

"""simple docstring"""


import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class      __a							(_UpperCamelCase   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = GPTaTokenizer
     _SCREAMING_SNAKE_CASE						:List[Any]       = GPTaTokenizerFast
     _SCREAMING_SNAKE_CASE						:Any       = True
     _SCREAMING_SNAKE_CASE						:List[str]       = {"""add_prefix_space""": True}
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = False

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	[
               "l",
               "o",
               "w",
               "e",
               "r",
               "s",
               "t",
               "i",
               "d",
               "n",
               "\u0120",
               "\u0120l",
               "\u0120n",
               "\u0120lo",
               "\u0120low",
               "er",
               "\u0120lowest",
               "\u0120newer",
               "\u0120wider",
               "<unk>",
               "<|endoftext|>",
           ]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	dict(zip(_a	,    range(len(_a  )  )  )  )
           SCREAMING_SNAKE_CASE__   :      str      		=	["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{"unk_token": "<unk>"}

           SCREAMING_SNAKE_CASE__   :      Dict      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""vocab_file"""]  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES["""merges_file"""]  )
           with open(self.vocab_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 fp.write(json.dumps(_a  ) + """\n"""  )
           with open(self.merges_file	,    """w"""	,    encoding="""utf-8"""  ) as fp:
                 fp.write("""\n""".join(_a  )  )

     def 					_a			(						self	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           kwargs.update(self.special_tokens_map  )
           return GPTaTokenizer.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    **_a  )  ->      Optional[int]:


           """simple docstring"""


           kwargs.update(self.special_tokens_map  )
           return GPTaTokenizerFast.from_pretrained(self.tmpdirname	,    **_a  )

     def 					_a			(						self	,    _a  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	"lower newer"
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"lower newer"
           return input_text, output_text

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	GPTaTokenizer(self.vocab_file	,    self.merges_file	,    **self.special_tokens_map  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	"lower newer"
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	tokenizer.tokenize(_a	,    add_prefix_space=_a  )
           self.assertListEqual(_a	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokens + [tokenizer.unk_token]
           SCREAMING_SNAKE_CASE__   :      str      		=	[14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )	,    _a  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           if not self.test_rust_tokenizer:
                 return

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.get_tokenizer()
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.get_rust_tokenizer(add_prefix_space=_a  )

           SCREAMING_SNAKE_CASE__   :      int      		=	"lower newer"

           # Testing tokenization
           SCREAMING_SNAKE_CASE__   :      str      		=	tokenizer.tokenize(_a	,    add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	rust_tokenizer.tokenize(_a  )
           self.assertListEqual(_a	,    _a  )

           # Testing conversion to ids without special tokens
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer.encode(_a	,    add_special_tokens=_a	,    add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	rust_tokenizer.encode(_a	,    add_special_tokens=_a  )
           self.assertListEqual(_a	,    _a  )

           # Testing conversion to ids with special tokens
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_rust_tokenizer(add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.encode(_a	,    add_prefix_space=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	rust_tokenizer.encode(_a  )
           self.assertListEqual(_a	,    _a  )

           # Testing the unknown token
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokens + [rust_tokenizer.unk_token]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[14, 15, 10, 9, 3, 2, 15, 19]
           self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a  )	,    _a  )

     def 					_a			(						self	,    *_a	,    **_a  )  ->      Dict:


           """simple docstring"""


           pass

     def 					_a			(						self	,    _a=15  )  ->      Dict:


           """simple docstring"""


           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.rust_tokenizer_class.from_pretrained(_a	,    **_a  )

                       # Simple input
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	"This is a simple input"
                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	["This is a simple input 1", "This is a simple input 2"]
                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	("This is a simple input", "This is a pair")
                       SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	[
                           ("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(_a	,    tokenizer_r.encode	,    _a	,    max_length=_a	,    padding="""max_length"""  )

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

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

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

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

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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	GPTaTokenizer.from_pretrained(self.tmpdirname	,    pad_token="""<pad>"""  )

           # Simple input
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"This is a simple input"
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	["This is a simple input looooooooong", "This is a simple input"]
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	("This is a simple input", "This is a pair")
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[
               ("This is a simple input loooooong", "This is a simple input"),
               ("This is a simple pair loooooong", "This is a simple pair"),
           ]

           SCREAMING_SNAKE_CASE__   :      Any      		=	tokenizer.pad_token_id

           SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer(_a	,    padding="""max_length"""	,    max_length=30	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer(_a	,    padding=_a	,    truncate=_a	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer(*_a	,    padding="""max_length"""	,    max_length=60	,    return_tensors="""np"""  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer(_a	,    padding=_a	,    truncate=_a	,    return_tensors="""np"""  )

