




"""simple docstring"""

import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           LevitForImageClassification,
           LevitForImageClassificationWithTeacher,
           LevitModel,
       )
       from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor





class     lowercase_	(					SCREAMING_SNAKE_CASE__							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       self.config_class(**self.inputs_dict		)
       self.parent.assertTrue(hasattr(A__   ,							'hidden_sizes'		)		)
       self.parent.assertTrue(hasattr(A__   ,							'num_attention_heads'		)		)





class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]=13   ,							_UpperCAmelCase	:	List[Any]=64   ,							_UpperCAmelCase	:	Union[str, Any]=3   ,							_UpperCAmelCase	:	Tuple=3   ,							_UpperCAmelCase	:	Dict=2   ,							_UpperCAmelCase	:	Any=1   ,							_UpperCAmelCase	:	Tuple=16   ,							_UpperCAmelCase	:	Optional[int]=[128, 256, 384]   ,							_UpperCAmelCase	:	str=[4, 6, 8]   ,							_UpperCAmelCase	:	str=[2, 3, 4]   ,							_UpperCAmelCase	:	List[Any]=[16, 16, 16]   ,							_UpperCAmelCase	:	Optional[int]=0   ,							_UpperCAmelCase	:	Optional[int]=[2, 2, 2]   ,							_UpperCAmelCase	:	Optional[int]=[2, 2, 2]   ,							_UpperCAmelCase	:	List[str]=0.02   ,							_UpperCAmelCase	:	str=True   ,							_UpperCAmelCase	:	int=True   ,							_UpperCAmelCase	:	Union[str, Any]=2   ,							):
       _A        =       parent
       _A        =       batch_size
       _A        =       image_size
       _A        =       num_channels
       _A        =       kernel_size
       _A        =       stride
       _A        =       padding
       _A        =       hidden_sizes
       _A        =       num_attention_heads
       _A        =       depths
       _A        =       key_dim
       _A        =       drop_path_rate
       _A        =       patch_size
       _A        =       attention_ratio
       _A        =       mlp_ratio
       _A        =       initializer_range
       _A        =       [
           ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
           ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
       ]
       _A        =       is_training
       _A        =       use_labels
       _A        =       num_labels
       _A        =       initializer_range

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

       _A        =       None
       if self.use_labels:
             _A        =       ids_tensor([self.batch_size]   ,							self.num_labels		)

       _A        =       self.get_config()
       return config, pixel_values, labels

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       return LevitConfig(
           image_size=self.image_size   ,							num_channels=self.num_channels   ,							kernel_size=self.kernel_size   ,							stride=self.stride   ,							padding=self.padding   ,							patch_size=self.patch_size   ,							hidden_sizes=self.hidden_sizes   ,							num_attention_heads=self.num_attention_heads   ,							depths=self.depths   ,							key_dim=self.key_dim   ,							drop_path_rate=self.drop_path_rate   ,							mlp_ratio=self.mlp_ratio   ,							attention_ratio=self.attention_ratio   ,							initializer_range=self.initializer_range   ,							down_ops=self.down_ops   ,							)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Dict   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       LevitModel(config=A__		)
       model.to(A__		)
       model.eval()
       _A        =       model(A__		)
       _A        =       (self.image_size, self.image_size)
       _A       ,   _A        =       image_size[0], image_size[1]
       for _ in range(4		):
             _A        =       floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
             _A        =       floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1		)
       self.parent.assertEqual(
           result.last_hidden_state.shape   ,							(self.batch_size, ceil(height / 4		) * ceil(width / 4		), self.hidden_sizes[-1])   ,							)

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	Optional[Any]		):
       _A        =       self.num_labels
       _A        =       LevitForImageClassification(A__		)
       model.to(A__		)
       model.eval()
       _A        =       model(A__   ,							labels=A__		)
       self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.num_labels)		)



 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       self.prepare_config_and_inputs()
       _A       ,   _A       ,   _A        =       config_and_inputs
       _A        =       {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class     lowercase_	(					SCREAMING_SNAKE_CASE__      ,     SCREAMING_SNAKE_CASE__      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     (
     (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
     if is_torch_available()
     else ()
 )
 UpperCAmelCase   :					Optional[Any]				  =     (
     {
         '''feature-extraction''': LevitModel,
         '''image-classification''': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase   :					Tuple				  =     False
 UpperCAmelCase   :					Any				  =     False
 UpperCAmelCase   :					Any				  =     False
 UpperCAmelCase   :					Dict				  =     False
 UpperCAmelCase   :					List[str]				  =     False

