code: 
  
  
  # 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)
  lowerCAmelCase					 =	1E-10
  lowerCAmelCase					 =	'''This is incredible! I love it, this is the best chicken I have ever had.'''
  lowerCAmelCase					 =	1_0_0
  
  
  
  
  
  class    A					(     nn.Module				):
  
  
  							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase="gpt2-medium"   ,       lowerCAmelCase=False   ,       lowerCAmelCase="cpu"      ):
  									super().__init__()
  									__lowercase=    GPTaTokenizer.from_pretrained(lowerCAmelCase      )
  									__lowercase=    GPTaLMHeadModel.from_pretrained(lowerCAmelCase      )
  									__lowercase=    self.encoder.transformer.config.hidden_size
  									__lowercase=    ClassificationHead(class_size=lowerCAmelCase   ,       embed_size=self.embed_size      )
  									__lowercase=    cached_mode
  									__lowercase=    device
  
  
  							def     _A (self      ):
  									return self.classifier_head
  
  
  							def     _A (self      ):
  									for param in self.encoder.parameters():
  											__lowercase=    False
  									self.classifier_head.train()
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									__lowercase=    x.ne(0      ).unsqueeze(2      ).repeat(1   ,       1   ,       self.embed_size      ).float().to(self.device      ).detach()
  									__lowercase=    self.encoder.transformer(lowerCAmelCase      )['last_hidden_state']
  									__lowercase=    hidden * mask
  									__lowercase=    torch.sum(lowerCAmelCase   ,       dim=1      ) / (torch.sum(lowerCAmelCase   ,       dim=1      ).detach() + EPSILON)
  									return avg_hidden
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  									if self.cached_mode:
  											__lowercase=    x.to(self.device      )
  									else:
  											__lowercase=    self.avg_representation(x.to(self.device      )      )
  
  									__lowercase=    self.classifier_head(lowerCAmelCase      )
  									__lowercase=    nn.functional.log_softmax(lowerCAmelCase   ,       dim=-1      )
  
  									return probs
  
  
  
  
  
  class    A					(     data.Dataset				):
  
  
  							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    X
  									__lowercase=    y
  
  
  							def __len__(self      ):
  									return len(self.X      )
  
  
  
  
  
  
  
  							def __getitem__(self   ,       lowerCAmelCase      ):
  									__lowercase=    {}
  									__lowercase=    self.X[index]
  									__lowercase=    self.y[index]
  									return data
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Dict:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def pad_sequences(lowercase__			):
  				__lowercase=    [len(lowercase__			) for seq in sequences]
  
  				__lowercase=    torch.zeros(len(lowercase__			)     ,				max(lowercase__			)			).long()  # padding value = 0
  
  				for i, seq in enumerate(lowercase__			):
  						__lowercase=    lengths[i]
  						__lowercase=    seq[:end]
  
  				return padded_sequences, lengths
  
  		__lowercase=    {}
  		for key in data[0].keys():
  				__lowercase=    [d[key] for d in data]
  
  		__lowercase,							__lowercase=    pad_sequences(item_info['X']			)
  		__lowercase=    torch.tensor(item_info['y']     ,				dtype=torch.long			)
  
  		return x_batch, y_batch
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Dict:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    {}
  		for key in data[0].keys():
  				__lowercase=    [d[key] for d in data]
  
  		__lowercase=    torch.cat(item_info['X']     ,				0			)
  		__lowercase=    torch.tensor(item_info['y']     ,				dtype=torch.long			)
  
  		return x_batch, y_batch
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=0     ,				lowercase__=1_0     ,				lowercase__="cpu"			)		->			int:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    0
  		discriminator.train_custom()
  		for batch_idx, (input_t, target_t) in enumerate(lowercase__			):
  				__lowercase,							__lowercase=    input_t.to(lowercase__			), target_t.to(lowercase__			)
  
  				optimizer.zero_grad()
  
