"""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 					lowerCAmelCase_      (     lowerCAmelCase       ,      unittest.TestCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      int     					=  OpenAIGPTTokenizer
				_lowerCAmelCase       :      int     					=  OpenAIGPTTokenizerFast
				_lowerCAmelCase       :      Tuple     					=  True
				_lowerCAmelCase       :      List[str]     					=  False

				def 						snake_case	(			self      ):






										"""simple docstring"""

										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										snake_case        =     [
										    '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>',
										]
										snake_case        =     dict(zip(lowerCAmelCase						,      range(len(lowerCAmelCase      )      )      )      )
										snake_case        =     ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

										snake_case        =     os.path.join(self.tmpdirname						,      VOCAB_FILES_NAMES['vocab_file']      )
										snake_case        =     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 						snake_case	(			self						,      lowerCAmelCase      ):






										"""simple docstring"""

										return "lower newer", "lower newer"

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     OpenAIGPTTokenizer(self.vocab_file						,      self.merges_file      )

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

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

				def 						snake_case	(			self						,      lowerCAmelCase=15      ):






										"""simple docstring"""

										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																						snake_case        =     self.rust_tokenizer_class.from_pretrained(lowerCAmelCase						,      **lowerCAmelCase      )

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






										"""simple docstring"""

										pass





@require_ftfy
@require_spacy
@require_tokenizers
class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				pass



"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
    "configuration_data2vec_text": [
        "DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecTextConfig",
        "Data2VecTextOnnxConfig",
    ],
    "configuration_data2vec_vision": [
        "DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Data2VecVisionConfig",
        "Data2VecVisionOnnxConfig",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecAudioForAudioFrameClassification",
					    "Data2VecAudioForCTC",
					    "Data2VecAudioForSequenceClassification",
					    "Data2VecAudioForXVector",
					    "Data2VecAudioModel",
					    "Data2VecAudioPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecTextForCausalLM",
					    "Data2VecTextForMaskedLM",
					    "Data2VecTextForMultipleChoice",
					    "Data2VecTextForQuestionAnswering",
					    "Data2VecTextForSequenceClassification",
					    "Data2VecTextForTokenClassification",
					    "Data2VecTextModel",
					    "Data2VecTextPreTrainedModel",
					]
					SCREAMING_SNAKE_CASE__   =					[
					    "DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Data2VecVisionForImageClassification",
					    "Data2VecVisionForMaskedImageModeling",
					    "Data2VecVisionForSemanticSegmentation",
					    "Data2VecVisionModel",
					    "Data2VecVisionPreTrainedModel",
					]

if is_tf_available():
					SCREAMING_SNAKE_CASE__   =					[
					    "TFData2VecVisionForImageClassification",
					    "TFData2VecVisionForSemanticSegmentation",
					    "TFData2VecVisionModel",
					    "TFData2VecVisionPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
					from .configuration_dataavec_text import (
					    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecTextConfig,
					    DataaVecTextOnnxConfig,
					)
					from .configuration_dataavec_vision import (
					    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DataaVecVisionConfig,
					    DataaVecVisionOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dataavec_audio import (
										    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecAudioForAudioFrameClassification,
										    DataaVecAudioForCTC,
										    DataaVecAudioForSequenceClassification,
										    DataaVecAudioForXVector,
										    DataaVecAudioModel,
										    DataaVecAudioPreTrainedModel,
										)
										from .modeling_dataavec_text import (
										    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecTextForCausalLM,
										    DataaVecTextForMaskedLM,
										    DataaVecTextForMultipleChoice,
										    DataaVecTextForQuestionAnswering,
										    DataaVecTextForSequenceClassification,
										    DataaVecTextForTokenClassification,
										    DataaVecTextModel,
										    DataaVecTextPreTrainedModel,
										)
										from .modeling_dataavec_vision import (
										    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DataaVecVisionForImageClassification,
										    DataaVecVisionForMaskedImageModeling,
										    DataaVecVisionForSemanticSegmentation,
										    DataaVecVisionModel,
										    DataaVecVisionPreTrainedModel,
										)
					if is_tf_available():
										from .modeling_tf_dataavec_vision import (
										    TFDataaVecVisionForImageClassification,
										    TFDataaVecVisionForSemanticSegmentation,
										    TFDataaVecVisionModel,
										    TFDataaVecVisionPreTrainedModel,
										)

else:
					import sys

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


