

"""simple docstring"""




# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
__A	:       str        =						1E-10
__A	:       Dict        =						'This is incredible! I love it, this is the best chicken I have ever had.'
__A	:       Tuple        =						1_00





class      __UpperCamelCase					(  nn.Module		):


		def __init__(self					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						str="gpt2-medium"	,	__SCREAMING_SNAKE_CASE					:						str=False	,	__SCREAMING_SNAKE_CASE					:						List[Any]="cpu"):
							super().__init__()
							A     =   GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
							A     =   GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE)
							A     =   self.encoder.transformer.config.hidden_size
							A     =   ClassificationHead(class_size=__SCREAMING_SNAKE_CASE	,	embed_size=self.embed_size)
							A     =   cached_mode
							A     =   device


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							return self.classifier_head


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							for param in self.encoder.parameters():
												A     =   False
							self.classifier_head.train()


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]):
							A     =   x.ne(0).unsqueeze(2).repeat(1	,	1	,	self.embed_size).float().to(self.device).detach()
							A     =   self.encoder.transformer(__SCREAMING_SNAKE_CASE)["last_hidden_state"]
							A     =   hidden * mask
							A     =   torch.sum(__SCREAMING_SNAKE_CASE	,	dim=1) / (torch.sum(__SCREAMING_SNAKE_CASE	,	dim=1).detach() + EPSILON)
							return avg_hidden


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Optional[int]):
							if self.cached_mode:
												A     =   x.to(self.device)
							else:
												A     =   self.avg_representation(x.to(self.device))

							A     =   self.classifier_head(__SCREAMING_SNAKE_CASE)
							A     =   nn.functional.log_softmax(__SCREAMING_SNAKE_CASE	,	dim=-1)

							return probs





class      __UpperCamelCase					(  data.Dataset		):


		def __init__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]):
							A     =   X
							A     =   y


		def __len__(self					:						List[Any]):
							return len(self.X)


		def __getitem__(self					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Tuple):
							A     =   {}
							A     =   self.X[index]
							A     =   self.y[index]
							return data

def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					def pad_sequences(lowercase__ ):
										A     =   [len(lowercase__ ) for seq in sequences]

										A     =   torch.zeros(len(lowercase__ )     ,				max(lowercase__ ) ).long()  # padding value = 0

										for i, seq in enumerate(lowercase__ ):
															A     =   lengths[i]
															A     =   seq[:end]

										return padded_sequences, lengths

					A     =   {}
					for key in data[0].keys():
										A     =   [d[key] for d in data]

					A     ,       A     =   pad_sequences(item_info["X"] )
					A     =   torch.tensor(item_info["y"]     ,				dtype=torch.long )

					return x_batch, y_batch
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   {}
					for key in data[0].keys():
										A     =   [d[key] for d in data]

					A     =   torch.cat(item_info["X"]     ,				0 )
					A     =   torch.tensor(item_info["y"]     ,				dtype=torch.long )

					return x_batch, y_batch
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=0     ,				lowercase__=10     ,				lowercase__="cpu" ):
					"""simple docstring"""
					A     =   0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(lowercase__ ):
										A     ,       A     =   input_t.to(lowercase__ ), target_t.to(lowercase__ )

										optimizer.zero_grad()

										A     =   discriminator(lowercase__ )
										A     =   nn.functional.nll_loss(lowercase__     ,				lowercase__ )
										loss.backward(retain_graph=lowercase__ )
										optimizer.step()

										samples_so_far += len(lowercase__ )

										if batch_idx % log_interval == 0:
															print(
															    "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
															        epoch + 1     ,				lowercase__     ,				len(data_loader.dataset )     ,				100 * samples_so_far / len(data_loader.dataset )     ,				loss.item()     ,				) )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__="cpu" ):
					"""simple docstring"""
					discriminator.eval()
					A     =   0
					A     =   0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															A     ,       A     =   input_t.to(lowercase__ ), target_t.to(lowercase__ )
															A     =   discriminator(lowercase__ )
															# sum up batch loss
															test_loss += nn.functional.nll_loss(lowercase__     ,				lowercase__     ,				reduction="sum" ).item()
															# get the index of the max log-probability
															A     =   output_t.argmax(dim=1     ,				keepdim=lowercase__ )
															correct += pred_t.eq(target_t.view_as(lowercase__ ) ).sum().item()

