'''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 				_lowercase     (				lowerCAmelCase_  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       int      	=					OpenAIGPTTokenizer
 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					OpenAIGPTTokenizerFast
 _SCREAMING_SNAKE_CASE     :       Dict      	=					True
 _SCREAMING_SNAKE_CASE     :       Tuple      	=					False
 def a					(      self   :	List[str]							)  ->    Optional[int]:
  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(__snake_case      ,       range(len(__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(__snake_case							)							)
  with open(self.merges_file      ,       """w"""							) as fp:
   fp.write("""\n""".join(__snake_case							)							)
 def a					(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    List[Any]:
  return "lower newer", "lower newer"
 def a					(      self   :	List[str]							)  ->    Tuple:
  __lowerCAmelCase											=						OpenAIGPTTokenizer(self.vocab_file      ,       self.merges_file							)

  __lowerCAmelCase											=						"""lower"""
  __lowerCAmelCase											=						["""low""", """er</w>"""]
  __lowerCAmelCase											=						tokenizer.tokenize(__snake_case							)
  self.assertListEqual(__snake_case      ,       __snake_case							)

  __lowerCAmelCase											=						tokens + ["""<unk>"""]
  __lowerCAmelCase											=						[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case							)      ,       __snake_case							)
 def a					(      self   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=15							)  ->    List[str]:
  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(__snake_case      ,       **__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(__snake_case      ,       tokenizer_r.encode      ,       __snake_case      ,       max_length=__snake_case      ,       padding="""max_length"""							)

    # Simple input
    self.assertRaises(__snake_case      ,       tokenizer_r.encode_plus      ,       __snake_case      ,       max_length=__snake_case      ,       padding="""max_length"""							)

    # Simple input
    self.assertRaises(
        __snake_case      ,       tokenizer_r.batch_encode_plus      ,       __snake_case      ,       max_length=__snake_case      ,       padding="""max_length"""      ,       )

    # Pair input
    self.assertRaises(__snake_case      ,       tokenizer_r.encode      ,       __snake_case      ,       max_length=__snake_case      ,       padding="""max_length"""							)

    # Pair input
    self.assertRaises(__snake_case      ,       tokenizer_r.encode_plus      ,       __snake_case      ,       max_length=__snake_case      ,       padding="""max_length"""							)

    # Pair input
    self.assertRaises(
        __snake_case      ,       tokenizer_r.batch_encode_plus      ,       __snake_case      ,       max_length=__snake_case      ,       padding="""max_length"""      ,       )





 def a					(      self   :	List[str]							)  ->    Tuple:
  pass







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


 '''simple docstring'''






 pass









import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase					:			List[Any]  				=			get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")






@require_sentencepiece
@require_tokenizers
class 	__magic_name__    (			lowerCAmelCase_  ,    unittest.TestCase      ):
			SCREAMING_SNAKE_CASE							   =						GPTSwaTokenizer
			SCREAMING_SNAKE_CASE							   =						False
			SCREAMING_SNAKE_CASE							   =						True
			SCREAMING_SNAKE_CASE							   =						False





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().setUp()

						# We have a SentencePiece fixture for testing
						__a        =GPTSwaTokenizer(__snake_case					,  eos_token='<unk>'					,  bos_token='<unk>'					,  pad_token='<unk>'					)

						tokenizer.save_pretrained(self.tmpdirname					)





			def 		__magic_name__    (		self					,  __snake_case					)   -> Tuple:




						'''simple docstring'''
						__a        ='This is a test'
						__a        ='This is a test'
						return input_text, output_text





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						__a        ='<s>'
						__a        =1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case					)					,  __snake_case					)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case					)					,  __snake_case					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						__a        =list(self.get_tokenizer().get_vocab().keys()					)

						self.assertEqual(vocab_keys[0]					,  '<unk>'					)
						self.assertEqual(vocab_keys[1]					,  '<s>'					)
						self.assertEqual(vocab_keys[-1]					,  'j'					)
						self.assertEqual(len(__snake_case					)					,  2000					)





			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						self.assertEqual(self.get_tokenizer().vocab_size					,  2000					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)

						__a        =tokenizer.tokenize('This is a test'					)
						self.assertListEqual(__snake_case					,  ['▁This', '▁is', '▁a', '▁t', 'est']					)

						self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)					,  [465, 287, 265, 631, 842]					)

						__a        =tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,  )
						# fmt: on

						__a        =tokenizer.convert_tokens_to_ids(__snake_case					)
						self.assertListEqual(
						    __snake_case					,  [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,  )

						__a        =tokenizer.convert_ids_to_tokens(__snake_case					)
						# fmt: off
						self.assertListEqual(
						    __snake_case					,  ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					)
						# fmt: on





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =GPTSwaTokenizer(__snake_case					)
						__a        =['This is a test', 'I was born in 92000, and this is falsé.']
						__a        =[
						    [465, 287, 265, 631, 842],
						    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
						]

						# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
						for text, expected_ids in zip(__snake_case					,  __snake_case					):
									self.assertListEqual(tokenizer.encode_fast(__snake_case					)					,  __snake_case					)

						# Test that decode_fast returns the input text
						for text, token_ids in zip(__snake_case					,  __snake_case					):
									self.assertEqual(tokenizer.decode_fast(__snake_case					)					,  __snake_case					)





			@slow
			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						__a        =[
						    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
						    'Hey there, how are you doing this fine day?',
						    'This is a text with a trailing spaces followed by a dot     .',
						    'Häj sväjs lillebrör! =)',
						    'Det är inget fel på Mr. Cool',
						]

						# fmt: off
						__a        ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
						# fmt: on
						self.tokenizer_integration_test_util(
						    expected_encoding=__snake_case					,  model_name='AI-Sweden/gpt-sw3-126m'					,  sequences=__snake_case					,  )






