





'''simple docstring'''






import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




@require_tokenizers
class 				UpperCAmelCase  (    unittest.TestCase     ):
      _lowercase:    Union[str, Any]		  =     EsmTokenizer




      def        lowercase__					(    self    :				List[Any]						)       ->   int:
            super().setUp()
            _lowerCAmelCase							  =							tempfile.mkdtemp()
            # fmt: off
            _lowerCAmelCase							  =							["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
            # fmt: on
            _lowerCAmelCase							  =							os.path.join(self.tmpdirname    ,    VOCAB_FILES_NAMES["""vocab_file"""]						)
            with open(self.vocab_file    ,    """w"""    ,    encoding="""utf-8"""						) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)




      def        lowercase__					(    self    :				str    ,    **__snake_case    :				Dict						)       ->   int:
            return [self.get_tokenizer(**UpperCAmelCase_						)]




      def        lowercase__					(    self    :				Dict    ,    **__snake_case    :				List[Any]						)       ->   Optional[Any]:
            return self.tokenizer_class.from_pretrained(self.tmpdirname    ,    **UpperCAmelCase_						)




      def        lowercase__					(    self    :				Any						)       ->   Union[str, Any]:
            _lowerCAmelCase							  =							self.tokenizer_class(self.vocab_file						)

            _lowerCAmelCase							  =							tokenizer.tokenize("""LAGVS"""						)
            self.assertListEqual(UpperCAmelCase_    ,    ["""L""", """A""", """G""", """V""", """S"""]						)
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_						)    ,    [4, 5, 6, 7, 8]						)




      def        lowercase__					(    self    :				Dict						)       ->   Optional[Any]:
            _lowerCAmelCase							  =							self.tokenizer_class(self.vocab_file						)

            _lowerCAmelCase							  =							'LAGVS'
            self.assertListEqual(tokenizer.encode(UpperCAmelCase_						)    ,    [0, 4, 5, 6, 7, 8, 2]						)




      def        lowercase__					(    self    :				Tuple						)       ->   Union[str, Any]:
            _lowerCAmelCase							  =							self.tokenizer_class(self.vocab_file						)

            _lowerCAmelCase							  =							['LAGVS', 'WCB']
            _lowerCAmelCase							  =							tokenizer(UpperCAmelCase_    ,    padding=UpperCAmelCase_						)['input_ids']

            self.assertListEqual(UpperCAmelCase_    ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]						)




      def        lowercase__					(    self    :				Any						)       ->   Any:
            _lowerCAmelCase							  =							self.tokenizer_class(self.vocab_file						)

            _lowerCAmelCase							  =							['LAGVS', 'WCB']
            _lowerCAmelCase							  =							tokenizer(UpperCAmelCase_    ,    padding=UpperCAmelCase_						)['input_ids']

            self.assertListEqual(UpperCAmelCase_    ,    [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]						)




      def        lowercase__					(    self    :				Any						)       ->   Dict:
            _lowerCAmelCase							  =							self.get_tokenizers(fast=UpperCAmelCase_						)
            for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"						):
                        _lowerCAmelCase							  =							'<unk>'
                        _lowerCAmelCase							  =							'<mask>'

                        _lowerCAmelCase							  =							tokenizer.tokenize(UpperCAmelCase_						)
                        _lowerCAmelCase							  =							tokenizer.tokenize(UpperCAmelCase_						)

                        self.assertEqual(len(UpperCAmelCase_						)    ,    1						)
                        self.assertEqual(len(UpperCAmelCase_						)    ,    1						)
                        self.assertEqual(token_a[0]    ,    UpperCAmelCase_						)
                        self.assertEqual(token_a[0]    ,    UpperCAmelCase_						)








'''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     ):




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

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




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