  				__lowercase=    discriminator(lowercase__			)
  				__lowercase=    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			)     ,				1_0_0 * samples_so_far / len(data_loader.dataset			)     ,				loss.item()     ,				)			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__="cpu"			)		->			Dict:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		discriminator.eval()
  		__lowercase=    0
  		__lowercase=    0
  		with torch.no_grad():
  				for input_t, target_t in data_loader:
  						__lowercase,							__lowercase=    input_t.to(lowercase__			), target_t.to(lowercase__			)
  						__lowercase=    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
  						__lowercase=    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			)     ,				100.0 * correct / len(data_loader.dataset			)			)			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=False     ,				lowercase__="cpu"			)		->			List[str]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    model.tokenizer.encode(lowercase__			)
  		__lowercase=    torch.tensor([input_t]     ,				dtype=torch.long     ,				device=lowercase__			)
  		if cached:
  				__lowercase=    model.avg_representation(lowercase__			)
  
  		__lowercase=    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  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=False     ,				lowercase__="cpu"			)		->			Union[str, Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    torch.utils.data.DataLoader(dataset=lowercase__     ,				batch_size=lowercase__     ,				collate_fn=lowercase__			)
  
  		__lowercase=    []
  		__lowercase=    []
  		for batch_idx, (x, y) in enumerate(tqdm(lowercase__     ,				ascii=lowercase__			)			):
  				with torch.no_grad():
  						__lowercase=    x.to(lowercase__			)
  						__lowercase=    discriminator.avg_representation(lowercase__			).cpu().detach()
  						__lowercase=    torch.unbind(avg_rep.unsqueeze(1			)			)
  						xs += avg_rep_list
  						ys += y.cpu().numpy().tolist()
  
  		__lowercase=    torch.utils.data.DataLoader(
  		    dataset=Dataset(lowercase__     ,				lowercase__			)     ,				batch_size=lowercase__     ,				shuffle=lowercase__     ,				collate_fn=lowercase__			)
  
  		return data_loader
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__=None     ,				lowercase__="gpt2-medium"     ,				lowercase__=1_0     ,				lowercase__=6_4     ,				lowercase__=1_0     ,				lowercase__=False     ,				lowercase__=False     ,				lowercase__=False     ,				)		->			Dict:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'
  
  		print('Preprocessing {} dataset...'.format(lowercase__			)			)
  		__lowercase=    time.time()
  
  		if dataset == "SST":
  				__lowercase=    ['positive', 'negative', 'very positive', 'very negative', 'neutral']
  				__lowercase=    {c: i for i, c in enumerate(lowercase__			)}
  
  				__lowercase=    Discriminator(
  				    class_size=len(lowercase__			)     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__			).to(lowercase__			)
  
  				__lowercase=    torchtext_data.Field()
  				__lowercase=    torchtext_data.Field(sequential=lowercase__			)
  				__lowercase,							__lowercase,							__lowercase=    datasets.SST.splits(
  				    lowercase__     ,				lowercase__     ,				fine_grained=lowercase__     ,				train_subtrees=lowercase__     ,				)
  
  				__lowercase=    []
  				__lowercase=    []
  				for i in trange(len(lowercase__			)     ,				ascii=lowercase__			):
  						__lowercase=    TreebankWordDetokenizer().detokenize(vars(train_data[i]			)['text']			)
  						__lowercase=    discriminator.tokenizer.encode(lowercase__			)
  						__lowercase=    torch.tensor([5_0_2_5_6] + seq     ,				device=lowercase__     ,				dtype=torch.long			)
  						x.append(lowercase__			)
  						y.append(classaidx[vars(train_data[i]			)['label']]			)
  				__lowercase=    Dataset(lowercase__     ,				lowercase__			)
  
  				__lowercase=    []
  				__lowercase=    []
  				for i in trange(len(lowercase__			)     ,				ascii=lowercase__			):
  						__lowercase=    TreebankWordDetokenizer().detokenize(vars(test_data[i]			)['text']			)
  						__lowercase=    discriminator.tokenizer.encode(lowercase__			)
  						__lowercase=    torch.tensor([5_0_2_5_6] + seq     ,				device=lowercase__     ,				dtype=torch.long			)
  						test_x.append(lowercase__			)
  						test_y.append(classaidx[vars(test_data[i]			)['label']]			)
  				__lowercase=    Dataset(lowercase__     ,				lowercase__			)
  
