




"""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    SCREAMING_SNAKE_CASE__       (       snake_case_       ):


						def __init__(				self					:      Optional[int]    ,    lowerCAmelCase					:      int=None    ,    lowerCAmelCase					:      List[str]=None    ,    *lowerCAmelCase					:      Tuple    ,    **lowerCAmelCase					:      Tuple       ):
										super().__init__(*__lowercase    ,    **__lowercase       )

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

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

														lowerCAmelCase						=					label_smoothed_nll_loss


						def 	__lowercase			(				self					:      List[Any]    ,    lowerCAmelCase					:      Any       ):







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

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


						def 	__lowercase			(				self					:      Any    ,    lowerCAmelCase					:      Optional[Any]       ):
										lowerCAmelCase						=					arg_to_scheduler[self.args.lr_scheduler]
										if self.args.lr_scheduler == "constant":
														lowerCAmelCase						=					schedule_func(self.optimizer       )
										elif self.args.lr_scheduler == "constant_w_warmup":
														lowerCAmelCase						=					schedule_func(self.optimizer    ,    num_warmup_steps=self.args.warmup_steps       )
										else:
														lowerCAmelCase						=					schedule_func(
														    self.optimizer    ,    num_warmup_steps=self.args.warmup_steps    ,    num_training_steps=__lowercase       )
										return scheduler


						def 	__lowercase			(				self					:      Union[str, 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 	__lowercase			(				self					:      List[str]    ,    lowerCAmelCase					:      List[str]    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      Tuple       ):







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


						def 	__lowercase			(				self					:      List[Any]    ,    lowerCAmelCase					:      str    ,    lowerCAmelCase					:      Tuple       ):
										lowerCAmelCase						=					inputs.pop("""labels"""       )
										lowerCAmelCase						=					self._compute_loss(__lowercase    ,    __lowercase    ,    __lowercase       )
										return loss


						def 	__lowercase			(				self					:      Tuple    ,    lowerCAmelCase					:      int    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      List[str] = None    ,    ):
										lowerCAmelCase						=					self._prepare_inputs(__lowercase       )

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

										lowerCAmelCase						=					inputs.pop("""labels"""       )
										with torch.no_grad():
														# compute loss on predict data
														lowerCAmelCase						=					self._compute_loss(__lowercase    ,    __lowercase    ,    __lowercase       )

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

										lowerCAmelCase						=					generated_tokens if self.args.predict_with_generate else logits

										if labels.shape[-1] < gen_kwargs["max_length"]:
														lowerCAmelCase						=					self._pad_tensors_to_max_len(__lowercase    ,    gen_kwargs["""max_length"""]       )

										return (loss, logits, labels)




						def 	__lowercase			(				self					:      Tuple    ,    lowerCAmelCase					:      Optional[Any]    ,    lowerCAmelCase					:      Optional[int]       ):
										lowerCAmelCase						=					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}'''       )

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









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  List[Any]										=   {
    'facebook/deit-base-distilled-patch16-224': (
        'https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json'
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    '''deit'''


 def __init__(   self   ,   __lowercase=768   ,   __lowercase=12   ,   __lowercase=12   ,   __lowercase=3_072   ,   __lowercase="gelu"   ,   __lowercase=0.0   ,   __lowercase=0.0   ,   __lowercase=0.0_2   ,   __lowercase=1E-12   ,   __lowercase=224   ,   __lowercase=16   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=16   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(**__lowercase    )

  __A	:		Union[str, Any]	  =      hidden_size
  __A	:		int	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		List[str]	  =      hidden_act
  __A	:		str	  =      hidden_dropout_prob
  __A	:		str	  =      attention_probs_dropout_prob
  __A	:		int	  =      initializer_range
  __A	:		Tuple	  =      layer_norm_eps
  __A	:		List[Any]	  =      image_size
  __A	:		Dict	  =      patch_size
  __A	:		Optional[Any]	  =      num_channels
  __A	:		List[Any]	  =      qkv_bias
  __A	:		str	  =      encoder_stride


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    version.parse('''1.11'''					)


 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return OrderedDict(
      [
          ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
      ]    )







 @property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return 1E-4






