

'''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         =  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        a__  (							_lowerCAmelCase  ):


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





      """simple docstring"""





      super().__init__(*SCREAMING_SNAKE_CASE_      ,    **SCREAMING_SNAKE_CASE_  )

      if config is None:
         assert isinstance(self.model      ,    SCREAMING_SNAKE_CASE_  ), (
             "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
             f''' {self.model.__class__}'''
         )
         _lowercase      :				Optional[int]      					=       self.model.config
      else:
         _lowercase      :				str      					=       config

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

         _lowercase      :				Union[str, Any]      					=       label_smoothed_nll_loss


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





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

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       arg_to_scheduler[self.args.lr_scheduler]
      if self.args.lr_scheduler == "constant":
         _lowercase      :				Any      					=       schedule_func(self.optimizer  )
      elif self.args.lr_scheduler == "constant_w_warmup":
         _lowercase      :				Union[str, Any]      					=       schedule_func(self.optimizer      ,    num_warmup_steps=self.args.warmup_steps  )
      else:
         _lowercase      :				Optional[Any]      					=       schedule_func(
             self.optimizer      ,    num_warmup_steps=self.args.warmup_steps      ,    num_training_steps=SCREAMING_SNAKE_CASE_  )
      return scheduler


   def 						_lowerCamelCase       (			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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Any      					=       inputs.pop("labels"  )
      _lowercase      :				Any      					=       self._compute_loss(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  )
      return loss


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





      """simple docstring"""





      _lowercase      :				List[Any]      					=       self._prepare_inputs(SCREAMING_SNAKE_CASE_  )

      _lowercase      :				List[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:
         _lowercase      :				Union[str, Any]      					=       self.model.generate(
             inputs["input_ids"]      ,    attention_mask=inputs["attention_mask"]      ,    **SCREAMING_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"]:
            _lowercase      :				List[Any]      					=       self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,    gen_kwargs["max_length"]  )

      _lowercase      :				Optional[int]      					=       inputs.pop("labels"  )
      with torch.no_grad():
         # compute loss on predict data
         _lowercase      :				Any      					=       self._compute_loss(SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  )

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

      _lowercase      :				Tuple      					=       generated_tokens if self.args.predict_with_generate else logits

      if labels.shape[-1] < gen_kwargs["max_length"]:
         _lowercase      :				Dict      					=       self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_      ,    gen_kwargs["max_length"]  )

      return (loss, logits, labels)






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Union[str, 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}'''  )

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









'''simple docstring'''
import os
import sys
import unittest


_snake_case         =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_snake_case         =  os.path.join(git_repo_path, 'src', 'transformers')

_snake_case         =  '\n{0} = None\n'

_snake_case         =  '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_snake_case         =  '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'


class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"  )
      self.assertIsNone(_UpperCamelCase  )

      _lowercase      :				Dict      					=       find_backend("    if not is_tokenizers_available():"  )
      self.assertEqual(_UpperCamelCase      ,    "tokenizers"  )

      _lowercase      :				str      					=       find_backend("    if not is_tensorflow_text_available():"  )
      self.assertEqual(_UpperCamelCase      ,    "tensorflow_text"  )

      _lowercase      :				Optional[Any]      					=       find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tokenizers"  )

      _lowercase      :				List[str]      					=       find_backend(
          "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tensorflow_text"  )

      _lowercase      :				Optional[Any]      					=       find_backend(
          "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tokenizers_and_vision"  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"      ,    _UpperCamelCase  )
      self.assertIn("tensorflow_text"      ,    _UpperCamelCase  )
      self.assertIn("sentencepiece_and_tokenizers"      ,    _UpperCamelCase  )

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("BertModel"      ,    objects["torch"]  )
      self.assertIn("TFBertModel"      ,    objects["tf"]  )
      self.assertIn("FlaxBertModel"      ,    objects["flax"]  )
      self.assertIn("BertModel"      ,    objects["torch"]  )
      self.assertIn("TFBertTokenizer"      ,    objects["tensorflow_text"]  )
      self.assertIn("convert_slow_tokenizer"      ,    objects["sentencepiece_and_tokenizers"]  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       create_dummy_object("CONSTANT"      ,    "'torch'"  )
      self.assertEqual(_UpperCamelCase      ,    "\nCONSTANT = None\n"  )

      _lowercase      :				Optional[Any]      					=       create_dummy_object("function"      ,    "'torch'"  )
      self.assertEqual(
          _UpperCamelCase      ,    "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"  )

      _lowercase      :				Union[str, Any]      					=       "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
      _lowercase      :				List[str]      					=       create_dummy_object("FakeClass"      ,    "'torch'"  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
      _lowercase      :				str      					=       create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}  )
      self.assertEqual(dummy_files["torch"]      ,    _UpperCamelCase  )