  				__lowercase=    {
  				    'class_size': len(lowercase__			),
  				    'embed_size': discriminator.embed_size,
  				    'pretrained_model': pretrained_model,
  				    'class_vocab': classaidx,
  				    'default_class': 2,
  				}
  
  		elif dataset == "clickbait":
  				__lowercase=    ['non_clickbait', 'clickbait']
  				__lowercase=    {c: i for i, c in enumerate(lowercase__			)}
  
  				__lowercase=    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:
  						__lowercase=    []
  						for i, line in enumerate(lowercase__			):
  								try:
  										data.append(eval(lowercase__			)			)
  								except Exception:
  										print('Error evaluating line {}: {}'.format(lowercase__     ,				lowercase__			)			)
  										continue
  				__lowercase=    []
  				__lowercase=    []
  				with open('datasets/clickbait/clickbait_train_prefix.txt'			) as f:
  						for i, line in enumerate(tqdm(lowercase__     ,				ascii=lowercase__			)			):
  								try:
  										__lowercase=    eval(lowercase__			)
  										__lowercase=    discriminator.tokenizer.encode(d['text']			)
  
  										if len(lowercase__			) < max_length_seq:
  												__lowercase=    torch.tensor([5_0_2_5_6] + 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
  
  				__lowercase=    Dataset(lowercase__     ,				lowercase__			)
  				__lowercase=    int(0.9 * len(lowercase__			)			)
  				__lowercase=    len(lowercase__			) - train_size
  				__lowercase,							__lowercase=    torch.utils.data.random_split(lowercase__     ,				[train_size, test_size]			)
  
  				__lowercase=    {
  				    'class_size': len(lowercase__			),
  				    'embed_size': discriminator.embed_size,
  				    'pretrained_model': pretrained_model,
  				    'class_vocab': classaidx,
  				    'default_class': 1,
  				}
  
  		elif dataset == "toxic":
  				__lowercase=    ['non_toxic', 'toxic']
  				__lowercase=    {c: i for i, c in enumerate(lowercase__			)}
  
  				__lowercase=    Discriminator(
  				    class_size=len(lowercase__			)     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__			).to(lowercase__			)
  
  				__lowercase=    []
  				__lowercase=    []
  				with open('datasets/toxic/toxic_train.txt'			) as f:
  						for i, line in enumerate(tqdm(lowercase__     ,				ascii=lowercase__			)			):
  								try:
  										__lowercase=    eval(lowercase__			)
  										__lowercase=    discriminator.tokenizer.encode(d['text']			)
  
  										if len(lowercase__			) < max_length_seq:
  												__lowercase=    torch.tensor([5_0_2_5_6] + 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
  
  				__lowercase=    Dataset(lowercase__     ,				lowercase__			)
  				__lowercase=    int(0.9 * len(lowercase__			)			)
  				__lowercase=    len(lowercase__			) - train_size
  				__lowercase,							__lowercase=    torch.utils.data.random_split(lowercase__     ,				[train_size, test_size]			)
  
  				__lowercase=    {
  				    '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.'			)
  
  				__lowercase=    set()
  				with open(lowercase__			) as f:
  						__lowercase=    csv.reader(lowercase__     ,				delimiter='\t'			)
  						for row in tqdm(lowercase__     ,				ascii=lowercase__			):
  								if row:
  										classes.add(row[0]			)
  
  				__lowercase=    sorted(lowercase__			)
  				__lowercase=    {c: i for i, c in enumerate(lowercase__			)}
  
  				__lowercase=    Discriminator(
  				    class_size=len(lowercase__			)     ,				pretrained_model=lowercase__     ,				cached_mode=lowercase__     ,				device=lowercase__			).to(lowercase__			)
  
  				__lowercase=    []
  				__lowercase=    []
  				with open(lowercase__			) as f:
  						__lowercase=    csv.reader(lowercase__     ,				delimiter='\t'			)
  						for i, row in enumerate(tqdm(lowercase__     ,				ascii=lowercase__			)			):
  								if row:
  										__lowercase=    row[0]
  										__lowercase=    row[1]
  
  										try:
  												__lowercase=    discriminator.tokenizer.encode(lowercase__			)
  												if len(lowercase__			) < max_length_seq:
  														__lowercase=    torch.tensor([5_0_2_5_6] + 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
  
