






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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   Any						=					OpenAIGPTTokenizer
						A     :   int						=					OpenAIGPTTokenizerFast
						A     :   Optional[Any]						=					True
						A     :   Tuple						=					False






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

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

								snake_case_				:							Union[str, Any]						 =       os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES["vocab_file"]				)
								snake_case_				:							Tuple						 =       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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Any:
								return "lower newer", "lower newer"






						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								snake_case_				:							int						 =       OpenAIGPTTokenizer(self.vocab_file		,				self.merges_file				)

								snake_case_				:							Optional[Any]						 =       "lower"
								snake_case_				:							List[str]						 =       ["low", "er</w>"]
								snake_case_				:							Dict						 =       tokenizer.tokenize(_SCREAMING_SNAKE_CASE				)
								self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Tuple						 =       tokens + ["<unk>"]
								snake_case_				:							Any						 =       [14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE				)		,				_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=15				) ->				Dict:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
												snake_case_				:							str						 =       self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)

												# Simple input
												snake_case_				:							List[str]						 =       "This is a simple input"
												snake_case_				:							str						 =       ["This is a simple input 1", "This is a simple input 2"]
												snake_case_				:							int						 =       ("This is a simple input", "This is a pair")
												snake_case_				:							List[Any]						 =       [
												    ("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       _lowerCAmelCase       (	self				) ->				Any:
								pass




@require_ftfy
@require_spacy
@require_tokenizers
class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						pass








from typing import TYPE_CHECKING

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


lowercase  :							str			  =    {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							List[str]			  =    [
							    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''BioGptForCausalLM''',
							    '''BioGptForTokenClassification''',
							    '''BioGptForSequenceClassification''',
							    '''BioGptModel''',
							    '''BioGptPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
							from .tokenization_biogpt import BioGptTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_biogpt import (
														    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BioGptForCausalLM,
														    BioGptForSequenceClassification,
														    BioGptForTokenClassification,
														    BioGptModel,
														    BioGptPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Tuple			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
