






from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
							from fairscale.optim import OSS


A__	:			Tuple			   = logging.get_logger(__name__)

A__	:			int			   = {
    'linear': get_linear_schedule_with_warmup,
    'cosine': get_cosine_schedule_with_warmup,
    'cosine_w_restarts': get_cosine_with_hard_restarts_schedule_with_warmup,
    'polynomial': get_polynomial_decay_schedule_with_warmup,
    'constant': get_constant_schedule,
    'constant_w_warmup': get_constant_schedule_with_warmup,
}
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				def __init__(     self						:    Optional[int],  lowerCamelCase						:    int=None,  lowerCamelCase						:    int=None,  *lowerCamelCase						:    List[Any],  **lowerCamelCase						:    Any  ):

								'''simple docstring'''
								super().__init__(*lowerCamelCase,  **lowerCamelCase  )

								if config is None:
												assert isinstance(self.model,  lowerCamelCase  ), (
												    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
												    F""" {self.model.__class__}"""
												)
												lowercase__    =			self.model.config
								else:
												lowercase__    =			config

								lowercase__    =			data_args
								lowercase__    =			self.config.tgt_vocab_size if isinstance(self.config,  lowerCamelCase  ) else self.config.vocab_size

								if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
												assert self.config.pad_token_id is not None, (
												    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
												    " calculation or doing label smoothing."
												)

								if self.config.pad_token_id is None and self.config.eos_token_id is not None:
												logger.warning(
												    F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
												    ''' padding..'''  )

								if self.args.label_smoothing == 0:
												lowercase__    =			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id  )
								else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												lowercase__    =			label_smoothed_nll_loss




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								if self.optimizer is None:
												lowercase__    =			['''bias''', '''LayerNorm.weight''']
												lowercase__    =			[
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay  )],
												        '''weight_decay''': self.args.weight_decay,
												    },
												    {
												        '''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay  )],
												        '''weight_decay''': 0.0,
												    },
												]
												lowercase__    =			Adafactor if self.args.adafactor else AdamW
												if self.args.adafactor:
																lowercase__    =			Adafactor
																lowercase__    =			{'''scale_parameter''': False, '''relative_step''': False}
												else:
																lowercase__    =			AdamW
																lowercase__    =			{
																    '''betas''': (self.args.adam_betaa, self.args.adam_betaa),
																    '''eps''': self.args.adam_epsilon,
																}
												lowercase__    =			self.args.learning_rate
												if self.sharded_ddp:
																lowercase__    =			OSS(
																    params=lowerCamelCase,  optim=lowerCamelCase,  **lowerCamelCase,  )
												else:
																lowercase__    =			optimizer_cls(lowerCamelCase,  **lowerCamelCase  )

								if self.lr_scheduler is None:
												lowercase__    =			self._get_lr_scheduler(lowerCamelCase  )
								else:  # ignoring --lr_scheduler
												logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''  )




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
												lowercase__    =			schedule_func(self.optimizer  )
								elif self.args.lr_scheduler == "constant_w_warmup":
												lowercase__    =			schedule_func(self.optimizer,  num_warmup_steps=self.args.warmup_steps  )
								else:
												lowercase__    =			schedule_func(
												    self.optimizer,  num_warmup_steps=self.args.warmup_steps,  num_training_steps=lowerCamelCase  )
								return scheduler




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								if isinstance(self.train_dataset,  torch.utils.data.IterableDataset  ):
												return None
								elif is_torch_tpu_available():
												return get_tpu_sampler(self.train_dataset  )
								else:
												if self.args.sortish_sampler:
																self.train_dataset.make_sortish_sampler(
																    self.args.per_device_train_batch_size,  distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),  )

												return (
												    RandomSampler(self.train_dataset  )
												    if self.args.local_rank == -1
												    else DistributedSampler(self.train_dataset  )
												)




				def 				lowercase__   (     self						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Union[str, Any]  ):

								'''simple docstring'''
								if self.args.label_smoothing == 0:
												if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																# force training to ignore pad token
																lowercase__    =			model(**lowerCamelCase,  use_cache=lowerCamelCase  )[0]
																lowercase__    =			self.loss_fn(logits.view(-1,  logits.shape[-1]  ),  labels.view(-1  )  )
												else:
																# compute usual loss via models
																lowercase__ ,						lowercase__    =			model(**lowerCamelCase,  labels=lowerCamelCase,  use_cache=lowerCamelCase  )[:2]
								else:
												# compute label smoothed loss
												lowercase__    =			model(**lowerCamelCase,  use_cache=lowerCamelCase  )[0]
												lowercase__    =			torch.nn.functional.log_softmax(lowerCamelCase,  dim=-1  )
												lowercase__ ,						lowercase__    =			self.loss_fn(lowerCamelCase,  lowerCamelCase,  self.args.label_smoothing,  ignore_index=self.config.pad_token_id  )
								return loss, logits