  				__lowercase=    Dataset(lowercase__     ,				lowercase__			)
  				__lowercase=    int(0.9 * len(lowercase__			)			)
  				__lowercase=    len(lowercase__			) - train_size
  				__lowercase,							__lowercase=    torch.utils.data.random_split(lowercase__     ,				[train_size, test_size]			)
  
  				__lowercase=    {
  				    'class_size': len(lowercase__			),
  				    'embed_size': discriminator.embed_size,
  				    'pretrained_model': pretrained_model,
  				    'class_vocab': classaidx,
  				    'default_class': 0,
  				}
  
  		__lowercase=    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...'			)
  
  				__lowercase=    time.time()
  
  				__lowercase=    get_cached_data_loader(lowercase__     ,				lowercase__     ,				lowercase__     ,				shuffle=lowercase__     ,				device=lowercase__			)
  
  				__lowercase=    get_cached_data_loader(lowercase__     ,				lowercase__     ,				lowercase__     ,				device=lowercase__			)
  
  				__lowercase=    time.time()
  				print('Building representation cache took: {:.3f}s'.format(end - start			)			)
  
  		else:
  				__lowercase=    torch.utils.data.DataLoader(
  				    dataset=lowercase__     ,				batch_size=lowercase__     ,				shuffle=lowercase__     ,				collate_fn=lowercase__			)
  				__lowercase=    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__			)
  
  		__lowercase=    optim.Adam(discriminator.parameters()     ,				lr=0.0001			)
  
  		for epoch in range(lowercase__			):
  				__lowercase=    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__			)
  
  				__lowercase=    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__":
  	lowerCAmelCase					 =	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=1_0, metavar='''N''', help='''Number of training epochs''')
  	parser.add_argument(
  	    '''--batch_size''', type=int, default=6_4, metavar='''N''', help='''input batch size for training (default: 64)'''
  	)
  	parser.add_argument(
  	    '''--log_interval''',
  	    type=int,
  	    default=1_0,
  	    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''')
  	lowerCAmelCase					 =	parser.parse_args()
  
  	train_discriminator(**(vars(args)))
  
  
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  	import tensorflow as tf
  
  	from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  
  
  
  @require_tf
  class    A					:
  							UpperCamelCase_     :							int =MarianConfig
  							UpperCamelCase_     :							List[Any] ={}
  							UpperCamelCase_     :							Optional[int] ='''gelu'''
  
  
  							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase=1_3   ,       lowerCAmelCase=7   ,       lowerCAmelCase=True   ,       lowerCAmelCase=False   ,       lowerCAmelCase=9_9   ,       lowerCAmelCase=3_2   ,       lowerCAmelCase=2   ,       lowerCAmelCase=4   ,       lowerCAmelCase=3_7   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=2_0   ,       lowerCAmelCase=2   ,       lowerCAmelCase=1   ,       lowerCAmelCase=0   ,       ):
  									__lowercase=    parent
  									__lowercase=    batch_size
  									__lowercase=    seq_length
  									__lowercase=    is_training
  									__lowercase=    use_labels
  									__lowercase=    vocab_size
  									__lowercase=    hidden_size
  									__lowercase=    num_hidden_layers
  									__lowercase=    num_attention_heads
  									__lowercase=    intermediate_size
  
  									__lowercase=    hidden_dropout_prob
  									__lowercase=    attention_probs_dropout_prob
  									__lowercase=    max_position_embeddings
  									__lowercase=    eos_token_id
  									__lowercase=    pad_token_id
  									__lowercase=    bos_token_id
  
  
  							def     _A (self      ):
  									__lowercase=    ids_tensor([self.batch_size, self.seq_length - 1]   ,       self.vocab_size      )
  									__lowercase=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )   ,       1      )
  									__lowercase=    tf.concat([input_ids, eos_tensor]   ,       axis=1      )
  
