






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


SCREAMING_SNAKE_CASE__   :      Dict				=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   :      List[str]				=		{
    """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     lowerCamelCase_					(   lowerCamelCase			):







				def __init__(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       *__lowerCAmelCase							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__(*__lowerCAmelCase							,       **__lowerCAmelCase  )

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

								__magic_name__		:Tuple   =   data_args
								__magic_name__		:Any   =   self.config.tgt_vocab_size if isinstance(self.config							,       __lowerCAmelCase  ) else self.config.vocab_size

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

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

								if self.args.label_smoothing == 0:
												__magic_name__		:List[Any]   =   torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id  )
								else:
												# dynamically import label_smoothed_nll_loss
												from utils import label_smoothed_nll_loss

												__magic_name__		:str   =   label_smoothed_nll_loss







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

								if self.lr_scheduler is None:
												__magic_name__		:Union[str, Any]   =   self._get_lr_scheduler(__lowerCAmelCase  )
								else:  # ignoring --lr_scheduler
												logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'''  )







				def       A						(    self							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   arg_to_scheduler[self.args.lr_scheduler]
								if self.args.lr_scheduler == "constant":
												__magic_name__		:int   =   schedule_func(self.optimizer  )
								elif self.args.lr_scheduler == "constant_w_warmup":
												__magic_name__		:List[str]   =   schedule_func(self.optimizer							,       num_warmup_steps=self.args.warmup_steps  )
								else:
												__magic_name__		:str   =   schedule_func(
												    self.optimizer							,       num_warmup_steps=self.args.warmup_steps							,       num_training_steps=__lowerCAmelCase  )
								return scheduler







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







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								__magic_name__		:str   =   inputs.pop('''labels'''  )
								__magic_name__  ,					__magic_name__		:Optional[Any]   =   self._compute_loss(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )
								return loss







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase = None							,       ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self._prepare_inputs(__lowerCAmelCase  )

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

								__magic_name__		:int   =   inputs.pop('''labels'''  )
								with torch.no_grad():
												# compute loss on predict data
												__magic_name__  ,					__magic_name__		:Optional[int]   =   self._compute_loss(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

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

								__magic_name__		:int   =   generated_tokens if self.args.predict_with_generate else logits

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

								return (loss, logits, labels)







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase  ):
								"""simple docstring"""
								# If PAD token is not defined at least EOS token has to be defined
								__magic_name__		:Optional[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}'''  )

								__magic_name__		:str   =   pad_token_id * torch.ones(
								    (tensor.shape[0], max_length)							,       dtype=tensor.dtype							,       device=tensor.device  )
								__magic_name__		:Optional[Any]   =   tensor
								return padded_tensor











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCamelCase											=		{
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		['BridgeTowerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'BridgeTowerForContrastiveLearning',
       'BridgeTowerForImageAndTextRetrieval',
       'BridgeTowerForMaskedLM',
       'BridgeTowerModel',
       'BridgeTowerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _UpperCamelCase											=		_LazyModule(__name__, globals()['__file__'], _import_structure)






