





'''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:					int							=				logging.get_logger(__name__)

_A:					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 UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A=None     ,			__A=None     ,			*__A     ,			**__A					):
     super().__init__(*__A     ,			**__A					)

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

     __UpperCAmelCase					=   data_args
     __UpperCAmelCase					=   self.config.tgt_vocab_size if isinstance(self.config     ,			__A					) 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					=   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					=   label_smoothed_nll_loss



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

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



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



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



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   inputs.pop('labels'					)
     __UpperCAmelCase					,    __UpperCAmelCase					=   self._compute_loss(__A     ,			__A     ,			__A					)
     return loss



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A = None     ,			):
     __UpperCAmelCase					=   self._prepare_inputs(__A					)

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

     __UpperCAmelCase					=   inputs.pop('labels'					)
     with torch.no_grad():
      # compute loss on predict data
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._compute_loss(__A     ,			__A     ,			__A					)

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

     __UpperCAmelCase					=   generated_tokens if self.args.predict_with_generate else logits

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

     return (loss, logits, labels)




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # If PAD token is not defined at least EOS token has to be defined
     __UpperCAmelCase					=   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					=   pad_token_id * torch.ones(
         (tensor.shape[0], max_length)     ,			dtype=tensor.dtype     ,			device=tensor.device					)
     __UpperCAmelCase					=   tensor
     return padded_tensor












'''simple docstring'''




import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):
    _A :			List[str]						   =       MgpstrTokenizer
    _A :			Any						   =       False
    _A :			List[Any]						   =       {}
    _A :			int						   =       False



    def 					__lowerCamelCase						(  self					):
     super().setUp()

     # fmt: off
     __UpperCAmelCase					=   ['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
     # fmt: on
     __UpperCAmelCase					=   dict(zip(__A     ,			range(len(__A					)					)					)					)

     __UpperCAmelCase					=   os.path.join(self.tmpdirname     ,			VOCAB_FILES_NAMES['vocab_file']					)
     with open(self.vocab_file     ,			'w'     ,			encoding='utf-8'					) as fp:
      fp.write(json.dumps(__A					) + '\n'					)



    def 					__lowerCamelCase						(  self     ,			**__A					):
     return MgpstrTokenizer.from_pretrained(self.tmpdirname     ,			**__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   'tester'
     __UpperCAmelCase					=   'tester'
     return input_text, output_text



    @unittest.skip('MGP-STR always lower cases letters.'					)
    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers(do_lower_case=__A					)
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					=   '[SPECIAL_TOKEN]'

       tokenizer.add_special_tokens({'cls_token': special_token}					)
       __UpperCAmelCase					=   tokenizer.encode([special_token]     ,			add_special_tokens=__A					)
       self.assertEqual(len(__A					)     ,			1					)

       __UpperCAmelCase					=   tokenizer.decode(__A     ,			skip_special_tokens=__A					)
       self.assertTrue(special_token not in decoded					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_tokenizers()
     for tokenizer in tokenizers:
      with self.subTest(f'{tokenizer.__class__.__name__}'					):
       __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_input_output_texts(__A					)

       __UpperCAmelCase					=   tokenizer.tokenize(__A					)
       __UpperCAmelCase					=   tokenizer.convert_tokens_to_ids(__A					)
       __UpperCAmelCase					=   tokenizer.encode(__A     ,			add_special_tokens=__A					)
       self.assertListEqual(__A     ,			__A					)

       __UpperCAmelCase					=   tokenizer.convert_ids_to_tokens(__A					)
       self.assertNotEqual(len(__A					)     ,			0					)
       __UpperCAmelCase					=   tokenizer.decode(__A					)
       self.assertIsInstance(__A     ,			__A					)

       self.assertEqual(text_a.replace(' '     ,			''					)     ,			__A					)



    @unittest.skip('MGP-STR tokenizer only handles one sequence.'					)
    def 					__lowerCamelCase						(  self					):
     pass




    @unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'					)
    def 					__lowerCamelCase						(  self					):
     pass