					test_loss /= len(data_loader.dataset )

					print(
					    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
					        lowercase__     ,				lowercase__     ,				len(data_loader.dataset )     ,				1_00.0 * correct / len(data_loader.dataset ) ) )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=False     ,				lowercase__="cpu" ):
					"""simple docstring"""
					A     =   model.tokenizer.encode(lowercase__ )
					A     =   torch.tensor([input_t]     ,				dtype=torch.long     ,				device=lowercase__ )
					if cached:
										A     =   model.avg_representation(lowercase__ )

					A     =   model(lowercase__ ).data.cpu().numpy().flatten().tolist()
					print("Input sentence:"     ,				lowercase__ )
					print(
					    "Predictions:"     ,				", ".join("{}: {:.4f}".format(lowercase__     ,				math.exp(lowercase__ ) ) for c, log_prob in zip(lowercase__     ,				lowercase__ ) )     ,				)
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=False     ,				lowercase__="cpu" ):
					"""simple docstring"""
					A     =   torch.utils.data.DataLoader(dataset=lowercase__     ,				batch_size=lowercase__     ,				collate_fn=lowercase__ )

					A     =   []
					A     =   []
					for batch_idx, (x, y) in enumerate(tqdm(lowercase__     ,				ascii=lowercase__ ) ):
										with torch.no_grad():
															A     =   x.to(lowercase__ )
															A     =   discriminator.avg_representation(lowercase__ ).cpu().detach()
															A     =   torch.unbind(avg_rep.unsqueeze(1 ) )
															xs += avg_rep_list
															ys += y.cpu().numpy().tolist()

					A     =   torch.utils.data.DataLoader(
					    dataset=Dataset(lowercase__     ,				lowercase__ )     ,				batch_size=lowercase__     ,				shuffle=lowercase__     ,				collate_fn=lowercase__ )

					return data_loader
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__=None     ,				lowercase__="gpt2-medium"     ,				lowercase__=10     ,				lowercase__=64     ,				lowercase__=10     ,				lowercase__=False     ,				lowercase__=False     ,				lowercase__=False     ,				):
					"""simple docstring"""
					A     =   "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

					print("Preprocessing {} dataset...".format(lowercase__ ) )
					A     =   time.time()

					if dataset == "SST":
										A     =   ["positive", "negative", "very positive", "very negative", "neutral"]
										A     =   {c: i for i, c in enumerate(lowercase__ )}

										A     =   Discriminator(
										    class_size=len(lowercase__ )     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__ ).to(lowercase__ )

										A     =   torchtext_data.Field()
										A     =   torchtext_data.Field(sequential=lowercase__ )
										A     ,       A     ,       A     =   datasets.SST.splits(
										    lowercase__     ,				lowercase__     ,				fine_grained=lowercase__     ,				train_subtrees=lowercase__     ,				)

										A     =   []
										A     =   []
										for i in trange(len(lowercase__ )     ,				ascii=lowercase__ ):
															A     =   TreebankWordDetokenizer().detokenize(vars(train_data[i] )["text"] )
															A     =   discriminator.tokenizer.encode(lowercase__ )
															A     =   torch.tensor([50_256] + seq     ,				device=lowercase__     ,				dtype=torch.long )
															x.append(lowercase__ )
															y.append(classaidx[vars(train_data[i] )["label"]] )
										A     =   Dataset(lowercase__     ,				lowercase__ )

										A     =   []
										A     =   []
										for i in trange(len(lowercase__ )     ,				ascii=lowercase__ ):
															A     =   TreebankWordDetokenizer().detokenize(vars(test_data[i] )["text"] )
															A     =   discriminator.tokenizer.encode(lowercase__ )
															A     =   torch.tensor([50_256] + seq     ,				device=lowercase__     ,				dtype=torch.long )
															test_x.append(lowercase__ )
															test_y.append(classaidx[vars(test_data[i] )["label"]] )
										A     =   Dataset(lowercase__     ,				lowercase__ )