           # s
           # test single string max_length padding
           self.assertEqual(out_s["""input_ids"""].shape[-1]	,    30  )
           self.assertTrue(pad_token_id in out_s["""input_ids"""]  )
           self.assertTrue(0 in out_s["""attention_mask"""]  )

           # s2
           # test automatic padding
           self.assertEqual(out_sa["""input_ids"""].shape[-1]	,    33  )
           # long slice doesn't have padding
           self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]  )
           self.assertFalse(0 in out_sa["""attention_mask"""][0]  )
           # short slice does have padding
           self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]  )
           self.assertTrue(0 in out_sa["""attention_mask"""][1]  )

           # p
           # test single pair max_length padding
           self.assertEqual(out_p["""input_ids"""].shape[-1]	,    60  )
           self.assertTrue(pad_token_id in out_p["""input_ids"""]  )
           self.assertTrue(0 in out_p["""attention_mask"""]  )

           # p2
           # test automatic padding pair
           self.assertEqual(out_pa["""input_ids"""].shape[-1]	,    52  )
           # long slice pair doesn't have padding
           self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]  )
           self.assertFalse(0 in out_pa["""attention_mask"""][0]  )
           # short slice pair does have padding
           self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]  )
           self.assertTrue(0 in out_pa["""attention_mask"""][1]  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	"$$$"
           SCREAMING_SNAKE_CASE__   :      Dict      		=	GPTaTokenizer.from_pretrained(self.tmpdirname	,    bos_token=_a	,    add_bos_token=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"This is a simple input"
           SCREAMING_SNAKE_CASE__   :      Dict      		=	["This is a simple input 1", "This is a simple input 2"]

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tokenizer.bos_token_id

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer(_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	tokenizer(_a  )

           self.assertEqual(out_s.input_ids[0]	,    _a  )
           self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids  )  )

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.decode(out_s.input_ids  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tokenizer.batch_decode(out_sa.input_ids  )

           self.assertEqual(decode_s.split()[0]	,    _a  )
           self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa  )  )

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


           """simple docstring"""


           pass





     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	[self.get_tokenizer(do_lower_case=_a	,    add_bos_token=_a  )]
           for tokenizer in tokenizers:
                 with self.subTest(f'''{tokenizer.__class__.__name__}'''  ):
                       SCREAMING_SNAKE_CASE__   :      str      		=	"Encode this."
                       SCREAMING_SNAKE_CASE__   :      List[Any]      		=	"This one too please."
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer.encode(_a	,    add_special_tokens=_a  )
                       encoded_sequence += tokenizer.encode(_a	,    add_special_tokens=_a  )
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer.encode_plus(
                           _a	,    _a	,    add_special_tokens=_a	,    return_special_tokens_mask=_a	,    )
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	encoded_sequence_dict["input_ids"]
                       SCREAMING_SNAKE_CASE__   :      int      		=	encoded_sequence_dict["special_tokens_mask"]
                       self.assertEqual(len(_a  )	,    len(_a  )  )

                       SCREAMING_SNAKE_CASE__   :      List[str]      		=	[
                           (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a  )
                       ]
                       SCREAMING_SNAKE_CASE__   :      Any      		=	[x for x in filtered_sequence if x is not None]
                       self.assertEqual(_a	,    _a  )





@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	AutoTokenizer.from_pretrained("""facebook/opt-350m"""	,    from_slow=_a  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"A photo of a cat"

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer.encode(
               _a	,    )
           self.assertEqual(_a	,    [2, 250, 1_345, 9, 10, 4_758]  )
           tokenizer.save_pretrained("""test_opt"""  )

           SCREAMING_SNAKE_CASE__   :      int      		=	AutoTokenizer.from_pretrained("""./test_opt"""  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer.encode(
               _a	,    )
           self.assertEqual(_a	,    [2, 250, 1_345, 9, 10, 4_758]  )

     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	AutoTokenizer.from_pretrained("""facebook/opt-350m"""	,    use_slow=_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	"A photo of a cat"

           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer.encode(
               _a	,    )
           # Same as above
           self.assertEqual(_a	,    [2, 250, 1_345, 9, 10, 4_758]  )





     @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""  )
     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	AutoTokenizer.from_pretrained("""facebook/opt-350m"""	,    from_slow=_a  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"bos"
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tokenizer.get_vocab()["bos"]

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	"A photo of a cat"
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tokenizer.encode(
               _a	,    )
           # We changed the bos token
           self.assertEqual(_a	,    [31_957, 250, 1_345, 9, 10, 4_758]  )
           tokenizer.save_pretrained("""./tok"""  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	AutoTokenizer.from_pretrained("""./tok"""  )
           self.assertTrue(tokenizer.is_fast  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	tokenizer.encode(
               _a	,    )
           self.assertEqual(_a	,    [31_957, 250, 1_345, 9, 10, 4_758]  )





"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

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


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