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			inputs.pop('''labels'''  )
								lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )
								return loss




				def 				lowercase__   (     self						:    str,  lowerCamelCase						:    nn.Module,  lowerCamelCase						:    Dict[str, Union[torch.Tensor, Any]],  lowerCamelCase						:    bool,  lowerCamelCase						:    Optional[List[str]] = None,  ):

								'''simple docstring'''
								lowercase__    =			self._prepare_inputs(lowerCamelCase  )

								lowercase__    =			{
								    '''max_length''': self.data_args.val_max_target_length
								    if self.data_args is not None
								    else self.config.max_length,
								    '''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
								}

								if self.args.predict_with_generate and not self.args.prediction_loss_only:
												lowercase__    =			self.model.generate(
												    inputs['''input_ids'''],  attention_mask=inputs['''attention_mask'''],  **lowerCamelCase,  )
												# in case the batch is shorter than max length, the output should be padded
												if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

								lowercase__    =			inputs.pop('''labels'''  )
								with torch.no_grad():
												# compute loss on predict data
												lowercase__ ,						lowercase__    =			self._compute_loss(lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

								lowercase__    =			loss.mean().detach()
								if self.args.prediction_loss_only:
												return (loss, None, None)

								lowercase__    =			generated_tokens if self.args.predict_with_generate else logits

								if labels.shape[-1] < gen_kwargs["max_length"]:
												lowercase__    =			self._pad_tensors_to_max_len(lowerCamelCase,  gen_kwargs['''max_length''']  )

								return (loss, logits, labels)




				def 				lowercase__   (     self						:    List[Any],  lowerCamelCase						:    str,  lowerCamelCase						:    Any  ):

								'''simple docstring'''
								# If PAD token is not defined at least EOS token has to be defined
								lowercase__    =			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

								if pad_token_id is None:
												raise ValueError(
												    '''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
												    F""" padded to `max_length`={max_length}"""  )

								lowercase__    =			pad_token_id * torch.ones(
								    (tensor.shape[0], max_length),  dtype=tensor.dtype,  device=tensor.device  )
								lowercase__    =			tensor
								return padded_tensor












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       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str=14,  lowerCamelCase						:    List[str]=7,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    List[str]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    List[str]=99,  lowerCamelCase						:    Optional[Any]=32,  lowerCamelCase						:    Optional[int]=5,  lowerCamelCase						:    List[Any]=4,  lowerCamelCase						:    List[Any]=37,  lowerCamelCase						:    List[str]="gelu",  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    List[Any]=512,  lowerCamelCase						:    List[str]=16,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    Union[str, Any]=0.02,  lowerCamelCase						:    Optional[int]=3,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    List[Any]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_input_mask
								lowercase__    =			use_labels
								lowercase__    =			use_mc_token_ids
								lowercase__    =			vocab_size
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			type_vocab_size
								lowercase__    =			type_sequence_label_size
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			num_choices
								lowercase__    =			scope
								lowercase__    =			self.vocab_size - 1




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

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

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

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

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

								lowercase__    =			self.get_config()

								lowercase__    =			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						:    Optional[int]  ):

								'''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						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Union[str, Any],  *lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			CTRLModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								model(lowerCamelCase,  token_type_ids=lowerCamelCase,  head_mask=lowerCamelCase  )
								model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__    =			model(lowerCamelCase  )
								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						:    Optional[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  *lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						
								)    =			config_and_inputs

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

								return config, inputs_dict




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Any,  *lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			CTRLForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				lowercase__											=       (CTRLLMHeadModel,) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """feature-extraction""": CTRLModel,
				        """text-classification""": CTRLForSequenceClassification,
				        """text-generation""": CTRLLMHeadModel,
				        """zero-shot""": CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=       True
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]  ):

								'''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						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  n_embd=37  )




				def 				lowercase__   (     self						:    Tuple  ):

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




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCamelCase  )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			CTRLModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




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

								'''simple docstring'''
								pass



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''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						:    int  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel.from_pretrained('''ctrl'''  )
								model.to(lowerCamelCase  )
								lowercase__    =			torch.tensor(
								    [[11_859, 0, 1_611, 8]],  dtype=torch.long,  device=lowerCamelCase  )  # Legal the president is
								lowercase__    =			[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								lowercase__    =			model.generate(lowerCamelCase,  do_sample=lowerCamelCase  )
								self.assertListEqual(output_ids[0].tolist(),  lowerCamelCase  )




