'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


snake_case__					:      Any				      =  logging.get_logger(__name__)

snake_case__					:      List[str]				      =  {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		'''marian'''
  lowerCamelCase_		:List[Any]						   =		['''past_key_values''']
  lowerCamelCase_		:str						   =		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


  def __init__(			self				,		snake_case_=5_8_1_0_1				,		snake_case_=None				,		snake_case_=1_0_2_4				,		snake_case_=1_2				,		snake_case_=4_0_9_6				,		snake_case_=1_6				,		snake_case_=1_2				,		snake_case_=4_0_9_6				,		snake_case_=1_6				,		snake_case_=0.0				,		snake_case_=0.0				,		snake_case_=True				,		snake_case_=True				,		snake_case_="gelu"				,		snake_case_=1_0_2_4				,		snake_case_=0.1				,		snake_case_=0.0				,		snake_case_=0.0				,		snake_case_=0.02				,		snake_case_=5_8_1_0_0				,		snake_case_=False				,		snake_case_=5_8_1_0_0				,		snake_case_=0				,		snake_case_=0				,		snake_case_=True				,		**snake_case_				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       List[Any]       =				vocab_size
       UpperCAmelCase_							:       Any       =				decoder_vocab_size or vocab_size
       UpperCAmelCase_							:       Dict       =				max_position_embeddings
       UpperCAmelCase_							:       Optional[int]       =				d_model
       UpperCAmelCase_							:       Union[str, Any]       =				encoder_ffn_dim
       UpperCAmelCase_							:       Optional[int]       =				encoder_layers
       UpperCAmelCase_							:       Tuple       =				encoder_attention_heads
       UpperCAmelCase_							:       Tuple       =				decoder_ffn_dim
       UpperCAmelCase_							:       str       =				decoder_layers
       UpperCAmelCase_							:       Optional[Any]       =				decoder_attention_heads
       UpperCAmelCase_							:       Tuple       =				dropout
       UpperCAmelCase_							:       Tuple       =				attention_dropout
       UpperCAmelCase_							:       str       =				activation_dropout
       UpperCAmelCase_							:       Dict       =				activation_function
       UpperCAmelCase_							:       str       =				init_std
       UpperCAmelCase_							:       Any       =				encoder_layerdrop
       UpperCAmelCase_							:       List[Any]       =				decoder_layerdrop
       UpperCAmelCase_							:       Any       =				use_cache
       UpperCAmelCase_							:       Dict       =				encoder_layers
       UpperCAmelCase_							:       Any       =				scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCAmelCase_							:       str       =				share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=_snake_case				,		eos_token_id=_snake_case				,		is_encoder_decoder=_snake_case				,		decoder_start_token_id=_snake_case				,		forced_eos_token_id=_snake_case				,		**_snake_case				,		)







class 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_							:       str       =				OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]						)

            if self.use_past:
                 UpperCAmelCase_							:       Any       =				{0: 'batch'}
                 UpperCAmelCase_							:       str       =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                 UpperCAmelCase_							:       Tuple       =				{0: 'batch', 1: 'decoder_sequence'}
                 UpperCAmelCase_							:       List[Any]       =				{0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                 self.fill_with_past_key_values_(_snake_case				,		direction='inputs'						)
       elif self.task == "causal-lm":
            # TODO: figure this case out.
            UpperCAmelCase_							:       List[str]       =				OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]						)
            if self.use_past:
                 UpperCAmelCase_       ,      UpperCAmelCase_							:       int       =				self.num_layers
                 for i in range(_snake_case						):
                      UpperCAmelCase_							:       Union[str, Any]       =				{0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase_							:       Optional[int]       =				{0: 'batch', 2: 'past_sequence + sequence'}
       else:
            UpperCAmelCase_							:       int       =				OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ]						)

       return common_inputs


  @property
  # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_							:       int       =				super().outputs
       else:
            UpperCAmelCase_							:       Union[str, Any]       =				super(_snake_case				,		self						).outputs
            if self.use_past:
                 UpperCAmelCase_       ,      UpperCAmelCase_							:       Optional[int]       =				self.num_layers
                 for i in range(_snake_case						):
                      UpperCAmelCase_							:       str       =				{0: 'batch', 2: 'past_sequence + sequence'}
                      UpperCAmelCase_							:       Tuple       =				{0: 'batch', 2: 'past_sequence + sequence'}
       return common_outputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = -1				,		snake_case_ = -1				,		snake_case_ = False				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				self._generate_dummy_inputs_for_encoder_and_decoder(
           _snake_case				,		_snake_case				,		_snake_case				,		_snake_case				,		_snake_case						)