										A     =   {
										    "class_size": len(lowercase__ ),
										    "embed_size": discriminator.embed_size,
										    "pretrained_model": pretrained_model,
										    "class_vocab": classaidx,
										    "default_class": 2,
										}

					elif dataset == "clickbait":
										A     =   ["non_clickbait", "clickbait"]
										A     =   {c: i for i, c in enumerate(lowercase__ )}

										A     =   Discriminator(
										    class_size=len(lowercase__ )     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__ ).to(lowercase__ )

										with open("datasets/clickbait/clickbait_train_prefix.txt" ) as f:
															A     =   []
															for i, line in enumerate(lowercase__ ):
																				try:
																									data.append(eval(lowercase__ ) )
																				except Exception:
																									print("Error evaluating line {}: {}".format(lowercase__     ,				lowercase__ ) )
																									continue
										A     =   []
										A     =   []
										with open("datasets/clickbait/clickbait_train_prefix.txt" ) as f:
															for i, line in enumerate(tqdm(lowercase__     ,				ascii=lowercase__ ) ):
																				try:
																									A     =   eval(lowercase__ )
																									A     =   discriminator.tokenizer.encode(d["text"] )

																									if len(lowercase__ ) < max_length_seq:
																														A     =   torch.tensor([50_256] + seq     ,				device=lowercase__     ,				dtype=torch.long )
																									else:
																														print("Line {} is longer than maximum length {}".format(lowercase__     ,				lowercase__ ) )
																														continue
																									x.append(lowercase__ )
																									y.append(d["label"] )
																				except Exception:
																									print("Error evaluating / tokenizing line {}, skipping it".format(lowercase__ ) )
																									pass

										A     =   Dataset(lowercase__     ,				lowercase__ )
										A     =   int(0.9 * len(lowercase__ ) )
										A     =   len(lowercase__ ) - train_size
										A     ,       A     =   torch.utils.data.random_split(lowercase__     ,				[train_size, test_size] )

										A     =   {
										    "class_size": len(lowercase__ ),
										    "embed_size": discriminator.embed_size,
										    "pretrained_model": pretrained_model,
										    "class_vocab": classaidx,
										    "default_class": 1,
										}

					elif dataset == "toxic":
										A     =   ["non_toxic", "toxic"]
										A     =   {c: i for i, c in enumerate(lowercase__ )}

										A     =   Discriminator(
										    class_size=len(lowercase__ )     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__ ).to(lowercase__ )

										A     =   []
										A     =   []
										with open("datasets/toxic/toxic_train.txt" ) as f:
															for i, line in enumerate(tqdm(lowercase__     ,				ascii=lowercase__ ) ):
																				try:
																									A     =   eval(lowercase__ )
																									A     =   discriminator.tokenizer.encode(d["text"] )

																									if len(lowercase__ ) < max_length_seq:
																														A     =   torch.tensor([50_256] + seq     ,				device=lowercase__     ,				dtype=torch.long )
																									else:
																														print("Line {} is longer than maximum length {}".format(lowercase__     ,				lowercase__ ) )
																														continue
																									x.append(lowercase__ )
																									y.append(int(np.sum(d["label"] ) > 0 ) )
																				except Exception:
																									print("Error evaluating / tokenizing line {}, skipping it".format(lowercase__ ) )
																									pass

										A     =   Dataset(lowercase__     ,				lowercase__ )
										A     =   int(0.9 * len(lowercase__ ) )
										A     =   len(lowercase__ ) - train_size
										A     ,       A     =   torch.utils.data.random_split(lowercase__     ,				[train_size, test_size] )

										A     =   {
										    "class_size": len(lowercase__ ),
										    "embed_size": discriminator.embed_size,
										    "pretrained_model": pretrained_model,
										    "class_vocab": classaidx,
										    "default_class": 0,
										}

					else:  # if dataset == "generic":
										# This assumes the input dataset is a TSV with the following structure:
										# class \t text

										if dataset_fp is None:
															raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell." )

