





'''simple docstring'''



import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
snake_case_   :  List[str]								=      '''stas/tiny-wmt19-en-ru'''

class     A_ (  lowerCAmelCase_    ,			unittest.TestCase  ):

			'''simple docstring'''





			_lowerCAmelCase						=    FSMTTokenizer
			_lowerCAmelCase						=    False




			def       a	(					self   ):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								_UpperCamelCase											=   [
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "w</w>",
								    "r</w>",
								    "t</w>",
								    "lo",
								    "low",
								    "er</w>",
								    "low</w>",
								    "lowest</w>",
								    "newer</w>",
								    "wider</w>",
								    "<unk>",
								]
								_UpperCamelCase											=   dict(zip(A_			,     range(len(A_   )   )   )   )
								_UpperCamelCase											=   ["l o 123", "lo w 1456", "e r</w> 1789", ""]

								_UpperCamelCase											=   ["en", "ru"]
								_UpperCamelCase											=   {
								    "langs": self.langs,
								    "src_vocab_size": 10,
								    "tgt_vocab_size": 20,
								}

								_UpperCamelCase											=   os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["src_vocab_file"]   )
								_UpperCamelCase											=   os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["tgt_vocab_file"]   )
								_UpperCamelCase											=   os.path.join(self.tmpdirname			,     "tokenizer_config.json"   )
								_UpperCamelCase											=   os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["merges_file"]   )
								with open(self.src_vocab_file			,     "w"   ) as fp:
													fp.write(json.dumps(A_   )   )
								with open(self.tgt_vocab_file			,     "w"   ) as fp:
													fp.write(json.dumps(A_   )   )
								with open(self.merges_file			,     "w"   ) as fp:
													fp.write("\n".join(A_   )   )
								with open(A_			,     "w"   ) as fp:
													fp.write(json.dumps(A_   )   )




			@cached_property
			def       a	(					self   ):
								return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"   )




			@cached_property
			def       a	(					self   ):
								return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"   )




			def       a	(					self   ):
								_UpperCamelCase											=   FSMTTokenizer.from_pretrained(A_   )
								self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]			,     ["en", "ru"]   )
								self.assertEqual(tokenizer.src_vocab_size			,     21   )
								self.assertEqual(tokenizer.tgt_vocab_size			,     21   )




			def       a	(					self   ):
								_UpperCamelCase											=   FSMTTokenizer(self.langs			,     self.src_vocab_file			,     self.tgt_vocab_file			,     self.merges_file   )

								_UpperCamelCase											=   "lower"
								_UpperCamelCase											=   ["low", "er</w>"]
								_UpperCamelCase											=   tokenizer.tokenize(A_   )
								self.assertListEqual(A_			,     A_   )

								_UpperCamelCase											=   tokens + ["<unk>"]
								_UpperCamelCase											=   [14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )			,     A_   )




			@slow
			def       a	(					self   ):
								_UpperCamelCase											=   self.tokenizer_ru_en

								_UpperCamelCase											=   tokenizer.encode("sequence builders"			,     add_special_tokens=A_   )
								_UpperCamelCase											=   tokenizer.encode("multi-sequence build"			,     add_special_tokens=A_   )

								_UpperCamelCase											=   tokenizer.build_inputs_with_special_tokens(A_   )
								_UpperCamelCase											=   tokenizer.build_inputs_with_special_tokens(A_			,     A_   )

								assert encoded_sentence == text + [2]
								assert encoded_pair == text + [2] + text_a + [2]




			@slow
			def       a	(					self   ):
								_UpperCamelCase											=   self.tokenizer_en_ru
								_UpperCamelCase											=   self.tokenizer_ru_en

								_UpperCamelCase											=   [
								    [
								        "Here's a little song I wrote. Don't worry, be happy.",
								        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
								    ],
								    ["This is it. No more. I'm done!", [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
								]

								# if data needs to be recreated or added, run:
								# import torch
								# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
								# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

								for src_text, tgt_input_ids in targets:
													_UpperCamelCase											=   tokenizer_enc.encode(A_			,     return_tensors=A_   )
													self.assertListEqual(A_			,     A_   )

													# and decode backward, using the reversed languages model
													_UpperCamelCase											=   tokenizer_dec.decode(A_			,     skip_special_tokens=A_   )
													self.assertEqual(A_			,     A_   )




			@slow
			def       a	(					self   ):
								_UpperCamelCase											=   FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"			,     do_lower_case=A_   )
								_UpperCamelCase											=   tokenizer.tokenize("USA is United States of America"   )
								_UpperCamelCase											=   ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
								self.assertListEqual(A_			,     A_   )




			@unittest.skip("FSMTConfig.__init__  requires non-optional args"   )
			def       a	(					self   ):
								pass




			@unittest.skip("FSMTConfig.__init__  requires non-optional args"   )
			def       a	(					self   ):
								pass










'''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   )