       # Generate decoder inputs
       UpperCAmelCase_							:       List[str]       =				seq_length if not self.use_past else 1
       UpperCAmelCase_							:       List[str]       =				self._generate_dummy_inputs_for_encoder_and_decoder(
           _snake_case				,		_snake_case				,		_snake_case				,		_snake_case				,		_snake_case						)
       UpperCAmelCase_							:       Any       =				{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       UpperCAmelCase_							:       int       =				dict(**_snake_case				,		**_snake_case						)

       if self.use_past:
            if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
            else:
                 import torch
            UpperCAmelCase_       ,      UpperCAmelCase_							:       Tuple       =				common_inputs['input_ids'].shape
            UpperCAmelCase_							:       Union[str, Any]       =				common_inputs['decoder_input_ids'].shape[1]
            UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				self.num_attention_heads
            UpperCAmelCase_							:       Tuple       =				(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            UpperCAmelCase_							:       Optional[Any]       =				decoder_seq_length + 3
            UpperCAmelCase_							:       Union[str, Any]       =				(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            UpperCAmelCase_							:       Optional[int]       =				torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(_snake_case				,		_snake_case						)]				,		dim=1						)

            UpperCAmelCase_							:       Any       =				[]
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				self.num_layers
            UpperCAmelCase_							:       Any       =				min(_snake_case				,		_snake_case						)
            UpperCAmelCase_							:       str       =				max(_snake_case				,		_snake_case						) - min_num_layers
            UpperCAmelCase_							:       Optional[int]       =				'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

            for _ in range(_snake_case						):
                 common_inputs["past_key_values"].append(
                     (
                         torch.zeros(_snake_case						),
                         torch.zeros(_snake_case						),
                         torch.zeros(_snake_case						),
                         torch.zeros(_snake_case						),
                     )						)
            # TODO: test this.
            UpperCAmelCase_							:       int       =				encoder_shape if remaining_side_name == 'encoder' else decoder_shape
            for _ in range(_snake_case				,		_snake_case						):
                 common_inputs["past_key_values"].append((torch.zeros(_snake_case						), torch.zeros(_snake_case						))						)
       return common_inputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = -1				,		snake_case_ = -1				,		snake_case_ = False				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[int]       =				self._generate_dummy_inputs_for_encoder_and_decoder(
           _snake_case				,		_snake_case				,		_snake_case				,		_snake_case				,		_snake_case						)

       if self.use_past:
            if not is_torch_available():
                 raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'						)
            else:
                 import torch
            UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            UpperCAmelCase_							:       List[Any]       =				seqlen + 2
            UpperCAmelCase_       ,      UpperCAmelCase_							:       str       =				self.num_layers
            UpperCAmelCase_       ,      UpperCAmelCase_							:       Tuple       =				self.num_attention_heads
            UpperCAmelCase_							:       Tuple       =				(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            UpperCAmelCase_							:       List[Any]       =				common_inputs['attention_mask'].dtype
            UpperCAmelCase_							:       str       =				torch.cat(
                [common_inputs['attention_mask'], torch.ones(_snake_case				,		_snake_case				,		dtype=_snake_case						)]				,		dim=1						)
            UpperCAmelCase_							:       List[Any]       =				[
                (torch.zeros(_snake_case						), torch.zeros(_snake_case						)) for _ in range(_snake_case						)
            ]
       return common_inputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = -1				,		snake_case_ = -1				,		snake_case_ = False				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				compute_effective_axis_dimension(
           _snake_case				,		fixed_dimension=OnnxConfig.default_fixed_batch				,		num_token_to_add=0						)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase_							:       Union[str, Any]       =				tokenizer.num_special_tokens_to_add(_snake_case						)
       UpperCAmelCase_							:       List[str]       =				compute_effective_axis_dimension(
           _snake_case				,		fixed_dimension=OnnxConfig.default_fixed_sequence				,		num_token_to_add=_snake_case						)

