


"""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 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					Any													=OpenAIGPTTokenizer
     snake_case__	:					Tuple													=OpenAIGPTTokenizerFast
     snake_case__	:					Any													=True
     snake_case__	:					str													=False

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           lowerCamelCase					:					Optional[int]        =	[
               """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					:					Optional[Any]        =	dict(zip(__a     ,		range(len(__a	)	)	)	)
           lowerCamelCase					:					Optional[int]        =	["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

           lowerCamelCase					:					List[Any]        =	os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]	)
           lowerCamelCase					:					Union[str, Any]        =	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 		a__						(	self:						Dict     ,		__a:						Dict	)->    str:
           return "lower newer", "lower newer"

     def 		a__						(	self:						int	)->    int:
           lowerCamelCase					:					List[Any]        =	OpenAIGPTTokenizer(self.vocab_file     ,		self.merges_file	)

           lowerCamelCase					:					str        =	"""lower"""
           lowerCamelCase					:					str        =	["""low""", """er</w>"""]
           lowerCamelCase					:					List[Any]        =	tokenizer.tokenize(__a	)
           self.assertListEqual(__a     ,		__a	)

           lowerCamelCase					:					Union[str, Any]        =	tokens + ["""<unk>"""]
           lowerCamelCase					:					Tuple        =	[14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	)     ,		__a	)

     def 		a__						(	self:						List[str]     ,		__a:						Optional[int]=15	)->    Dict:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'	):
                       lowerCamelCase					:					int        =	self.rust_tokenizer_class.from_pretrained(__a     ,		**__a	)

                       # Simple input
                       lowerCamelCase					:					List[Any]        =	"""This is a simple input"""
                       lowerCamelCase					:					Dict        =	["""This is a simple input 1""", """This is a simple input 2"""]
                       lowerCamelCase					:					Any        =	("""This is a simple input""", """This is a pair""")
                       lowerCamelCase					:					Any        =	[
                           ("""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:						Optional[Any]	)->    List[str]:
           pass


@require_ftfy
@require_spacy
@require_tokenizers
class 			A__	(						__lowercase):


     """simple docstring"""
     pass










"""simple docstring"""


from typing import TYPE_CHECKING

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


__lowerCamelCase      :Optional[Any]	    =		{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Union[str, Any]	    =		[
        'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
        'EncodecModel',
        'EncodecPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_encodec import (
        ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
        EncodecConfig,
    )
    from .feature_extraction_encodec import EncodecFeatureExtractor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encodec import (
            ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
            EncodecModel,
            EncodecPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