										A     =   set()
										with open(lowercase__ ) as f:
															A     =   csv.reader(lowercase__     ,				delimiter="\t" )
															for row in tqdm(lowercase__     ,				ascii=lowercase__ ):
																				if row:
																									classes.add(row[0] )

										A     =   sorted(lowercase__ )
										A     =   {c: i for i, c in enumerate(lowercase__ )}

										A     =   Discriminator(
										    class_size=len(lowercase__ )     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__ ).to(lowercase__ )

										A     =   []
										A     =   []
										with open(lowercase__ ) as f:
															A     =   csv.reader(lowercase__     ,				delimiter="\t" )
															for i, row in enumerate(tqdm(lowercase__     ,				ascii=lowercase__ ) ):
																				if row:
																									A     =   row[0]
																									A     =   row[1]

																									try:
																														A     =   discriminator.tokenizer.encode(lowercase__ )
																														if len(lowercase__ ) < max_length_seq:
																																			A     =   torch.tensor([50_256] + seq     ,				device=lowercase__     ,				dtype=torch.long )

																														else:
																																			print("Line {} is longer than maximum length {}".format(lowercase__     ,				lowercase__ ) )
																																			continue

																														x.append(lowercase__ )
																														y.append(classaidx[label] )

																									except Exception:
																														print("Error tokenizing line {}, skipping it".format(lowercase__ ) )
																														pass

										A     =   Dataset(lowercase__     ,				lowercase__ )
										A     =   int(0.9 * len(lowercase__ ) )
										A     =   len(lowercase__ ) - train_size
										A     ,       A     =   torch.utils.data.random_split(lowercase__     ,				[train_size, test_size] )

										A     =   {
										    "class_size": len(lowercase__ ),
										    "embed_size": discriminator.embed_size,
										    "pretrained_model": pretrained_model,
										    "class_vocab": classaidx,
										    "default_class": 0,
										}

					A     =   time.time()
					print("Preprocessed {} data points".format(len(lowercase__ ) + len(lowercase__ ) ) )
					print("Data preprocessing took: {:.3f}s".format(end - start ) )

					if cached:
										print("Building representation cache..." )

										A     =   time.time()

										A     =   get_cached_data_loader(lowercase__     ,				lowercase__     ,				lowercase__     ,				shuffle=lowercase__     ,				device=lowercase__ )

										A     =   get_cached_data_loader(lowercase__     ,				lowercase__     ,				lowercase__     ,				device=lowercase__ )

										A     =   time.time()
										print("Building representation cache took: {:.3f}s".format(end - start ) )

					else:
										A     =   torch.utils.data.DataLoader(
										    dataset=lowercase__     ,				batch_size=lowercase__     ,				shuffle=lowercase__     ,				collate_fn=lowercase__ )
										A     =   torch.utils.data.DataLoader(dataset=lowercase__     ,				batch_size=lowercase__     ,				collate_fn=lowercase__ )

					if save_model:
										with open("{}_classifier_head_meta.json".format(lowercase__ )     ,				"w" ) as meta_file:
															json.dump(lowercase__     ,				lowercase__ )

					A     =   optim.Adam(discriminator.parameters()     ,				lr=0.00_01 )

					for epoch in range(lowercase__ ):
										A     =   time.time()
										print("\nEpoch"     ,				epoch + 1 )

										train_epoch(
										    discriminator=lowercase__     ,				data_loader=lowercase__     ,				optimizer=lowercase__     ,				epoch=lowercase__     ,				log_interval=lowercase__     ,				device=lowercase__     ,				)
										evaluate_performance(data_loader=lowercase__     ,				discriminator=lowercase__     ,				device=lowercase__ )

