




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
UpperCamelCase__    			=			logging.getLogger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Any      ,      SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
					__lowerCAmelCase				   =       np.argmax(SCREAMING_SNAKE_CASE_      ,      axis=1     )
					return np.sum(outputs == labels     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					with open(SCREAMING_SNAKE_CASE_      ,      encoding="utf_8"     ) as f:
										__lowerCAmelCase				   =       csv.reader(SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       []
										next(SCREAMING_SNAKE_CASE_     )  # skip the first line
										for line in tqdm(SCREAMING_SNAKE_CASE_     ):
															output.append((" ".join(line[1:5]     ), line[5], line[6], int(line[-1]     ) - 1)     )
					return output







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       []
					for dataset in encoded_datasets:
										__lowerCAmelCase				   =       len(SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       np.zeros((n_batch, 2, input_len)      ,      dtype=np.intaa     )
										__lowerCAmelCase				   =       np.zeros((n_batch, 2)      ,      dtype=np.intaa     )
										__lowerCAmelCase				   =       np.full((n_batch, 2, input_len)      ,      fill_value=-1_00      ,      dtype=np.intaa     )
										__lowerCAmelCase				   =       np.zeros((n_batch,)      ,      dtype=np.intaa     )
										for (
										    i,
										    (story, conta, conta, mc_label),
										) in enumerate(SCREAMING_SNAKE_CASE_     ):
															__lowerCAmelCase				   =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															__lowerCAmelCase				   =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															__lowerCAmelCase				   =       with_conta
															__lowerCAmelCase				   =       with_conta
															__lowerCAmelCase				   =       len(SCREAMING_SNAKE_CASE_     ) - 1
															__lowerCAmelCase				   =       len(SCREAMING_SNAKE_CASE_     ) - 1
															__lowerCAmelCase				   =       with_conta
															__lowerCAmelCase				   =       with_conta
															__lowerCAmelCase				   =       mc_label
										__lowerCAmelCase				   =       (input_ids, mc_token_ids, lm_labels, mc_labels)
										tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE_     ) for t in all_inputs     )     )
					return tensor_datasets







def 	_a      (						):
					__lowerCAmelCase				   =       argparse.ArgumentParser()
					parser.add_argument("--model_name"      ,      type=SCREAMING_SNAKE_CASE_      ,      default="openai-gpt"      ,      help="pretrained model name"     )
					parser.add_argument("--do_train"      ,      action="store_true"      ,      help="Whether to run training."     )
					parser.add_argument("--do_eval"      ,      action="store_true"      ,      help="Whether to run eval on the dev set."     )
					parser.add_argument(
					    "--output_dir"      ,      default=SCREAMING_SNAKE_CASE_      ,      type=SCREAMING_SNAKE_CASE_      ,      required=SCREAMING_SNAKE_CASE_      ,      help="The output directory where the model predictions and checkpoints will be written."      ,      )
					parser.add_argument("--train_dataset"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""     )
					parser.add_argument("--eval_dataset"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""     )
					parser.add_argument("--seed"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=42     )
					parser.add_argument("--num_train_epochs"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=3     )
					parser.add_argument("--train_batch_size"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=8     )
					parser.add_argument("--eval_batch_size"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=16     )
					parser.add_argument("--adam_epsilon"      ,      default=1E-8      ,      type=SCREAMING_SNAKE_CASE_      ,      help="Epsilon for Adam optimizer."     )
					parser.add_argument("--max_grad_norm"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=1     )
					parser.add_argument(
					    "--max_steps"      ,      default=-1      ,      type=SCREAMING_SNAKE_CASE_      ,      help=(
					        "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
					    )      ,      )
					parser.add_argument(
					    "--gradient_accumulation_steps"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=1      ,      help="Number of updates steps to accumulate before                        performing a backward/update pass."      ,      )
					parser.add_argument("--learning_rate"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=6.25E-5     )
					parser.add_argument("--warmup_steps"      ,      default=0      ,      type=SCREAMING_SNAKE_CASE_      ,      help="Linear warmup over warmup_steps."     )
					parser.add_argument("--lr_schedule"      ,      type=SCREAMING_SNAKE_CASE_      ,      default="warmup_linear"     )
					parser.add_argument("--weight_decay"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=0.01     )
					parser.add_argument("--lm_coef"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=0.9     )
					parser.add_argument("--n_valid"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=3_74     )

					parser.add_argument("--server_ip"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""      ,      help="Can be used for distant debugging."     )
					parser.add_argument("--server_port"      ,      type=SCREAMING_SNAKE_CASE_      ,      default=""      ,      help="Can be used for distant debugging."     )
					__lowerCAmelCase				   =       parser.parse_args()
					print(SCREAMING_SNAKE_CASE_     )