  									__lowercase=    ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size      )
  
  									__lowercase=    self.config_cls(
  									    vocab_size=self.vocab_size   ,       d_model=self.hidden_size   ,       encoder_layers=self.num_hidden_layers   ,       decoder_layers=self.num_hidden_layers   ,       encoder_attention_heads=self.num_attention_heads   ,       decoder_attention_heads=self.num_attention_heads   ,       encoder_ffn_dim=self.intermediate_size   ,       decoder_ffn_dim=self.intermediate_size   ,       dropout=self.hidden_dropout_prob   ,       attention_dropout=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       eos_token_ids=[2]   ,       bos_token_id=self.bos_token_id   ,       pad_token_id=self.pad_token_id   ,       decoder_start_token_id=self.pad_token_id   ,       **self.config_updates   ,       )
  									__lowercase=    prepare_marian_inputs_dict(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									return config, inputs_dict
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    TFMarianModel(config=lowerCAmelCase      ).get_decoder()
  									__lowercase=    inputs_dict['input_ids']
  
  									__lowercase=    input_ids[:1, :]
  									__lowercase=    inputs_dict['attention_mask'][:1, :]
  									__lowercase=    inputs_dict['head_mask']
  									__lowercase=    1
  
  									# first forward pass
  									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       head_mask=lowerCAmelCase   ,       use_cache=lowerCAmelCase      )
  
  									__lowercase,							__lowercase=    outputs.to_tuple()
  
  									# create hypothetical next token and extent to next_input_ids
  									__lowercase=    ids_tensor((self.batch_size, 3)   ,       config.vocab_size      )
  									__lowercase=    tf.cast(ids_tensor((self.batch_size, 3)   ,       2      )   ,       tf.inta      )
  
  									# append to next input_ids and
  									__lowercase=    tf.concat([input_ids, next_tokens]   ,       axis=-1      )
  									__lowercase=    tf.concat([attention_mask, next_attn_mask]   ,       axis=-1      )
  
  									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase      )[0]
  									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       past_key_values=lowerCAmelCase      )[0]
  
  									self.parent.assertEqual(next_tokens.shape[1]   ,       output_from_past.shape[1]      )
  
  									# select random slice
  									__lowercase=    int(ids_tensor((1,)   ,       output_from_past.shape[-1]      )      )
  									__lowercase=    output_from_no_past[:, -3:, random_slice_idx]
  									__lowercase=    output_from_past[:, :, random_slice_idx]
  
  									# test that outputs are equal for slice
  									tf.debugging.assert_near(lowerCAmelCase   ,       lowerCAmelCase   ,       rtol=1E-3      )
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				)		->			Any:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if attention_mask is None:
  				__lowercase=    tf.cast(tf.math.not_equal(lowercase__     ,				config.pad_token_id			)     ,				tf.inta			)
  		if decoder_attention_mask is None:
  				__lowercase=    tf.concat(
  				    [
  				        tf.ones(decoder_input_ids[:, :1].shape     ,				dtype=tf.inta			),
  				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,				config.pad_token_id			)     ,				tf.inta			),
  				    ]     ,				axis=-1     ,				)
  		if head_mask is None:
  				__lowercase=    tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
  		if decoder_head_mask is None:
  				__lowercase=    tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
  		if cross_attn_head_mask is None:
  				__lowercase=    tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
  		return {
  		    "input_ids": input_ids,
  		    "decoder_input_ids": decoder_input_ids,
  		    "attention_mask": attention_mask,
  		    "decoder_attention_mask": decoder_attention_mask,
  		    "head_mask": head_mask,
  		    "decoder_head_mask": decoder_head_mask,
  		    "cross_attn_head_mask": cross_attn_head_mask,
  		}
  
  
  
  
  