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       LevitModelTester(self		)
       _A        =       ConfigTester(self   ,							config_class=A__   ,							has_text_modality=A__   ,							hidden_size=37		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       return

 @unittest.skip(reason='Levit does not use inputs_embeds'		)
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       pass

 @unittest.skip(reason='Levit does not support input and output embeddings'		)
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       pass

 @unittest.skip(reason='Levit does not output attentions'		)
 def       lowerCAmelCase_							(			self	:	Tuple		):
       pass

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             _A        =       model_class(A__		)
             _A        =       inspect.signature(model.forward		)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             _A        =       [*signature.parameters.keys()]

             _A        =       ['pixel_values']
             self.assertListEqual(arg_names[:1]   ,							A__		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       def check_hidden_states_output(_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	List[Any]   ,							_UpperCAmelCase	:	List[Any]		):
             _A        =       model_class(A__		)
             model.to(A__		)
             model.eval()

             with torch.no_grad():
                   _A        =       model(**self._prepare_for_class(A__   ,							A__		)		)

             _A        =       outputs.hidden_states

             _A        =       len(self.model_tester.depths		) + 1
             self.assertEqual(len(A__		)   ,							A__		)

             _A        =       (self.model_tester.image_size, self.model_tester.image_size)
             _A       ,   _A        =       image_size[0], image_size[1]
             for _ in range(4		):
                   _A        =       floor(
                       (
                           (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1		)
                   _A        =       floor(
                       (
                           (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                           / self.model_tester.stride
                       )
                       + 1		)
             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:]		)   ,							[
                     height * width,
                     self.model_tester.hidden_sizes[0],
                 ]   ,							)

       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             _A        =       True
             check_hidden_states_output(A__   ,							A__   ,							A__		)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             _A        =       True

             check_hidden_states_output(A__   ,							A__   ,							A__		)

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

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	Optional[int]   ,							_UpperCAmelCase	:	Any=False		):
       _A        =       super()._prepare_for_class(A__   ,							A__   ,							return_labels=A__		)

       if return_labels:
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   del inputs_dict["labels"]

       return inputs_dict

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A__		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A__		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       if not self.model_tester.is_training:
             return

       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()
       _A        =       True

       for model_class in self.all_model_classes:
             # LevitForImageClassificationWithTeacher supports inference-only
             if (
                 model_class in get_values(A__		)
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   continue
             _A        =       model_class(A__		)
             model.to(A__		)
             model.train()
             _A        =       self._prepare_for_class(A__   ,							A__   ,							return_labels=A__		)
             _A        =       model(**A__		).loss
             loss.backward()

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       _A        =       False
       _A        =       True

       for model_class in self.all_model_classes:
             if model_class in get_values(A__		) or not model_class.supports_gradient_checkpointing:
                   continue
             # LevitForImageClassificationWithTeacher supports inference-only
             if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                   continue
             _A        =       model_class(A__		)
             model.gradient_checkpointing_enable()
             model.to(A__		)
             model.train()
             _A        =       self._prepare_for_class(A__   ,							A__   ,							return_labels=A__		)
             _A        =       model(**A__		).loss
             loss.backward()

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A       ,   _A        =       self.model_tester.prepare_config_and_inputs_for_common()

       _A        =       [
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
             if (
                 model_class
                 not in [
                     *get_values(A__		),
                 ]
                 or model_class.__name__ == "LevitForImageClassificationWithTeacher"
             ):
                   continue

             for problem_type in problem_types:
                   with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''		):
                         _A        =       problem_type['title']
                         _A        =       problem_type['num_labels']

                         _A        =       model_class(A__		)
                         model.to(A__		)
                         model.train()

                         _A        =       self._prepare_for_class(A__   ,							A__   ,							return_labels=A__		)

                         if problem_type["num_labels"] > 1:
                               _A        =       inputs['labels'].unsqueeze(1		).repeat(1   ,							problem_type['num_labels']		)

