"""simple docstring"""




from typing import TYPE_CHECKING

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


_a   :	List[str] 					=     {
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	str 					=     [
         'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TapasForMaskedLM',
         'TapasForQuestionAnswering',
         'TapasForSequenceClassification',
         'TapasModel',
         'TapasPreTrainedModel',
         'load_tf_weights_in_tapas',
     ]
try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a   :	Dict 					=     [
         'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFTapasForMaskedLM',
         'TFTapasForQuestionAnswering',
         'TFTapasForSequenceClassification',
         'TFTapasModel',
         'TFTapasPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
     from .tokenization_tapas import TapasTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tapas import (
              TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TapasForMaskedLM,
              TapasForQuestionAnswering,
              TapasForSequenceClassification,
              TapasModel,
              TapasPreTrainedModel,
              load_tf_weights_in_tapas,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_tapas import (
              TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFTapasForMaskedLM,
              TFTapasForQuestionAnswering,
              TFTapasForSequenceClassification,
              TFTapasModel,
              TFTapasPreTrainedModel,
          )


else:
     import sys

     _a   :	List[Any] 					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizer
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizerFast
     _UpperCamelCase							:    Tuple	      =	True
     _UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
     _UpperCamelCase							:    str	      =	False






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

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

       _lowerCAmelCase		: List[str]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       _lowerCAmelCase		: List[Any]						    =  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__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  """lower newer"""
       _lowerCAmelCase		: Tuple						    =  """lower newer"""
       return input_text, output_text






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  CodeGenTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map						)
       _lowerCAmelCase		: int						    =  """lower newer"""
       _lowerCAmelCase		: List[str]						    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       self.assertListEqual(a__       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokens + [tokenizer.unk_token]
       _lowerCAmelCase		: List[str]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self						):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer()
       _lowerCAmelCase		: Optional[int]						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

       _lowerCAmelCase		: Any						    =  """lower newer"""

       # Testing tokenization
       _lowerCAmelCase		: Any						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: int						    =  rust_tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids without special tokens
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(a__       ,  add_special_tokens=a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Dict						    =  rust_tokenizer.encode(a__       ,  add_special_tokens=a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids with special tokens
       _lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.encode(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Any						    =  rust_tokenizer.encode(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing the unknown token
       _lowerCAmelCase		: List[str]						    =  tokens + [rust_tokenizer.unk_token]
       _lowerCAmelCase		: Dict						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass






     def 					__A						(	self       ,  a__=15						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
           _lowerCAmelCase		: List[Any]						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

           # Simple input
           _lowerCAmelCase		: Dict						    =  """This is a simple input"""
           _lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase		: Optional[int]						    =  ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase		: str						    =  [
               ("""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						):
       _lowerCAmelCase		: Any						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

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

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.pad_token_id

       _lowerCAmelCase		: Any						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: str						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  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						):
       _lowerCAmelCase		: List[str]						    =  """$$$"""
       _lowerCAmelCase		: str						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

       _lowerCAmelCase		: Tuple						    =  """This is a simple input"""
       _lowerCAmelCase		: Union[str, Any]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

       _lowerCAmelCase		: List[str]						    =  tokenizer.bos_token_id

       _lowerCAmelCase		: str						    =  tokenizer(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  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						)						)

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
       _lowerCAmelCase		: Optional[int]						    =  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						)						)






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""						)

       _lowerCAmelCase		: Optional[int]						    =  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
       _lowerCAmelCase		: List[Any]						    =  """\nif len_a > len_b:      result = a\nelse:      result = b"""

       _lowerCAmelCase		: Tuple						    =  tokenizer.encode(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  ["""^#""", re.escape("""<|endoftext|>"""						), """^'''""", """^\"\"\"""", """\n\n\n"""]
       _lowerCAmelCase		: int						    =  tokenizer.decode(a__       ,  truncate_before_pattern=a__						)
       self.assertEqual(a__       ,  a__						)






     def 					__A						(	self						):
       pass




