






"""simple docstring"""





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      a						(					lowerCAmelCase_  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     str						= OpenAIGPTTokenizer
    SCREAMING_SNAKE_CASE						:     List[str]						= OpenAIGPTTokenizerFast
    SCREAMING_SNAKE_CASE						:     Tuple						= True
    SCREAMING_SNAKE_CASE						:     int						= False






    def    UpperCamelCase       (       self     :							Dict					)     ->		List[str]:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_         =						[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          'w</w>',
          'r</w>',
          't</w>',
          'lo',
          'low',
          'er</w>',
          'low</w>',
          'lowest</w>',
          'newer</w>',
          'wider</w>',
          '<unk>',
      ]
      lowerCamelCase_         =						dict(zip(lowerCamelCase__       ,						range(len(lowerCamelCase__					)					)					)					)
      lowerCamelCase_         =						['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

      lowerCamelCase_         =						os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['vocab_file']					)
      lowerCamelCase_         =						os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['merges_file']					)
      with open(self.vocab_file       ,						'w'					) as fp:
        fp.write(json.dumps(lowerCamelCase__					)					)
      with open(self.merges_file       ,						'w'					) as fp:
        fp.write('\n'.join(lowerCamelCase__					)					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Dict					)     ->		Optional[int]:
      return "lower newer", "lower newer"






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      lowerCamelCase_         =						OpenAIGPTTokenizer(self.vocab_file       ,						self.merges_file					)

      lowerCamelCase_         =						'lower'
      lowerCamelCase_         =						['low', 'er</w>']
      lowerCamelCase_         =						tokenizer.tokenize(lowerCamelCase__					)
      self.assertListEqual(lowerCamelCase__       ,						lowerCamelCase__					)

      lowerCamelCase_         =						tokens + ['<unk>']
      lowerCamelCase_         =						[14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__					)       ,						lowerCamelCase__					)






    def    UpperCamelCase       (       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=15					)     ->		Tuple:


      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
          lowerCamelCase_         =						self.rust_tokenizer_class.from_pretrained(lowerCamelCase__       ,						**lowerCamelCase__					)

          # Simple input
          lowerCamelCase_         =						'This is a simple input'
          lowerCamelCase_         =						['This is a simple input 1', 'This is a simple input 2']
          lowerCamelCase_         =						('This is a simple input', 'This is a pair')
          lowerCamelCase_         =						[
              ('This is a simple input 1', 'This is a simple input 2'),
              ('This is a simple pair 1', 'This is a simple pair 2'),
          ]

          # Simple input tests
          self.assertRaises(lowerCamelCase__       ,						tokenizer_r.encode       ,						lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding='max_length'					)

          # Simple input
          self.assertRaises(lowerCamelCase__       ,						tokenizer_r.encode_plus       ,						lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding='max_length'					)

          # Simple input
          self.assertRaises(
              lowerCamelCase__       ,						tokenizer_r.batch_encode_plus       ,						lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding='max_length'       ,						)

          # Pair input
          self.assertRaises(lowerCamelCase__       ,						tokenizer_r.encode       ,						lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding='max_length'					)

          # Pair input
          self.assertRaises(lowerCamelCase__       ,						tokenizer_r.encode_plus       ,						lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding='max_length'					)

          # Pair input
          self.assertRaises(
              lowerCamelCase__       ,						tokenizer_r.batch_encode_plus       ,						lowerCamelCase__       ,						max_length=lowerCamelCase__       ,						padding='max_length'       ,						)






    def    UpperCamelCase       (       self     :							str					)     ->		List[str]:
      pass






@require_ftfy
@require_spacy
@require_tokenizers
class      a						(					lowerCAmelCase_		):
    pass











import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


SCREAMING_SNAKE_CASE_						   =       logging.getLogger(__name__)

@dataclass
class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Optional[float]			=			field(
         default=0.0							,      metadata={"help": "The label smoothing epsilon to apply (if not zero)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "Whether to SortishSamler or not."}   )
     __snake_case    :	bool			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}   )
     __snake_case    :	bool			=			field(default=lowerCAmelCase_							,      metadata={"help": "whether to use adafactor"}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Encoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Decoder layer dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(default=lowerCAmelCase_							,      metadata={"help": "Dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[float]			=			field(
         default=lowerCAmelCase_							,      metadata={"help": "Attention dropout probability. Goes into model.config."}   )
     __snake_case    :	Optional[str]			=			field(
         default="linear"							,      metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()   )}"}							,      )