       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase_							:       int       =				[' '.join([tokenizer.unk_token]						) * seq_length] * batch_size
       UpperCAmelCase_							:       Dict       =				dict(tokenizer(_snake_case				,		return_tensors=_snake_case						)						)
       return common_inputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_ = -1				,		snake_case_ = -1				,		snake_case_ = False				,		snake_case_ = None				,		):




       '''simple docstring'''

       if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_							:       int       =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                _snake_case				,		batch_size=_snake_case				,		seq_length=_snake_case				,		is_pair=_snake_case				,		framework=_snake_case						)

       else:
            UpperCAmelCase_							:       str       =				self._generate_dummy_inputs_for_causal_lm(
                _snake_case				,		batch_size=_snake_case				,		seq_length=_snake_case				,		is_pair=_snake_case				,		framework=_snake_case						)

       return common_inputs


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       if self.task in ["default", "seq2seq-lm"]:
            UpperCAmelCase_							:       Dict       =				super()._flatten_past_key_values_(_snake_case				,		_snake_case				,		_snake_case				,		_snake_case						)
       else:
            UpperCAmelCase_							:       int       =				super(_snake_case				,		self						)._flatten_past_key_values_(
                _snake_case				,		_snake_case				,		_snake_case				,		_snake_case						)






  @property
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       return 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


snake_case__					:      Optional[Any]				      =  logging.get_logger(__name__)

snake_case__					:      List[Any]				      =  {
    '''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 					__SCREAMING_SNAKE_CASE						(	lowerCamelCase_	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_=None				,		snake_case_=None				,		*snake_case_				,		**snake_case_						):




       '''simple docstring'''

       super().__init__(*snake_case_				,		**snake_case_						)

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

       UpperCAmelCase_							:       Optional[Any]       =				data_args
       UpperCAmelCase_							:       Dict       =				self.config.tgt_vocab_size if isinstance(self.config				,		snake_case_						) 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:
            UpperCAmelCase_							:       Dict       =				torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id						)
       else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            UpperCAmelCase_							:       Union[str, Any]       =				label_smoothed_nll_loss


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

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

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


  def _UpperCamelCase					(			self				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				arg_to_scheduler[self.args.lr_scheduler]
       if self.args.lr_scheduler == "constant":
            UpperCAmelCase_							:       List[Any]       =				schedule_func(self.optimizer						)
       elif self.args.lr_scheduler == "constant_w_warmup":
            UpperCAmelCase_							:       Optional[Any]       =				schedule_func(self.optimizer				,		num_warmup_steps=self.args.warmup_steps						)
       else:
            UpperCAmelCase_							:       int       =				schedule_func(
                self.optimizer				,		num_warmup_steps=self.args.warmup_steps				,		num_training_steps=snake_case_						)
       return scheduler


  def _UpperCamelCase					(			self						):




       '''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 _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_						):




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


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Union[str, Any]       =				inputs.pop('labels'						)
       UpperCAmelCase_       ,      UpperCAmelCase_							:       Dict       =				self._compute_loss(snake_case_				,		snake_case_				,		snake_case_						)
       return loss


  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_				,		snake_case_				,		snake_case_ = None				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				self._prepare_inputs(snake_case_						)

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

       UpperCAmelCase_							:       List[str]       =				inputs.pop('labels'						)
       with torch.no_grad():
            # compute loss on predict data
            UpperCAmelCase_       ,      UpperCAmelCase_							:       List[Any]       =				self._compute_loss(snake_case_				,		snake_case_				,		snake_case_						)

       UpperCAmelCase_							:       Optional[Any]       =				loss.mean().detach()
       if self.args.prediction_loss_only:
            return (loss, None, None)

       UpperCAmelCase_							:       str       =				generated_tokens if self.args.predict_with_generate else logits

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

       return (loss, logits, labels)






  def _UpperCamelCase					(			self				,		snake_case_				,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_							:       Any       =				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}'''						)

       UpperCAmelCase_							:       Tuple       =				pad_token_id * torch.ones(
           (tensor.shape[0], max_length)				,		dtype=tensor.dtype				,		device=tensor.device						)
       UpperCAmelCase_							:       Dict       =				tensor
       return padded_tensor






