



"""simple docstring"""
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       snake_case_    (    __lowercase  ,unittest.TestCase					):
							__lowerCAmelCase            =       BioGptTokenizer
							__lowerCAmelCase            =       False






							def snake_case_	( self   ):
									super().setUp()

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

									a_  :   Any			    =  os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES["vocab_file"]   )
									a_  :   int			    =  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 snake_case_	( self      ,  a_   ):
									a_  :   Any			    =  """lower newer"""
									a_  :   Optional[int]			    =  """lower newer"""
									return input_text, output_text






							def snake_case_	( self   ):
									a_  :   List[str]			    =  BioGptTokenizer(self.vocab_file      ,  self.merges_file   )

									a_  :   Any			    =  """lower"""
									a_  :   Tuple			    =  ["""low""", """er</w>"""]
									a_  :   Optional[int]			    =  tokenizer.tokenize(a_   )
									self.assertListEqual(a_      ,  a_   )

									a_  :   Tuple			    =  tokens + ["""<unk>"""]
									a_  :   Tuple			    =  [1_4, 1_5, 2_0]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_   )      ,  a_   )






							@slow
							def snake_case_	( self   ):
									a_  :   List[str]			    =  BioGptTokenizer.from_pretrained("microsoft/biogpt"   )

									a_  :   Tuple			    =  tokenizer.encode("sequence builders"      ,  add_special_tokens=a_   )
									a_  :   str			    =  tokenizer.encode("multi-sequence build"      ,  add_special_tokens=a_   )

									a_  :   int			    =  tokenizer.build_inputs_with_special_tokens(a_   )
									a_  :   Tuple			    =  tokenizer.build_inputs_with_special_tokens(a_      ,  a_   )

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






'''simple docstring'''






from unittest import TestCase

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




class  _A (					__lowercase		):

		def 	lowercase__     (   self					:   Any			)					->    str:


					"""simple docstring"""

					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					:   str			)					->    int:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     {"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
					return Dataset.from_dict(__magic_name__			)

		def 	lowercase__     (   self					:   str			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self._create_example_records()
					__snake_case					:      str							      =     Dataset.from_list(__magic_name__			)
					self.assertListEqual(dset.column_names  ,      ["""col_1""", """col_2"""]			)
					for i, r in enumerate(__magic_name__			):
								self.assertDictEqual(__magic_name__  ,      example_records[i]			)

		def 	lowercase__     (   self					:   Optional[Any]			)					->    List[Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self._create_example_records()
					__snake_case					:      Dict							      =     Dataset.from_list(__magic_name__			)
					__snake_case					:      List[Any]							      =     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					:   str			)					->    List[Any]:  # checks what happens with missing columns


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     [{"""col_1""": 1}, {"""col_2""": """x"""}]
					__snake_case					:      Optional[int]							      =     Dataset.from_list(__magic_name__			)
					self.assertDictEqual(dset[0]  ,      {"""col_1""": 1}			)
					self.assertDictEqual(dset[1]  ,      {"""col_1""": None}			)  # NB: first record is used for columns

		def 	lowercase__     (   self					:   List[str]			)					->    Optional[Any]:  # checks if the type can be inferred from the second record


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     [{"""col_1""": []}, {"""col_1""": [1, 2]}]
					__snake_case					:      int							      =     Dataset.from_list(__magic_name__			)
					self.assertEqual(dset.info.features["""col_1"""]  ,      Sequence(Value("""int64"""			)			)			)

		def 	lowercase__     (   self					:   int			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     Dataset.from_list([]			)
					self.assertEqual(len(__magic_name__			)  ,      0			)
					self.assertListEqual(dset.column_names  ,      []			)


