
import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin




class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Union[str, Any]				       =DownBlockaD  # noqa F405
	A__    :   Optional[Any]				       ="""down"""
	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= [-0.0_232, -0.9_869, 0.8_054, -0.0_637, -0.1_688, -1.4_264, 0.4_470, -1.3_394, 0.0_904]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =ResnetDownsampleBlockaD  # noqa F405
	A__    :   List[str]				       ="""down"""
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= [0.0_710, 0.2_410, -0.7_320, -1.0_757, -1.1_343, 0.3_540, -0.0_133, -0.2_576, 0.0_948]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =AttnDownBlockaD  # noqa F405
	A__    :   Tuple				       ="""down"""
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= [0.0_636, 0.8_964, -0.6_234, -1.0_131, 0.0_844, 0.4_935, 0.3_437, 0.0_911, -0.2_957]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[Any]				       =CrossAttnDownBlockaD  # noqa F405
	A__    :   Any				       ="""down"""
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= super().prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= 32
						return init_dict, inputs_dict





	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= [0.2_238, -0.7_396, -0.2_255, -0.3_829, 0.1_925, 1.1_665, 0.0_603, -0.7_295, 0.1_983]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =SimpleCrossAttnDownBlockaD  # noqa F405
	A__    :   List[str]				       ="""down"""
	@property
	def      A_				(			self							:   Any							):
						return super().get_dummy_input(include_encoder_hidden_states=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= super().prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= 32
						return init_dict, inputs_dict





	@unittest.skipIf(torch_device == 'mps'       ,   'MPS result is not consistent'							)
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__													= [0.7_921, -0.0_992, -0.1_962, -0.7_695, -0.4_242, 0.7_804, 0.4_737, 0.2_765, 0.3_338]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =SkipDownBlockaD  # noqa F405
	A__    :   Optional[int]				       ="""down"""
	@property
	def      A_				(			self							:   Dict							):
						return super().get_dummy_input(include_skip_sample=UpperCAmelCase_							)





	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= [-0.0_845, -0.2_087, -0.2_465, 0.0_971, 0.1_900, -0.0_484, 0.2_664, 0.4_179, 0.5_069]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[str]				       =AttnSkipDownBlockaD  # noqa F405
	A__    :   List[str]				       ="""down"""
	@property
	def      A_				(			self							:   Any							):
						return super().get_dummy_input(include_skip_sample=UpperCAmelCase_							)





	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= [0.5_539, 0.1_609, 0.4_924, 0.0_537, -0.1_995, 0.4_050, 0.0_979, -0.2_721, -0.0_642]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[Any]				       =DownEncoderBlockaD  # noqa F405
	A__    :   List[str]				       ="""down"""
	@property
	def      A_				(			self							:   int							):
						return super().get_dummy_input(include_temb=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= {
						    'in_channels': 32,
						    'out_channels': 32,
						}
						SCREAMING_SNAKE_CASE__													= self.dummy_input
						return init_dict, inputs_dict





	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= [1.1_102, 0.5_302, 0.4_872, -0.0_023, -0.8_042, 0.0_483, -0.3_489, -0.5_632, 0.7_626]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Dict				       =AttnDownEncoderBlockaD  # noqa F405
	A__    :   Tuple				       ="""down"""
	@property
	def      A_				(			self							:   int							):
						return super().get_dummy_input(include_temb=UpperCAmelCase_							)
	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= {
						    'in_channels': 32,
						    'out_channels': 32,
						}
						SCREAMING_SNAKE_CASE__													= self.dummy_input
						return init_dict, inputs_dict





	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= [0.8_966, -0.1_486, 0.8_568, 0.8_141, -0.9_046, -0.1_342, -0.0_972, -0.7_417, 0.1_538]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Optional[int]				       =UNetMidBlockaD  # noqa F405
	A__    :   Union[str, Any]				       ="""mid"""
	def      A_				(			self							:   int							):
						SCREAMING_SNAKE_CASE__													= {
						    'in_channels': 32,
						    'temb_channels': 128,
						}
						SCREAMING_SNAKE_CASE__													= self.dummy_input
						return init_dict, inputs_dict





	def      A_				(			self							:   int							):
						SCREAMING_SNAKE_CASE__													= [-0.1_062, 1.7_248, 0.3_494, 1.4_569, -0.0_910, -1.2_421, -0.9_984, 0.6_736, 1.0_028]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Optional[int]				       =UNetMidBlockaDCrossAttn  # noqa F405
	A__    :   List[Any]				       ="""mid"""
	def      A_				(			self							:   Optional[Any]							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= super().prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= 32
						return init_dict, inputs_dict





	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= [0.0_187, 2.4_220, 0.4_484, 1.1_203, -0.6_121, -1.5_122, -0.8_270, 0.7_851, 1.8_335]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Optional[int]				       =UNetMidBlockaDSimpleCrossAttn  # noqa F405
	A__    :   Dict				       ="""mid"""
	@property
	def      A_				(			self							:   List[str]							):
						return super().get_dummy_input(include_encoder_hidden_states=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= super().prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= 32
						return init_dict, inputs_dict





