



import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 							lowerCAmelCase__  (	UpperCamelCase					,							unittest.TestCase						):
					__A     :    List[Any]           =						BioGptTokenizer
					__A     :    Optional[Any]           =						False







					def  _lowercase			(       self      :     str):
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											A__	:							Any						    = [
											    "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>",
											]
											A__	:							Optional[Any]						    = dict(zip(_A			,		range(len(_A))))
											A__	:							str						    = ["l o 123", "lo w 1456", "e r</w> 1789", ""]

											A__	:							int						    = os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES["vocab_file"])
											A__	:							List[Any]						    = os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES["merges_file"])
											with open(self.vocab_file			,		"w") as fp:
																	fp.write(json.dumps(_A))
											with open(self.merges_file			,		"w") as fp:
																	fp.write("\n".join(_A))







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Dict):
											A__	:							Dict						    = "lower newer"
											A__	:							Dict						    = "lower newer"
											return input_text, output_text







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							Dict						    = BioGptTokenizer(self.vocab_file			,		self.merges_file)

											A__	:							Any						    = "lower"
											A__	:							List[Any]						    = ["low", "er</w>"]
											A__	:							Tuple						    = tokenizer.tokenize(_A)
											self.assertListEqual(_A			,		_A)

											A__	:							List[Any]						    = tokens + ["<unk>"]
											A__	:							Optional[Any]						    = [14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)			,		_A)







					@slow
					def  _lowercase			(       self      :     Optional[int]):
											A__	:							Tuple						    = BioGptTokenizer.from_pretrained("microsoft/biogpt")

											A__	:							Optional[int]						    = tokenizer.encode("sequence builders"			,		add_special_tokens=_A)
											A__	:							Optional[Any]						    = tokenizer.encode("multi-sequence build"			,		add_special_tokens=_A)

											A__	:							Tuple						    = tokenizer.build_inputs_with_special_tokens(_A)
											A__	:							Union[str, Any]						    = tokenizer.build_inputs_with_special_tokens(_A			,		_A)

											self.assertTrue(encoded_sentence == [2] + text)
											self.assertTrue(encoded_pair == [2] + text + [2] + text_a)




from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset




class 							lowerCAmelCase__  (	UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											return [
											    {"col_1": 3, "col_2": "a"},
											    {"col_1": 2, "col_2": "b"},
											    {"col_1": 1, "col_2": "c"},
											    {"col_1": 0, "col_2": "d"},
											]







					def  _lowercase			(       self      :     Tuple):
											A__	:							Any						    = {"col_1": [3, 2, 1, 0], "col_2": ["a", "b", "c", "d"]}
											return Dataset.from_dict(_A)







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							str						    = self._create_example_records()
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertListEqual(dset.column_names			,		["col_1", "col_2"])
											for i, r in enumerate(_A):
																	self.assertDictEqual(_A			,		example_records[i])







					def  _lowercase			(       self      :     List[str]):
											A__	:							List[str]						    = self._create_example_records()
											A__	:							Any						    = Dataset.from_list(_A)
											A__	:							Optional[int]						    = Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]})
											self.assertEqual(dset.info			,		dset_from_dict.info)







					def  _lowercase			(       self      :     Any):  # checks what happens with missing columns
											A__	:							Dict						    = [{"col_1": 1}, {"col_2": "x"}]
											A__	:							Any						    = Dataset.from_list(_A)
											self.assertDictEqual(dset[0]			,		{"col_1": 1})
											self.assertDictEqual(dset[1]			,		{"col_1": None})  # NB: first record is used for columns







					def  _lowercase			(       self      :     str):  # checks if the type can be inferred from the second record
											A__	:							Dict						    = [{"col_1": []}, {"col_1": [1, 2]}]
											A__	:							Dict						    = Dataset.from_list(_A)
											self.assertEqual(dset.info.features["col_1"]			,		Sequence(Value("int64")))







					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Dict						    = Dataset.from_list([])
											self.assertEqual(len(_A)			,		0)
											self.assertListEqual(dset.column_names			,		[])