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print("Waiting for debugger attach"     )
										ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,      redirect_output=SCREAMING_SNAKE_CASE_     )
										ptvsd.wait_for_attach()

					random.seed(args.seed     )
					np.random.seed(args.seed     )
					torch.manual_seed(args.seed     )
					torch.cuda.manual_seed_all(args.seed     )

					__lowerCAmelCase				   =       torch.device("cuda" if torch.cuda.is_available() else "cpu"     )
					__lowerCAmelCase				   =       torch.cuda.device_count()
					logger.info("device: {}, n_gpu {}".format(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )     )

					if not args.do_train and not args.do_eval:
										raise ValueError("At least one of `do_train` or `do_eval` must be True."     )

					if not os.path.exists(args.output_dir     ):
										os.makedirs(args.output_dir     )

					# Load tokenizer and model
					# This loading functions also add new tokens and embeddings called `special tokens`
					# These new embeddings will be fine-tuned on the RocStories dataset
					__lowerCAmelCase				   =       ["_start_", "_delimiter_", "_classify_"]
					__lowerCAmelCase				   =       OpenAIGPTTokenizer.from_pretrained(args.model_name     )
					tokenizer.add_tokens(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name     )
					model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_     )     )
					model.to(SCREAMING_SNAKE_CASE_     )

					# Load and encode the datasets
					def tokenize_and_encode(SCREAMING_SNAKE_CASE_							:				Union[str, Any]     ):
										if isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
															return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE_     )     )
										elif isinstance(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     ):
															return obj
										return [tokenize_and_encode(SCREAMING_SNAKE_CASE_     ) for o in obj]

					logger.info("Encoding dataset..."     )
					__lowerCAmelCase				   =       load_rocstories_dataset(args.train_dataset     )
					__lowerCAmelCase				   =       load_rocstories_dataset(args.eval_dataset     )
					__lowerCAmelCase				   =       (train_dataset, eval_dataset)
					__lowerCAmelCase				   =       tokenize_and_encode(SCREAMING_SNAKE_CASE_     )

					# Compute the max input length for the Transformer
					__lowerCAmelCase				   =       model.config.n_positions // 2 - 2
					__lowerCAmelCase				   =       max(
					    len(story[:max_length]     ) + max(len(conta[:max_length]     )      ,      len(conta[:max_length]     )     ) + 3
					    for dataset in encoded_datasets
					    for story, conta, conta, _ in dataset     )
					__lowerCAmelCase				   =       min(SCREAMING_SNAKE_CASE_      ,      model.config.n_positions     )  # Max size of input for the pre-trained model

					# Prepare inputs tensors and dataloaders
					__lowerCAmelCase				   =       pre_process_datasets(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      *SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase   ,							__lowerCAmelCase				   =       tensor_datasets[0], tensor_datasets[1]

					__lowerCAmelCase				   =       TensorDataset(*SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       RandomSampler(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       DataLoader(SCREAMING_SNAKE_CASE_      ,      sampler=SCREAMING_SNAKE_CASE_      ,      batch_size=args.train_batch_size     )

					__lowerCAmelCase				   =       TensorDataset(*SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       SequentialSampler(SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       DataLoader(SCREAMING_SNAKE_CASE_      ,      sampler=SCREAMING_SNAKE_CASE_      ,      batch_size=args.eval_batch_size     )

					# Prepare optimizer
					if args.do_train:
										if args.max_steps > 0:
															__lowerCAmelCase				   =       args.max_steps
															__lowerCAmelCase				   =       args.max_steps // (len(SCREAMING_SNAKE_CASE_     ) // args.gradient_accumulation_steps) + 1
										else:
															__lowerCAmelCase				   =       len(SCREAMING_SNAKE_CASE_     ) // args.gradient_accumulation_steps * args.num_train_epochs

										__lowerCAmelCase				   =       list(model.named_parameters()     )
										__lowerCAmelCase				   =       ["bias", "LayerNorm.bias", "LayerNorm.weight"]
										__lowerCAmelCase				   =       [
										    {
										        "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay     )],
										        "weight_decay": args.weight_decay,
										    },
										    {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay     )], "weight_decay": 0.0},
										]
										__lowerCAmelCase				   =       AdamW(SCREAMING_SNAKE_CASE_      ,      lr=args.learning_rate      ,      eps=args.adam_epsilon     )
										__lowerCAmelCase				   =       get_linear_schedule_with_warmup(
										    SCREAMING_SNAKE_CASE_      ,      num_warmup_steps=args.warmup_steps      ,      num_training_steps=SCREAMING_SNAKE_CASE_     )