	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= [0.7_143, 1.9_974, 0.5_448, 1.3_977, 0.1_282, -1.1_237, -1.4_238, 0.5_530, 0.8_880]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =UpBlockaD  # noqa F405
	A__    :   Union[str, Any]				       ="""up"""
	@property
	def      A_				(			self							:   List[str]							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_							)





	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= [-0.2_041, -0.4_165, -0.3_022, 0.0_041, -0.6_628, -0.7_053, 0.1_928, -0.0_325, 0.0_523]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Optional[int]				       =ResnetUpsampleBlockaD  # noqa F405
	A__    :   Union[str, Any]				       ="""up"""
	@property
	def      A_				(			self							:   List[Any]							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_							)





	def      A_				(			self							:   List[str]							):
						SCREAMING_SNAKE_CASE__													= [0.2_287, 0.3_549, -0.1_346, 0.4_797, -0.1_715, -0.9_649, 0.7_305, -0.5_864, -0.6_244]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   str				       =CrossAttnUpBlockaD  # noqa F405
	A__    :   Optional[Any]				       ="""up"""
	@property
	def      A_				(			self							:   Tuple							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_							)
	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= super().prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= 32
						return init_dict, inputs_dict





	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= [-0.1_403, -0.3_515, -0.0_420, -0.1_425, 0.3_167, 0.5_094, -0.2_181, 0.5_931, 0.5_582]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Optional[Any]				       =SimpleCrossAttnUpBlockaD  # noqa F405
	A__    :   int				       ="""up"""
	@property
	def      A_				(			self							:   List[str]							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_       ,   include_encoder_hidden_states=UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= super().prepare_init_args_and_inputs_for_common()
						SCREAMING_SNAKE_CASE__													= 32
						return init_dict, inputs_dict





	def      A_				(			self							:   Any							):
						SCREAMING_SNAKE_CASE__													= [0.2_645, 0.1_480, 0.0_909, 0.8_044, -0.9_758, -0.9_083, 0.0_994, -1.1_453, -0.7_402]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Union[str, Any]				       =AttnUpBlockaD  # noqa F405
	A__    :   int				       ="""up"""
	@property
	def      A_				(			self							:   Dict							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_							)





	@unittest.skipIf(torch_device == 'mps'       ,   'MPS result is not consistent'							)
	def      A_				(			self							:   Optional[int]							):
						SCREAMING_SNAKE_CASE__													= [0.0_979, 0.1_326, 0.0_021, 0.0_659, 0.2_249, 0.0_059, 0.1_132, 0.5_952, 0.1_033]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[Any]				       =SkipUpBlockaD  # noqa F405
	A__    :   Tuple				       ="""up"""
	@property
	def      A_				(			self							:   Any							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_							)





	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= [-0.0_893, -0.1_234, -0.1_506, -0.0_332, 0.0_123, -0.0_211, 0.0_566, 0.0_143, 0.0_362]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[Any]				       =AttnSkipUpBlockaD  # noqa F405
	A__    :   int				       ="""up"""
	@property
	def      A_				(			self							:   Dict							):
						return super().get_dummy_input(include_res_hidden_states_tuple=UpperCAmelCase_							)





	def      A_				(			self							:   Tuple							):
						SCREAMING_SNAKE_CASE__													= [0.0_361, 0.0_617, 0.2_787, -0.0_350, 0.0_342, 0.3_421, -0.0_843, 0.0_913, 0.3_015]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   List[Any]				       =UpDecoderBlockaD  # noqa F405
	A__    :   Tuple				       ="""up"""
	@property
	def      A_				(			self							:   Any							):
						return super().get_dummy_input(include_temb=UpperCAmelCase_							)
	def      A_				(			self							:   Union[str, Any]							):
						SCREAMING_SNAKE_CASE__													= {'in_channels': 32, 'out_channels': 32}

						SCREAMING_SNAKE_CASE__													= self.dummy_input
						return init_dict, inputs_dict





	def      A_				(			self							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= [0.4_404, 0.1_998, -0.9_886, -0.3_320, -0.3_128, -0.7_034, -0.6_955, -0.2_338, -0.3_137]
						super().test_output(UpperCAmelCase_							)





class 						lowercase__  (						_UpperCAmelCase     ,       unittest.TestCase  ):
	A__    :   Any				       =AttnUpDecoderBlockaD  # noqa F405
	A__    :   Any				       ="""up"""
	@property
	def      A_				(			self							:   Any							):
						return super().get_dummy_input(include_temb=UpperCAmelCase_							)
	def      A_				(			self							:   Dict							):
						SCREAMING_SNAKE_CASE__													= {'in_channels': 32, 'out_channels': 32}

						SCREAMING_SNAKE_CASE__													= self.dummy_input
						return init_dict, inputs_dict





	def      A_				(			self							:   str							):
						SCREAMING_SNAKE_CASE__													= [0.6_738, 0.4_491, 0.1_055, 1.0_710, 0.7_316, 0.3_339, 0.3_352, 0.1_023, 0.3_568]
						super().test_output(UpperCAmelCase_							)







