


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   ):
       UpperCAmelCase__ :  str						      =	GPTaTokenizer
       UpperCAmelCase__ :  List[Any]						      =	GPTaTokenizerFast
       UpperCAmelCase__ :  List[Any]						      =	True
       UpperCAmelCase__ :  Optional[Any]						      =	{"add_prefix_space": True}
       UpperCAmelCase__ :  Dict						      =	False





       def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
          super().setUp()

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

          __lowerCamelCase:				Dict     =	os.path.join(self.tmpdirname    ,	VOCAB_FILES_NAMES["""vocab_file"""]							)
          __lowerCamelCase:				Optional[int]     =	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(SCREAMING_SNAKE_CASE_							) + """\n"""							)
          with open(self.merges_file    ,	"""w"""    ,	encoding="""utf-8"""							) as fp:
             fp.write("""\n""".join(SCREAMING_SNAKE_CASE_							)							)





       def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname    ,	**SCREAMING_SNAKE_CASE_							)





       def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]    ,	**SCREAMING_SNAKE_CASE_					:				Optional[int]							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname    ,	**SCREAMING_SNAKE_CASE_							)





       def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]							):
          __lowerCamelCase:				List[str]     =	"""lower newer"""
          __lowerCamelCase:				str     =	"""lower newer"""
          return input_text, output_text





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

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





       def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
          if not self.test_rust_tokenizer:
             return

          __lowerCamelCase:				Optional[int]     =	self.get_tokenizer()
          __lowerCamelCase:				Union[str, Any]     =	self.get_rust_tokenizer(add_prefix_space=SCREAMING_SNAKE_CASE_							)

          __lowerCamelCase:				List[str]     =	"""lower newer"""

          # Testing tokenization
          __lowerCamelCase:				List[str]     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE_    ,	add_prefix_space=SCREAMING_SNAKE_CASE_							)
          __lowerCamelCase:				List[str]     =	rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_							)
          self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

          # Testing conversion to ids without special tokens
          __lowerCamelCase:				List[Any]     =	tokenizer.encode(SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_    ,	add_prefix_space=SCREAMING_SNAKE_CASE_							)
          __lowerCamelCase:				int     =	rust_tokenizer.encode(SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_							)
          self.assertListEqual(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

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

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





       def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	*SCREAMING_SNAKE_CASE_					:				Dict    ,	**SCREAMING_SNAKE_CASE_					:				List[str]							):
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass





       def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Tuple=15							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
                __lowerCamelCase:				List[str]     =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

                # 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:				List[Any]     =	("""This is a simple input""", """This is a pair""")
                __lowerCamelCase:				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(SCREAMING_SNAKE_CASE_    ,	tokenizer_r.encode    ,	SCREAMING_SNAKE_CASE_    ,	max_length=SCREAMING_SNAKE_CASE_    ,	padding="""max_length"""							)

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

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

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

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

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





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

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

          __lowerCamelCase:				Dict     =	tokenizer.pad_token_id

          __lowerCamelCase:				List[Any]     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	padding="""max_length"""    ,	max_length=30    ,	return_tensors="""np"""							)
          __lowerCamelCase:				List[Any]     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	padding=SCREAMING_SNAKE_CASE_    ,	truncate=SCREAMING_SNAKE_CASE_    ,	return_tensors="""np"""							)
          __lowerCamelCase:				Optional[int]     =	tokenizer(*SCREAMING_SNAKE_CASE_    ,	padding="""max_length"""    ,	max_length=60    ,	return_tensors="""np"""							)
          __lowerCamelCase:				Dict     =	tokenizer(SCREAMING_SNAKE_CASE_    ,	padding=SCREAMING_SNAKE_CASE_    ,	truncate=SCREAMING_SNAKE_CASE_    ,	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  SCREAMING_SNAKE_CASE__       (    self					:				int							):
          __lowerCamelCase:				List[Any]     =	"""$$$"""
          __lowerCamelCase:				List[str]     =	GPTaTokenizer.from_pretrained(self.tmpdirname    ,	bos_token=SCREAMING_SNAKE_CASE_    ,	add_bos_token=SCREAMING_SNAKE_CASE_							)

          __lowerCamelCase:				Any     =	"""This is a simple input"""
          __lowerCamelCase:				int     =	["""This is a simple input 1""", """This is a simple input 2"""]

