




from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCamelCase__					:  Any      							=				logging.get_logger(__name__)


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset

   from transformers import Trainer





   class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


    '''simple docstring'''







    def __init__(				self    :						Any							,							_lowerCAmelCase    :						int = 101		):
          SCREAMING_SNAKE_CASE_      		=     length



    def __len__(				self    :						str		):
          return self.length




    def __getitem__(				self    :						Dict							,							_lowerCAmelCase    :						str		):
          return i





   class 			lowerCamelCase_				:


    '''simple docstring'''







    def __call__(				self    :						List[str]							,							_lowerCAmelCase    :						Optional[int]		):
          return {"input_ids": torch.tensor(_lowerCAmelCase		), "labels": torch.tensor(_lowerCAmelCase		)}





   class 			lowerCamelCase_				( nn.Module   ):


    '''simple docstring'''







    def __init__(				self    :						List[Any]		):
          super().__init__()
          # Add some (unused) params otherwise DDP will complain.
          SCREAMING_SNAKE_CASE_      		=     nn.Linear(120							,							80		)




    def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						int=None		):
          if labels is not None:
                return torch.tensor(0.0							,							device=input_ids.device		), input_ids
          else:
                return input_ids





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 @require_torch_neuroncore
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     F"--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
       SCREAMING_SNAKE_CASE_      		=     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_      		=     F"--output_dir {output_dir}".split()
       SCREAMING_SNAKE_CASE_      		=     ['torchrun'] + distributed_args + args
       execute_subprocess_async(_lowerCAmelCase							,							env=self.get_env()		)
       # successful return here == success - any errors would have caused an error in the sub-call





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 @require_torch_multi_gpu
 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     F"--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        ".split()
       SCREAMING_SNAKE_CASE_      		=     self.get_auto_remove_tmp_dir()
       SCREAMING_SNAKE_CASE_      		=     F"--output_dir {output_dir}".split()
       SCREAMING_SNAKE_CASE_      		=     ['torchrun'] + distributed_args + args
       execute_subprocess_async(_lowerCAmelCase							,							env=self.get_env()		)
       # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
   # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
   #
   # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

   lowerCamelCase__					:  int      							=				HfArgumentParser((TrainingArguments,))
   lowerCamelCase__					:  Optional[int]      							=				parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
       f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
   )

   # Essentially, what we want to verify in the distributed case is that we get all samples back,
   # in the right order. (this is crucial for prediction for instance)
   for dataset_length in [101, 40, 7]:
      lowerCamelCase__					:  str      							=				DummyDataset(dataset_length)

      def    UpperCAmelCase_				( __UpperCAmelCase		:      EvalPrediction				)   ->					Dict:
            SCREAMING_SNAKE_CASE_      		=     list(range(len(__UpperCAmelCase				)				)				)
            SCREAMING_SNAKE_CASE_      		=     p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
            if not success and training_args.local_rank == 0:
                  logger.warning(
                      'Predictions and/or labels do not match expected results:\n  - predictions: '
                      f"{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}"				)
            return {"success": success}

      lowerCamelCase__					:  Tuple      							=				Trainer(
          model=DummyModel(),
          args=training_args,
          data_collator=DummyDataCollator(),
          eval_dataset=dataset,
          compute_metrics=compute_metrics,
      )
      lowerCamelCase__					:  List[Any]      							=				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      lowerCamelCase__					:  Dict      							=				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)

      lowerCamelCase__					:  List[str]      							=				2

      lowerCamelCase__					:  Tuple      							=				trainer.evaluate()
      logger.info(metrics)
      if metrics["eval_success"] is not True:
         logger.error(metrics)
         exit(1)

      lowerCamelCase__					:  int      							=				trainer.predict(dataset)
      logger.info(p.metrics)
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics)
         exit(1)





      lowerCamelCase__					:  Union[str, Any]      							=				None





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   lowerCamelCase__					:  Optional[Any]      							=				None

lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)
lowerCamelCase__					:  List[str]      							=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					:  List[str]      							=				{
    'vocab_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/spiece.model',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					:  List[Any]      							=				'▁'





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       ["input_ids", "token_type_ids"]
 lowercase_							       =       FNetTokenizer



 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						List[str]=False							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						List[Any]="<unk>"							,							_lowerCAmelCase    :						Optional[Any]="[SEP]"							,							_lowerCAmelCase    :						Optional[Any]="<pad>"							,							_lowerCAmelCase    :						Optional[int]="[CLS]"							,							_lowerCAmelCase    :						Optional[Any]="[MASK]"							,							**_lowerCAmelCase    :						Any							,							):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       SCREAMING_SNAKE_CASE_      		=     (
           AddedToken(_lowerCAmelCase							,							lstrip=_lowerCAmelCase							,							rstrip=_lowerCAmelCase							,							normalized=_lowerCAmelCase		)
           if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		)
           else mask_token
       )

       super().__init__(
           _lowerCAmelCase							,							tokenizer_file=_lowerCAmelCase							,							do_lower_case=_lowerCAmelCase							,							remove_space=_lowerCAmelCase							,							keep_accents=_lowerCAmelCase							,							unk_token=_lowerCAmelCase							,							sep_token=_lowerCAmelCase							,							pad_token=_lowerCAmelCase							,							cls_token=_lowerCAmelCase							,							mask_token=_lowerCAmelCase							,							**_lowerCAmelCase							,							)

       SCREAMING_SNAKE_CASE_      		=     do_lower_case
       SCREAMING_SNAKE_CASE_      		=     remove_space
       SCREAMING_SNAKE_CASE_      		=     keep_accents
       SCREAMING_SNAKE_CASE_      		=     vocab_file
       SCREAMING_SNAKE_CASE_      		=     False if not self.vocab_file else True



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [self.cls_token_id]
       if token_ids_a is None:
             return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep



 def  lowerCAmelCase_    (				self    :						Optional[int]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = None		):
       SCREAMING_SNAKE_CASE_      		=     [self.sep_token_id]
       SCREAMING_SNAKE_CASE_      		=     [self.cls_token_id]

       if token_ids_a is None:
             return len(cls + token_ids_a + sep		) * [0]
       return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       if not os.path.isdir(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
             return
       SCREAMING_SNAKE_CASE_      		=     os.path.join(
           _lowerCAmelCase							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

       if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCAmelCase		):
             copyfile(self.vocab_file							,							_lowerCAmelCase		)

       return (out_vocab_file,)