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


__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """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 						lowercase__  (						_UpperCAmelCase  ):
	def __init__(			self							:   str       ,   UpperCAmelCase_							:   int=None       ,   UpperCAmelCase_							:   List[str]=None       ,   *UpperCAmelCase_							:   Union[str, Any]       ,   **UpperCAmelCase_							:   Optional[Any]							):
						super().__init__(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

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

						SCREAMING_SNAKE_CASE__													= data_args
						SCREAMING_SNAKE_CASE__													= self.config.tgt_vocab_size if isinstance(self.config       ,   UpperCAmelCase_							) 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:
											SCREAMING_SNAKE_CASE__													= torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id							)
						else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											SCREAMING_SNAKE_CASE__													= label_smoothed_nll_loss
	def      A_				(			self							:   Tuple       ,   UpperCAmelCase_							:   int							):
						if self.optimizer is None:
											SCREAMING_SNAKE_CASE__													= ['bias', 'LayerNorm.weight']
											SCREAMING_SNAKE_CASE__													= [
											    {
											        '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,
											    },
											]
											SCREAMING_SNAKE_CASE__													= Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
																SCREAMING_SNAKE_CASE__													= Adafactor
																SCREAMING_SNAKE_CASE__													= {'scale_parameter': False, 'relative_step': False}
											else:
																SCREAMING_SNAKE_CASE__													= AdamW
																SCREAMING_SNAKE_CASE__													= {
																    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																    'eps': self.args.adam_epsilon,
																}
											SCREAMING_SNAKE_CASE__													= self.args.learning_rate
											if self.sharded_ddp:
																SCREAMING_SNAKE_CASE__													= OSS(
																    params=UpperCAmelCase_       ,   optim=UpperCAmelCase_       ,   **UpperCAmelCase_       ,   )
											else:
																SCREAMING_SNAKE_CASE__													= optimizer_cls(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if self.lr_scheduler is None:
											SCREAMING_SNAKE_CASE__													= self._get_lr_scheduler(UpperCAmelCase_							)
						else:  # ignoring --lr_scheduler
											logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'							)
	def      A_				(			self							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer							)
						elif self.args.lr_scheduler == "constant_w_warmup":
											SCREAMING_SNAKE_CASE__													= schedule_func(self.optimizer       ,   num_warmup_steps=self.args.warmup_steps							)
						else:
											SCREAMING_SNAKE_CASE__													= schedule_func(
											    self.optimizer       ,   num_warmup_steps=self.args.warmup_steps       ,   num_training_steps=UpperCAmelCase_							)
						return scheduler
	def      A_				(			self							:   List[str]							):
						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      A_				(			self							:   Any       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   Optional[Any]       ,   UpperCAmelCase_							:   Dict							):
						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
																SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
																SCREAMING_SNAKE_CASE__													= self.loss_fn(logits.view(-1       ,   logits.shape[-1]							)       ,   labels.view(-1							)							)
											else:
																# compute usual loss via models
																SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   labels=UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[:2]
						else:
											# compute label smoothed loss
											SCREAMING_SNAKE_CASE__													= model(**UpperCAmelCase_       ,   use_cache=UpperCAmelCase_							)[0]
											SCREAMING_SNAKE_CASE__													= torch.nn.functional.log_softmax(UpperCAmelCase_       ,   dim=-1							)
											SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self.loss_fn(UpperCAmelCase_       ,   UpperCAmelCase_       ,   self.args.label_smoothing       ,   ignore_index=self.config.pad_token_id							)
						return loss, logits
	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   str       ,   UpperCAmelCase_							:   List[Any]							):
						SCREAMING_SNAKE_CASE__													= inputs.pop('labels'							)
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= self._compute_loss(UpperCAmelCase_       ,   UpperCAmelCase_       ,   UpperCAmelCase_							)
						return loss
	def      A_				(			self							:   List[str]       ,   UpperCAmelCase_							:   nn.Module       ,   UpperCAmelCase_							:   Dict[str, Union[torch.Tensor, Any]]       ,   UpperCAmelCase_							:   bool       ,   UpperCAmelCase_							:   Optional[List[str]] = None       ,   ):
						SCREAMING_SNAKE_CASE__													= self._prepare_inputs(UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= {
						    '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:
											SCREAMING_SNAKE_CASE__													= self.model.generate(
											    inputs['input_ids']       ,   attention_mask=inputs['attention_mask']       ,   **UpperCAmelCase_       ,   )
											# in case the batch is shorter than max length, the output should be padded
											if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																SCREAMING_SNAKE_CASE__													= self._pad_tensors_to_max_len(UpperCAmelCase_       ,   gen_kwargs['max_length']							)

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

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

						SCREAMING_SNAKE_CASE__													= generated_tokens if self.args.predict_with_generate else logits

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

						return (loss, logits, labels)





	def      A_				(			self							:   Union[str, Any]       ,   UpperCAmelCase_							:   Tuple       ,   UpperCAmelCase_							:   List[str]							):
						# If PAD token is not defined at least EOS token has to be defined
						SCREAMING_SNAKE_CASE__													= 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}'							)

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