					if args.do_train:
										__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       0, 0, None
										model.train()
										for _ in trange(int(args.num_train_epochs     )      ,      desc="Epoch"     ):
															__lowerCAmelCase				   =       0
															__lowerCAmelCase				   =       0
															__lowerCAmelCase				   =       tqdm(SCREAMING_SNAKE_CASE_      ,      desc="Training"     )
															for step, batch in enumerate(SCREAMING_SNAKE_CASE_     ):
																				__lowerCAmelCase				   =       tuple(t.to(SCREAMING_SNAKE_CASE_     ) for t in batch     )
																				__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       batch
																				__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_      ,      mc_token_ids=SCREAMING_SNAKE_CASE_      ,      lm_labels=SCREAMING_SNAKE_CASE_      ,      mc_labels=SCREAMING_SNAKE_CASE_     )
																				__lowerCAmelCase				   =       args.lm_coef * losses[0] + losses[1]
																				loss.backward()
																				optimizer.step()
																				scheduler.step()
																				optimizer.zero_grad()
																				tr_loss += loss.item()
																				__lowerCAmelCase				   =       (
																				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																				)
																				nb_tr_steps += 1
																				__lowerCAmelCase				   =       "Training loss: {:.2e} lr: {:.2e}".format(SCREAMING_SNAKE_CASE_      ,      scheduler.get_lr()[0]     )

    # Save a trained model
					if args.do_train:
										# Save a trained model, configuration and tokenizer
										__lowerCAmelCase				   =       model.module if hasattr(SCREAMING_SNAKE_CASE_      ,      "module"     ) else model  # Only save the model itself

										# If we save using the predefined names, we can load using `from_pretrained`
										__lowerCAmelCase				   =       os.path.join(args.output_dir      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       os.path.join(args.output_dir      ,      SCREAMING_SNAKE_CASE_     )

										torch.save(model_to_save.state_dict()      ,      SCREAMING_SNAKE_CASE_     )
										model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE_     )
										tokenizer.save_vocabulary(args.output_dir     )

										# Load a trained model and vocabulary that you have fine-tuned
										__lowerCAmelCase				   =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir     )
										__lowerCAmelCase				   =       OpenAIGPTTokenizer.from_pretrained(args.output_dir     )
										model.to(SCREAMING_SNAKE_CASE_     )

					if args.do_eval:
										model.eval()
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       0, 0
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       0, 0
										for batch in tqdm(SCREAMING_SNAKE_CASE_      ,      desc="Evaluating"     ):
															__lowerCAmelCase				   =       tuple(t.to(SCREAMING_SNAKE_CASE_     ) for t in batch     )
															__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       batch
															with torch.no_grad():
																				__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       model(
																				    SCREAMING_SNAKE_CASE_      ,      mc_token_ids=SCREAMING_SNAKE_CASE_      ,      lm_labels=SCREAMING_SNAKE_CASE_      ,      mc_labels=SCREAMING_SNAKE_CASE_     )

															__lowerCAmelCase				   =       mc_logits.detach().cpu().numpy()
															__lowerCAmelCase				   =       mc_labels.to("cpu"     ).numpy()
															__lowerCAmelCase				   =       accuracy(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )

															eval_loss += mc_loss.mean().item()
															eval_accuracy += tmp_eval_accuracy

															nb_eval_examples += input_ids.size(0     )
															nb_eval_steps += 1

										__lowerCAmelCase				   =       eval_loss / nb_eval_steps
										__lowerCAmelCase				   =       eval_accuracy / nb_eval_examples
										__lowerCAmelCase				   =       tr_loss / nb_tr_steps if args.do_train else None
										__lowerCAmelCase				   =       {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

										__lowerCAmelCase				   =       os.path.join(args.output_dir      ,      "eval_results.txt"     )
										with open(SCREAMING_SNAKE_CASE_      ,      "w"     ) as writer:
															logger.info("***** Eval results *****"     )
															for key in sorted(result.keys()     ):
																				logger.info("  %s = %s"      ,      SCREAMING_SNAKE_CASE_      ,      str(result[key]     )     )
																				writer.write("%s = %s\n" % (key, str(result[key]     ))     )


if __name__ == "__main__":
					main()











import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CTRLForSequenceClassification,
				    CTRLLMHeadModel,
				    CTRLModel,
				)

class 	A_       :
	def __init__(    self							,					_A							,					_A=1_4							,					_A=7							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=True							,					_A=9_9							,					_A=3_2							,					_A=5							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=5_1_2							,					_A=1_6							,					_A=2							,					_A=0.02							,					_A=3							,					_A=4							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				seq_length
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_token_type_ids
				UpperCAmelCase   		=				use_input_mask
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				use_mc_token_ids
				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				max_position_embeddings
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				type_sequence_label_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				num_labels
				UpperCAmelCase   		=				num_choices
				UpperCAmelCase   		=				scope
				UpperCAmelCase   		=				self.vocab_size - 1
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size		)