          __lowerCamelCase:				str     =	tokenizer.bos_token_id

          __lowerCamelCase:				str     =	tokenizer(SCREAMING_SNAKE_CASE_							)
          __lowerCamelCase:				Optional[Any]     =	tokenizer(SCREAMING_SNAKE_CASE_							)

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

          __lowerCamelCase:				Tuple     =	tokenizer.decode(out_s.input_ids							)
          __lowerCamelCase:				List[Any]     =	tokenizer.batch_decode(out_sa.input_ids							)

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





       def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
          pass







       def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          __lowerCamelCase:				List[str]     =	[self.get_tokenizer(do_lower_case=SCREAMING_SNAKE_CASE_    ,	add_bos_token=SCREAMING_SNAKE_CASE_							)]
          for tokenizer in tokenizers:
             with self.subTest(F'{tokenizer.__class__.__name__}'							):
                __lowerCamelCase:				int     =	"""Encode this."""
                __lowerCamelCase:				List[Any]     =	"""This one too please."""
                __lowerCamelCase:				Any     =	tokenizer.encode(SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_							)
                encoded_sequence += tokenizer.encode(SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_							)
                __lowerCamelCase:				List[Any]     =	tokenizer.encode_plus(
                    SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	add_special_tokens=SCREAMING_SNAKE_CASE_    ,	return_special_tokens_mask=SCREAMING_SNAKE_CASE_    ,	)
                __lowerCamelCase:				Tuple     =	encoded_sequence_dict["""input_ids"""]
                __lowerCamelCase:				Any     =	encoded_sequence_dict["""special_tokens_mask"""]
                self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	len(SCREAMING_SNAKE_CASE_							)							)

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



@require_tokenizers
class        a					(			unittest.TestCase   ):





       def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          __lowerCamelCase:				Tuple     =	AutoTokenizer.from_pretrained("""facebook/opt-350m"""    ,	from_slow=SCREAMING_SNAKE_CASE_							)
          __lowerCamelCase:				Tuple     =	"""A photo of a cat"""

          __lowerCamelCase:				List[str]     =	tokenizer.encode(
              SCREAMING_SNAKE_CASE_    ,	)
          self.assertEqual(SCREAMING_SNAKE_CASE_    ,	[2, 250, 1345, 9, 10, 4758]							)
          tokenizer.save_pretrained("""test_opt"""							)

          __lowerCamelCase:				Any     =	AutoTokenizer.from_pretrained("""./test_opt"""							)
          __lowerCamelCase:				Optional[Any]     =	tokenizer.encode(
              SCREAMING_SNAKE_CASE_    ,	)
          self.assertEqual(SCREAMING_SNAKE_CASE_    ,	[2, 250, 1345, 9, 10, 4758]							)





       def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
          __lowerCamelCase:				int     =	AutoTokenizer.from_pretrained("""facebook/opt-350m"""    ,	use_slow=SCREAMING_SNAKE_CASE_							)
          __lowerCamelCase:				Optional[int]     =	"""A photo of a cat"""

          __lowerCamelCase:				Optional[int]     =	tokenizer.encode(
              SCREAMING_SNAKE_CASE_    ,	)
          # Same as above
          self.assertEqual(SCREAMING_SNAKE_CASE_    ,	[2, 250, 1345, 9, 10, 4758]							)







       @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""							)
       def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
          __lowerCamelCase:				Dict     =	AutoTokenizer.from_pretrained("""facebook/opt-350m"""    ,	from_slow=SCREAMING_SNAKE_CASE_							)

          __lowerCamelCase:				str     =	"""bos"""
          __lowerCamelCase:				Optional[Any]     =	tokenizer.get_vocab()["""bos"""]

          __lowerCamelCase:				str     =	"""A photo of a cat"""
          __lowerCamelCase:				Optional[int]     =	tokenizer.encode(
              SCREAMING_SNAKE_CASE_    ,	)
          # We changed the bos token
          self.assertEqual(SCREAMING_SNAKE_CASE_    ,	[31957, 250, 1345, 9, 10, 4758]							)
          tokenizer.save_pretrained("""./tok"""							)
          __lowerCamelCase:				List[Any]     =	AutoTokenizer.from_pretrained("""./tok"""							)
          self.assertTrue(tokenizer.is_fast							)
          __lowerCamelCase:				int     =	tokenizer.encode(
              SCREAMING_SNAKE_CASE_    ,	)
          self.assertEqual(SCREAMING_SNAKE_CASE_    ,	[31957, 250, 1345, 9, 10, 4758]							)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