                         _A        =       inputs['labels'].to(problem_type['dtype']		)

                         # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                         # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                         # they have the same size." which is a symptom something in wrong for the regression problem.
                         # See https://github.com/huggingface/transformers/issues/11780
                         with warnings.catch_warnings(record=A__		) as warning_list:
                               _A        =       model(**A__		).loss
                         for w in warning_list:
                               if "Using a target size that is different to the input size" in str(w.message		):
                                     raise ValueError(
                                         F'''Something is going wrong in the regression problem: intercepted {w.message}'''		)

                         loss.backward()



 @slow
 def       lowerCAmelCase_							(			self	:	str		):
       for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A        =       LevitModel.from_pretrained(A__		)
             self.assertIsNotNone(A__		)






def 						_snake_case					( )   -> List[str]:






      '''simple docstring'''


      _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
      return image







@require_torch
@require_vision
class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 @cached_property
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)



 @slow
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(
           A__		)

       _A        =       self.default_image_processor
       _A        =       prepare_img()
       _A        =       image_processor(images=A__   ,							return_tensors='pt'		).to(A__		)

       # forward pass
       with torch.no_grad():
             _A        =       model(**A__		)

       # verify the logits
       _A        =       torch.Size((1, 1_000)		)
       self.assertEqual(outputs.logits.shape   ,							A__		)

       _A        =       torch.tensor([1.0448, -0.3745, -1.8317]		).to(A__		)

       self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,							A__   ,							atol=1E-4		)		)












"""simple docstring"""

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         =      logging.get_logger(__name__)

a         =      {
    '''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_	(					__lowerCAmelCase							):



 '''simple docstring'''

 def __init__(			self	:	Any   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Tuple=None   ,							*_UpperCAmelCase	:	int   ,							**_UpperCAmelCase	:	Tuple		):
       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__}'''
             )
             _A        =       self.model.config
       else:
             _A        =       config

       _A        =       data_args
       _A        =       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:
             _A        =       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
       else:
             # dynamically import label_smoothed_nll_loss
             from utils import label_smoothed_nll_loss

             _A        =       label_smoothed_nll_loss

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

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Any		):
       _A        =       arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
             _A        =       schedule_func(self.optimizer		)
       elif self.args.lr_scheduler == "constant_w_warmup":
             _A        =       schedule_func(self.optimizer   ,							num_warmup_steps=self.args.warmup_steps		)
       else:
             _A        =       schedule_func(
                 self.optimizer   ,							num_warmup_steps=self.args.warmup_steps   ,							num_training_steps=_UpperCAmelCase		)
       return scheduler

 def       lowerCAmelCase_							(			self	:	Any		):
       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       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int		):
       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
                   _A        =       model(**_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[0]
                   _A        =       self.loss_fn(logits.view(-1   ,							logits.shape[-1]		)   ,							labels.view(-1		)		)
             else:
                   # compute usual loss via models
                   _A       ,   _A        =       model(**_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[:2]
       else:
             # compute label smoothed loss
             _A        =       model(**_UpperCAmelCase   ,							use_cache=_UpperCAmelCase		)[0]
             _A        =       torch.nn.functional.log_softmax(_UpperCAmelCase   ,							dim=-1		)
             _A       ,   _A        =       self.loss_fn(_UpperCAmelCase   ,							_UpperCAmelCase   ,							self.args.label_smoothing   ,							ignore_index=self.config.pad_token_id		)
       return loss, logits

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A        =       inputs.pop('labels'		)
       _A       ,   _A        =       self._compute_loss(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return loss

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	nn.Module   ,							_UpperCAmelCase	:	Dict[str, Union[torch.Tensor, Any]]   ,							_UpperCAmelCase	:	bool   ,							_UpperCAmelCase	:	Optional[List[str]] = None   ,							):
       _A        =       self._prepare_inputs(_UpperCAmelCase		)

       _A        =       {
           '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:
             _A        =       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"]:
                   _A        =       self._pad_tensors_to_max_len(_UpperCAmelCase   ,							gen_kwargs['max_length']		)

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

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

       _A        =       generated_tokens if self.args.predict_with_generate else logits

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

       return (loss, logits, labels)



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Tuple		):
       # If PAD token is not defined at least EOS token has to be defined
       _A        =       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}'''		)

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