  @require_tf
  class    A					(     A_      ,       A_      ,       unittest.TestCase				):
  							UpperCamelCase_     :							str =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  							UpperCamelCase_     :							Union[str, Any] =(TFMarianMTModel,) if is_tf_available() else ()
  							UpperCamelCase_     :							List[str] =(
  							    {
  							        '''conversational''': TFMarianMTModel,
  							        '''feature-extraction''': TFMarianModel,
  							        '''summarization''': TFMarianMTModel,
  							        '''text2text-generation''': TFMarianMTModel,
  							        '''translation''': TFMarianMTModel,
  							    }
  							    if is_tf_available()
  							    else {}
  							)
  							UpperCamelCase_     :							str =True
  							UpperCamelCase_     :							List[str] =False
  							UpperCamelCase_     :							Optional[int] =False
  
  
  							def     _A (self      ):
  									__lowercase=    TFMarianModelTester(self      )
  									__lowercase=    ConfigTester(self   ,       config_class=lowerCAmelCase      )
  
  
  							def     _A (self      ):
  									self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  							def     _A (self      ):
  									__lowercase=    self.model_tester.prepare_config_and_inputs_for_common()
  									self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase      )
  
  
  
  
  
  @require_tf
  class    A					(     unittest.TestCase				):
  							UpperCamelCase_     :							Tuple =1_000  # show more chars for failing integration tests
  
  
  							@classmethod
  							def     _A (cls      ):
  									__lowercase=    f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
  									return cls
  
  
  							@cached_property
  							def     _A (self      ):
  									return AutoTokenizer.from_pretrained(self.model_name      )
  
  
  							@property
  							def     _A (self      ):
  									return self.tokenizer.eos_token_id
  
  
  							@cached_property
  							def     _A (self      ):
  									warnings.simplefilter('error'      )
  									__lowercase=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
  									assert isinstance(lowerCAmelCase   ,       lowerCAmelCase      )
  									__lowercase=    model.config
  									self.assertListEqual(c.bad_words_ids   ,       [[c.pad_token_id]]      )
  									self.assertEqual(c.max_length   ,       5_1_2      )
  									self.assertEqual(c.decoder_start_token_id   ,       c.pad_token_id      )
  									return model
  
  
  							def     _A (self   ,       **lowerCAmelCase      ):
  									__lowercase=    self.translate_src_text(**lowerCAmelCase      )
  									self.assertListEqual(self.expected_text   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
  							def     _A (self   ,       **lowerCAmelCase      ):
  									__lowercase=    self.tokenizer(self.src_text   ,       **lowerCAmelCase   ,       padding=lowerCAmelCase   ,       return_tensors='tf'      )
  									__lowercase=    self.model.generate(
  									    model_inputs.input_ids   ,       attention_mask=model_inputs.attention_mask   ,       num_beams=2   ,       max_length=1_2_8      )
  									__lowercase=    self.tokenizer.batch_decode(generated_ids.numpy()   ,       skip_special_tokens=lowerCAmelCase      )
  									return generated_words
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[str, Any] ='''mt'''
  							UpperCamelCase_     :							Tuple ='''en'''
  							UpperCamelCase_     :							Optional[Any] =['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
  							UpperCamelCase_     :							List[str] =['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    A					(     A_				):
  							UpperCamelCase_     :							List[Any] ='''en'''
  							UpperCamelCase_     :							str ='''zh'''
  							UpperCamelCase_     :							Dict =['''My name is Wolfgang and I live in Berlin''']
  							UpperCamelCase_     :							Optional[int] =['''我叫沃尔夫冈 我住在柏林''']
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    A					(     A_				):
  							UpperCamelCase_     :							Tuple ='''en'''
  							UpperCamelCase_     :							List[Any] ='''ROMANCE'''
  							UpperCamelCase_     :							int =[
  							    '''>>fr<< Don\'t spend so much time watching TV.''',
  							    '''>>pt<< Your message has been sent.''',
  							    '''>>es<< He\'s two years older than me.''',
  							]
  							UpperCamelCase_     :							str =[
  							    '''Ne passez pas autant de temps à regarder la télé.''',
  							    '''A sua mensagem foi enviada.''',
  							    '''Es dos años más viejo que yo.''',
  							]
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									__lowercase=    TranslationPipeline(self.model   ,       self.tokenizer   ,       framework='tf'      )
  									__lowercase=    pipeline(self.src_text      )
  									self.assertEqual(self.expected_text   ,       [x['translation_text'] for x in output]      )
  
  
  
  
  
  
  