





'''simple docstring'''



from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_   :  List[Any]								=      logging.get_logger(__name__)

snake_case_   :  List[Any]								=      {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_   :  List[str]								=      {
    '''vocab_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''openai/clip-vit-base-patch32''': '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''openai/clip-vit-base-patch32''': (
            '''https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'''
        ),
    },
}

snake_case_   :  Optional[Any]								=      {
    '''openai/clip-vit-base-patch32''': 77,
}

class     A_ (  lowerCAmelCase_  ):

			'''simple docstring'''





			_lowerCAmelCase						=    VOCAB_FILES_NAMES
			_lowerCAmelCase						=    PRETRAINED_VOCAB_FILES_MAP
			_lowerCAmelCase						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_lowerCAmelCase						=    ["""input_ids""", """attention_mask"""]
			_lowerCAmelCase						=    CLIPTokenizer




			def __init__(					self			,     A_=None			,     A_=None			,     A_=None			,     A_="<|endoftext|>"			,     A_="<|startoftext|>"			,     A_="<|endoftext|>"			,     A_="<|endoftext|>"			,     **A_			,     ):
								super().__init__(
								    A_			,     A_			,     tokenizer_file=A_			,     unk_token=A_			,     bos_token=A_			,     eos_token=A_			,     pad_token=A_			,     **A_			,     )

								if not isinstance(self.backend_tokenizer.pre_tokenizer			,     pre_tokenizers.Sequence   ):
													raise ValueError(
													    "The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"
													    " heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"
													    " to be compatible with this version.The easiest way to do so is"
													    " `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"
													    " to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"
													    " transformers."   )

								self._wrap_decode_method_backend_tokenizer()




			def       a	(					self   ):
								_UpperCamelCase											=   self.backend_tokenizer.decode

								def new_decode_method(*A_			,     **A_   ):
													_UpperCamelCase											=   orig_decode_method(*A_			,     **A_   )
													_UpperCamelCase											=   text.replace(self.backend_tokenizer.model.end_of_word_suffix			,     " "   ).strip()
													return text

								_UpperCamelCase											=   new_decode_method




			def       a	(					self			,     A_			,     A_ = None   ):
								_UpperCamelCase											=   [self.bos_token_id]
								_UpperCamelCase											=   [self.eos_token_id]

								if token_ids_a is None:
													return bos_token + token_ids_a + eos_token
								return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token




			def       a	(					self			,     A_			,     A_ = None   ):
								_UpperCamelCase											=   [self.bos_token_id]
								_UpperCamelCase											=   [self.eos_token_id]

								if token_ids_a is None:
													return len(bos_token + token_ids_a + eos_token   ) * [0]
								return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]




			def       a	(					self			,     A_			,     A_ = None   ):
								_UpperCamelCase											=   self._tokenizer.model.save(A_			,     name=A_   )
								return tuple(A_   )










'''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			,     A_			,     A_			,     A_ = True			,     A_ = "constant"			,     A_ = 0.0			,     A_ = "topK"			,     ):
								super(A_			,     self   ).__init__(in_features=A_			,     out_features=A_			,     bias=A_   )
								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       a	(					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       a	(					self			,     A_			,     A_   ):
								# Get the mask
								if self.pruning_method == "topK":
													_UpperCamelCase											=   TopKBinarizer.apply(self.mask_scores			,     A_   )
								elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
													_UpperCamelCase											=   "sigmoied" in self.pruning_method
													_UpperCamelCase											=   ThresholdBinarizer.apply(self.mask_scores			,     A_			,     A_   )
								elif self.pruning_method == "magnitude":
													_UpperCamelCase											=   MagnitudeBinarizer.apply(self.weight			,     A_   )
								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(A_			,     A_			,     self.bias   )