				UpperCAmelCase   		=				None
				if self.use_input_mask:
							UpperCAmelCase   		=				random_attention_mask([self.batch_size, self.seq_length]		)

				UpperCAmelCase   		=				None
				if self.use_token_type_ids:
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.type_vocab_size		)

				UpperCAmelCase   		=				None
				if self.use_mc_token_ids:
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.num_choices]							,					self.seq_length		)

				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)
							UpperCAmelCase   		=				ids_tensor([self.batch_size, self.seq_length]							,					self.num_labels		)
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.num_choices		)

				UpperCAmelCase   		=				self.get_config()

				UpperCAmelCase   		=				ids_tensor([self.num_hidden_layers, self.num_attention_heads]							,					2		)

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				    token_type_ids,
				    mc_token_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				)
	def      _lowercase				(    self		):






				'''simple docstring'''







				return CTRLConfig(
				    vocab_size=self.vocab_size							,					n_embd=self.hidden_size							,					n_layer=self.num_hidden_layers							,					n_head=self.num_attention_heads							,					n_positions=self.max_position_embeddings							,					pad_token_id=self.pad_token_id							,					)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModel(config=_A		)
				model.to(_A		)
				model.eval()

				model(_A							,					token_type_ids=_A							,					head_mask=_A		)
				model(_A							,					token_type_ids=_A		)
				UpperCAmelCase   		=				model(_A		)
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)		)
				self.parent.assertEqual(len(result.past_key_values		)							,					config.n_layer		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel(_A		)
				model.to(_A		)
				model.eval()

				UpperCAmelCase   		=				model(_A							,					token_type_ids=_A							,					labels=_A		)
				self.parent.assertEqual(result.loss.shape							,					()		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.seq_length, self.vocab_size)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.prepare_config_and_inputs()

				(
				    (
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			(
				    UpperCAmelCase
				)		,			
				)   		=				config_and_inputs

				UpperCAmelCase   		=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

				return config, inputs_dict







	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					*_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.num_labels
				UpperCAmelCase   		=				CTRLForSequenceClassification(_A		)
				model.to(_A		)
				model.eval()
				UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)
				UpperCAmelCase   		=				model(_A							,					token_type_ids=_A							,					labels=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)		)

@require_torch
class 	A_       (a_     ,							a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
	UpperCAmelCase__					=			(CTRLLMHeadModel,) if is_torch_available() else ()
	UpperCAmelCase__					=			(
	    {
	        '''feature-extraction''': CTRLModel,
	        '''text-classification''': CTRLForSequenceClassification,
	        '''text-generation''': CTRLLMHeadModel,
	        '''zero-shot''': CTRLForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	UpperCAmelCase__					=			True
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self							,					_A							,					_A							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
							# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
							# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
							# config could not be created.
							return True

				return False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					n_embd=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_ctrl_model(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_lm_head_model(*_A		)
	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							UpperCAmelCase   		=				CTRLModel.from_pretrained(_A		)
							self.assertIsNotNone(_A		)







	@unittest.skip('''The model doesn\'t support left padding'''		)  # and it's not used enough to be worth fixing :)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass

@require_torch
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self		):






				'''simple docstring'''







				super().tearDown()
				# clean-up as much as possible GPU memory occupied by PyTorch
				gc.collect()
				torch.cuda.empty_cache()







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				CTRLLMHeadModel.from_pretrained('''ctrl'''		)
				model.to(_A		)
				UpperCAmelCase   		=				torch.tensor(
				    [[1_1_8_5_9, 0, 1_6_1_1, 8]]							,					dtype=torch.long							,					device=_A		)  # Legal the president is
				UpperCAmelCase   		=				[
				    1_1_8_5_9,
				    0,
				    1_6_1_1,
				    8,
				    5,
				    1_5_0,
				    2_6_4_4_9,
				    2,
				    1_9,
				    3_4_8,
				    4_6_9,
				    3,
				    2_5_9_5,
				    4_8,
				    2_0_7_4_0,
				    2_4_6_5_3_3,
				    2_4_6_5_3_3,
				    1_9,
				    3_0,
				    5,
				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

				UpperCAmelCase   		=				model.generate(_A							,					do_sample=_A		)
				self.assertListEqual(output_ids[0].tolist()							,					_A		)



