversation"   ):
										snake_case							:  List[str]			=     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_   ) + [self.eos_token_id]   )
										if len(SCREAMING_SNAKE_CASE_   ) > self.model_max_length:
																snake_case							:  str			=     input_ids[-self.model_max_length :]
										return input_ids









'''simple docstring'''







import math

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

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 					__UpperCAmelCase (		nn.Linear	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = True  ,		_UpperCAmelCase = "constant"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = "topK"  ,		):
             super(_UpperCAmelCase  ,		self						).__init__(in_features=_UpperCAmelCase  ,		out_features=_UpperCAmelCase  ,		bias=_UpperCAmelCase						)
             assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
             UpperCAmelCase__	:   Union[str, Any]												=	pruning_method

             if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                    UpperCAmelCase__	:   Union[str, Any]												=	mask_scale
                    UpperCAmelCase__	:   Optional[int]												=	mask_init
                    UpperCAmelCase__	:   List[str]												=	nn.Parameter(torch.empty(self.weight.size()						)						)
                    self.init_mask()




      def 							lowerCamelCase     (					self						):
             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 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             # Get the mask
             if self.pruning_method == "topK":
                    UpperCAmelCase__	:   Union[str, Any]												=	TopKBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase						)
             elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                    UpperCAmelCase__	:   List[Any]												=	'''sigmoied''' in self.pruning_method
                    UpperCAmelCase__	:   Tuple												=	ThresholdBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
             elif self.pruning_method == "magnitude":
                    UpperCAmelCase__	:   Optional[int]												=	MagnitudeBinarizer.apply(self.weight  ,		_UpperCAmelCase						)
             elif self.pruning_method == "l0":
                    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	-0.1, 1.1, 2 / 3
                    if self.training:
                           UpperCAmelCase__	:   Optional[int]												=	torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01  ,		0.99_99						)
                           UpperCAmelCase__	:   Tuple												=	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                    else:
                           UpperCAmelCase__	:   Any												=	torch.sigmoid(self.mask_scores						)
                    UpperCAmelCase__	:   List[Any]												=	s * (r - l) + l
                    UpperCAmelCase__	:   Optional[int]												=	s_bar.clamp(min=0.0  ,		max=1.0						)
             # Mask weights with computed mask
             UpperCAmelCase__	:   Union[str, Any]												=	mask * self.weight
             # Compute output (linear layer) with masked weights
             return nn.functional.linear(_UpperCAmelCase  ,		_UpperCAmelCase  ,		self.bias						)