


"""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      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				Dict             =    OpenAIGPTTokenizer
    a_    :				str             =    OpenAIGPTTokenizerFast
    a_    :				Tuple             =    True
    a_    :				Any             =    False
    def      _UpperCamelCase       ( self							:					List[Any]				):
          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(SCREAMING_SNAKE_CASE__							,					range(len(SCREAMING_SNAKE_CASE__				)				)				)				)
          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(SCREAMING_SNAKE_CASE__				)				)
          with open(self.merges_file							,					'w'				) as fp:
                fp.write('\n'.join(SCREAMING_SNAKE_CASE__				)				)
    def      _UpperCamelCase       ( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          return "lower newer", "lower newer"
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		OpenAIGPTTokenizer(self.vocab_file							,					self.merges_file				)

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

          lowerCamelCase__           =		tokens + ['<unk>']
          lowerCamelCase__           =		[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__				)							,					SCREAMING_SNAKE_CASE__				)
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					List[Any]=15				):
          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(SCREAMING_SNAKE_CASE__							,					**SCREAMING_SNAKE_CASE__				)

                      # 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(SCREAMING_SNAKE_CASE__							,					tokenizer_r.encode							,					SCREAMING_SNAKE_CASE__							,					max_length=SCREAMING_SNAKE_CASE__							,					padding='max_length'				)

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

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

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

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

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


    def      _UpperCamelCase       ( self							:					str				):
          pass





@require_ftfy
@require_spacy
@require_tokenizers
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    pass







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case          =							{
    "configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfig"],
    "tokenization_m2m_100": ["M2M100Tokenizer"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST",
       "M2M100ForConditionalGeneration",
       "M2M100Model",
       "M2M100PreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
   from .tokenization_mam_aaa import MaMaaaTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mam_aaa import (
          M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaMaaaForConditionalGeneration,
          MaMaaaModel,
          MaMaaaPreTrainedModel,
      )


else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