										A     =   time.time()
										print("Epoch took: {:.3f}s".format(end - start ) )

										print("\nExample prediction" )
										predict(lowercase__     ,				lowercase__     ,				lowercase__     ,				cached=lowercase__     ,				device=lowercase__ )

										if save_model:
															# torch.save(discriminator.state_dict(),
															#           "{}_discriminator_{}.pt".format(
															#               args.dataset, epoch + 1
															#               ))
															torch.save(
															    discriminator.get_classifier().state_dict()     ,				"{}_classifier_head_epoch_{}.pt".format(lowercase__     ,				epoch + 1 )     ,				)


if __name__ == "__main__":
	__A	:       List[Any]        =						argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
	parser.add_argument(
	    '--dataset',
	    type=str,
	    default='SST',
	    choices=('SST', 'clickbait', 'toxic', 'generic'),
	    help=(
	        'dataset to train the discriminator on.'
	        'In case of generic, the dataset is expected'
	        'to be a TSBV file with structure: class \\t text'
	    ),
	)
	parser.add_argument(
	    '--dataset_fp',
	    type=str,
	    default='',
	    help='File path of the dataset to use. Needed only in case of generic datadset',
	)
	parser.add_argument(
	    '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
	)
	parser.add_argument('--epochs', type=int, default=10, metavar='N', help='Number of training epochs')
	parser.add_argument(
	    '--batch_size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)'
	)
	parser.add_argument(
	    '--log_interval',
	    type=int,
	    default=10,
	    metavar='N',
	    help='how many batches to wait before logging training status',
	)
	parser.add_argument('--save_model', action='store_true', help='whether to save the model')
	parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
	parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
	__A	:       Dict        =						parser.parse_args()

	train_discriminator(**(vars(args)))








"""simple docstring"""




import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin





class      __UpperCamelCase					(  _A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       SpeechaTextaTokenizer
		SCREAMING_SNAKE_CASE														=       False


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							super().setUp()

							A     =   "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" ")
							A     =   ["he re</w> 123", "here a 1456"]
							A     =   dict(zip(__SCREAMING_SNAKE_CASE	,	range(len(__SCREAMING_SNAKE_CASE))))

							A     =   {"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

							A     =   tempfile.mkdtemp()
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["vocab_file"])
							A     =   os.path.join(self.tmpdirname	,	VOCAB_FILES_NAMES["merges_file"])

							with open(self.vocab_file	,	"w"	,	encoding="utf-8") as fp:
												fp.write(json.dumps(__SCREAMING_SNAKE_CASE) + "\n")

							with open(self.merges_file	,	"w") as fp:
												fp.write("\n".join(__SCREAMING_SNAKE_CASE))


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   list(self.get_tokenizer().get_vocab().keys())

							self.assertEqual(vocab_keys[0]	,	"<s>")
							self.assertEqual(vocab_keys[1]	,	"<pad>")
							self.assertEqual(vocab_keys[-1]	,	"vocab")
							self.assertEqual(len(__SCREAMING_SNAKE_CASE)	,	1_4)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							self.assertEqual(self.get_tokenizer().vocab_size	,	1_4)


		def    SCREAMING_SNAKE_CASE__  (self					:						Union[str, Any]):
							A     =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

							# make sure @@ is correctly concatenated
							A     =   [4, 6, 8, 7, 1_0]  # ["here@@", "couple", "words", "of@@", "the"]
							A     =   tokenizer.decode(__SCREAMING_SNAKE_CASE)

							self.assertTrue(output_string == "herecouple words ofthe")


		def    SCREAMING_SNAKE_CASE__  (self					:						str):
							A     =   SpeechaTextaTokenizer.from_pretrained(self.tmpdirname)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
												os.remove(os.path.join(__SCREAMING_SNAKE_CASE	,	"merges.txt"))

												# load tokenizer without merges file should not throw an error
												A     =   SpeechaTextaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
												A     =   SpeechaTextaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)

							self.assertIsNotNone(__SCREAMING_SNAKE_CASE)


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							if not self.test_slow_tokenizer:
												return

							A     =   inspect.signature(self.tokenizer_class.__init__)
							A     =   self.get_tokenizer()

							for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																	self.assertIn(__SCREAMING_SNAKE_CASE	,	tokenizer.init_kwargs)





