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


__lowerCamelCase      :			List[Any]							       =		logging.get_logger(__name__)

__lowerCamelCase      :			Optional[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 __snake_case    (						_lowerCAmelCase		):




   def __init__(   self      :    Union[str, Any]	,     _lowercase      :    str=None	,     _lowercase      :    Tuple=None	,     *_lowercase      :    List[str]	,     **_lowercase      :    Dict				):



     """simple docstring"""






     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__}"""
       )
       SCREAMING_SNAKE_CASE__      							=				self.model.config
     else:
       SCREAMING_SNAKE_CASE__      							=				config

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

       SCREAMING_SNAKE_CASE__      							=				label_smoothed_nll_loss




   def      __a						(   self      :    Any	,     _lowercase      :    int				):



     """simple docstring"""






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

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




   def      __a						(   self      :    int	,     _lowercase      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				arg_to_scheduler[self.args.lr_scheduler]
     if self.args.lr_scheduler == "constant":
       SCREAMING_SNAKE_CASE__      							=				schedule_func(self.optimizer				)
     elif self.args.lr_scheduler == "constant_w_warmup":
       SCREAMING_SNAKE_CASE__      							=				schedule_func(self.optimizer	,     num_warmup_steps=self.args.warmup_steps				)
     else:
       SCREAMING_SNAKE_CASE__      							=				schedule_func(
           self.optimizer	,     num_warmup_steps=self.args.warmup_steps	,     num_training_steps=_lowercase				)
     return scheduler




   def      __a						(   self      :    Union[str, Any]				):



     """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      :    Optional[int]	,     _lowercase      :    str	,     _lowercase      :    Optional[Any]	,     _lowercase      :    Any				):



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




   def      __a						(   self      :    Tuple	,     _lowercase      :    List[str]	,     _lowercase      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				inputs.pop("""labels"""				)
     SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				self._compute_loss(_lowercase	,     _lowercase	,     _lowercase				)
     return loss




   def      __a						(   self      :    Any	,     _lowercase      :    nn.Module	,     _lowercase      :    Dict[str, Union[torch.Tensor, Any]]	,     _lowercase      :    bool	,     _lowercase      :    Optional[List[str]] = None	,     ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self._prepare_inputs(_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				{
         """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:
       SCREAMING_SNAKE_CASE__      							=				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"]:
         SCREAMING_SNAKE_CASE__      							=				self._pad_tensors_to_max_len(_lowercase	,     gen_kwargs["""max_length"""]				)

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

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

     SCREAMING_SNAKE_CASE__      							=				generated_tokens if self.args.predict_with_generate else logits

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

     return (loss, logits, labels)




   def      __a						(   self      :    Union[str, Any]	,     _lowercase      :    Union[str, Any]	,     _lowercase      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				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}"""				)

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

import unittest

from .lib import (
    Matrix,
    Vector,
    axpy,
    square_zero_matrix,
    unit_basis_vector,
    zero_vector,
)







class __snake_case    (						unittest.TestCase		):




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     self.assertEqual(x.component(0				)	,     1				)
     self.assertEqual(x.component(2				)	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Vector()




   def      __a						(   self      :    int				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([0, 0, 0, 0, 0, 1]				)
     self.assertEqual(str(_lowercase				)	,     """(0,0,0,0,0,1)"""				)




   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3, 4]				)
     self.assertEqual(len(_lowercase				)	,     4				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3, 4, 5]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, -1, 1, -1, 2, -3, 4, -5]				)
     self.assertAlmostEqual(x.euclidean_length()	,     2.2_36	,     3				)
     self.assertAlmostEqual(y.euclidean_length()	,     7.4_16	,     3				)
     self.assertEqual(z.euclidean_length()	,     0				)
     self.assertAlmostEqual(w.euclidean_length()	,     7.6_16	,     3				)




   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 1, 1]				)
     self.assertEqual((x + y).component(0				)	,     2				)
     self.assertEqual((x + y).component(1				)	,     3				)
     self.assertEqual((x + y).component(2				)	,     4				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 1, 1]				)
     self.assertEqual((x - y).component(0				)	,     0				)
     self.assertEqual((x - y).component(1				)	,     1				)
     self.assertEqual((x - y).component(2				)	,     2				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([2, -1, 4]				)  # for test of dot product
     SCREAMING_SNAKE_CASE__      							=				Vector([1, -2, -1]				)
     self.assertEqual(str(x * 3.0				)	,     """(3.0,6.0,9.0)"""				)
     self.assertEqual((a * b)	,     0				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     self.assertEqual(str(zero_vector(10				)				).count("""0"""				)	,     10				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     self.assertEqual(str(unit_basis_vector(3	,     1				)				)	,     """(0,1,0)"""				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 0, 1]				)
     self.assertEqual(str(axpy(2	,     _lowercase	,     _lowercase				)				)	,     """(3,4,7)"""				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 0, 0, 0, 0, 0]				)
     SCREAMING_SNAKE_CASE__      							=				x.copy()
     self.assertEqual(str(_lowercase				)	,     str(_lowercase				)				)




   def      __a						(   self      :    str				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Vector([1, 0, 0]				)
     x.change_component(0	,     0				)
     x.change_component(1	,     1				)
     self.assertEqual(str(_lowercase				)	,     """(0,1,0)"""				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     self.assertEqual("""|1,2,3|\n|2,4,5|\n|6,7,8|\n"""	,     str(_lowercase				)				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				[[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
     for x in range(a.height()				):
       for y in range(a.width()				):
         self.assertEqual(minors[x][y]	,     a.minor(_lowercase	,     _lowercase				)				)




   def      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				[[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
     for x in range(a.height()				):
       for y in range(a.width()				):
         self.assertEqual(cofactors[x][y]	,     a.cofactor(_lowercase	,     _lowercase				)				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     self.assertEqual(-5	,     a.determinant()				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Vector([1, 2, 3]				)
     self.assertEqual("""(14,32,50)"""	,     str(a * x				)				)
     self.assertEqual("""|2,4,6|\n|8,10,12|\n|14,16,18|\n"""	,     str(a * 2				)				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     a.change_component(0	,     2	,     5				)
     self.assertEqual("""|1,2,5|\n|2,4,5|\n|6,7,8|\n"""	,     str(_lowercase				)				)




   def      __a						(   self      :    Union[str, Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     self.assertEqual(7	,     a.component(2	,     1				)	,     0.01				)




   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]	,     3	,     3				)
     self.assertEqual("""|2,4,10|\n|4,8,10|\n|12,14,18|\n"""	,     str(a + b				)				)




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]]	,     3	,     3				)
     SCREAMING_SNAKE_CASE__      							=				Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]]	,     3	,     3				)
     self.assertEqual("""|0,0,-4|\n|0,0,0|\n|0,0,-2|\n"""	,     str(a - b				)				)




   def      __a						(   self      :    Any				):



     """simple docstring"""






     self.assertEqual(
         """|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n"""	,     str(square_zero_matrix(5				)				)	,     )


if __name__ == "__main__":
   unittest.main()
