

"""simple docstring"""
import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 			lowerCamelCase  (					lowerCAmelCase__				,				unittest.TestCase      ):


  '''simple docstring'''







  SCREAMING_SNAKE_CASE							=     BertweetTokenizer
  SCREAMING_SNAKE_CASE							=     False



  def    _a			(self  ):


        """simple docstring"""

        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase__ :       Optional[int]						=						["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
        UpperCAmelCase__ :       Any						=						dict(zip(_lowerCamelCase       ,					range(len(_lowerCamelCase  )  )  )  )
        UpperCAmelCase__ :       int						=						["""#version: 0.2""", """a m</w>"""]
        UpperCAmelCase__ :       Optional[Any]						=						{"""unk_token""": """<unk>"""}

        UpperCAmelCase__ :       Optional[Any]						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
        UpperCAmelCase__ :       List[Any]						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""merges_file"""]  )
        with open(self.vocab_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
              for token in vocab_tokens:
                    fp.write(F"""{token} {vocab_tokens[token]}\n"""  )
        with open(self.merges_file       ,					"""w"""       ,					encoding="""utf-8"""  ) as fp:
              fp.write("""\n""".join(_lowerCamelCase  )  )



  def    _a			(self       ,					**_lowerCamelCase  ):


        """simple docstring"""

        kwargs.update(self.special_tokens_map  )
        return BertweetTokenizer.from_pretrained(self.tmpdirname       ,					**_lowerCamelCase  )



  def    _a			(self       ,					_lowerCamelCase  ):


        """simple docstring"""

        UpperCAmelCase__ :       Tuple						=						"""I am VinAI Research"""
        UpperCAmelCase__ :       Union[str, Any]						=						"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
        return input_text, output_text




  def    _a			(self  ):


        """simple docstring"""

        UpperCAmelCase__ :       Any						=						BertweetTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map  )
        UpperCAmelCase__ :       Any						=						"""I am VinAI Research"""
        UpperCAmelCase__ :       Dict						=						"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
        UpperCAmelCase__ :       Optional[Any]						=						tokenizer.tokenize(_lowerCamelCase  )
        self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )

        UpperCAmelCase__ :       Union[str, Any]						=						tokens + [tokenizer.unk_token]

        UpperCAmelCase__ :       List[str]						=						[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase  )       ,					_lowerCamelCase  )




"""simple docstring"""
from typing import TYPE_CHECKING

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


_A						=     {
    """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
    """tokenization_gpt2""": ["""GPT2Tokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""GPT2TokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """GPT2DoubleHeadsModel""",
        """GPT2ForQuestionAnswering""",
        """GPT2ForSequenceClassification""",
        """GPT2ForTokenClassification""",
        """GPT2LMHeadModel""",
        """GPT2Model""",
        """GPT2PreTrainedModel""",
        """load_tf_weights_in_gpt2""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     [
        """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFGPT2DoubleHeadsModel""",
        """TFGPT2ForSequenceClassification""",
        """TFGPT2LMHeadModel""",
        """TFGPT2MainLayer""",
        """TFGPT2Model""",
        """TFGPT2PreTrainedModel""",
    ]

try:
    if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""TFGPT2Tokenizer"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]

if TYPE_CHECKING:
    from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
    from .tokenization_gpta import GPTaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_fast import GPTaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )

    try:
        if not is_keras_nlp_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpta_tf import TFGPTaTokenizer

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
    import sys

    _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

