
'''simple docstring'''

import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin


class 					a_					(lowerCAmelCase_							,     unittest.TestCase      ):
       __lowerCAmelCase      :			Optional[Any]      = BertweetTokenizer
       __lowerCAmelCase      :			List[str]      = False




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

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

         _lowerCAmelCase    :					Tuple							=   os.path.join(self.tmpdirname				,				VOCAB_FILES_NAMES["""vocab_file"""]					)
         _lowerCAmelCase    :					Tuple							=   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(_snake_case					)					)




       def 							__UpperCamelCase    (       self				,				**snake_case_					):
         kwargs.update(self.special_tokens_map					)
         return BertweetTokenizer.from_pretrained(self.tmpdirname				,				**_snake_case					)




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					List[str]							=   'I am VinAI Research'
         _lowerCAmelCase    :					str							=   'I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
         return input_text, output_text







       def 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   BertweetTokenizer(self.vocab_file				,				self.merges_file				,				**self.special_tokens_map					)
         _lowerCAmelCase    :					List[str]							=   'I am VinAI Research'
         _lowerCAmelCase    :					str							=   'I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
         _lowerCAmelCase    :					Optional[int]							=   tokenizer.tokenize(_snake_case					)
         self.assertListEqual(_snake_case				,				_snake_case					)

         _lowerCAmelCase    :					Tuple							=   tokens + [tokenizer.unk_token]

         _lowerCAmelCase    :					Dict							=   [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case					)				,				_snake_case					)




'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCamelCase_            =    {"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPTJForCausalLM""",
         """GPTJForQuestionAnswering""",
         """GPTJForSequenceClassification""",
         """GPTJModel""",
         """GPTJPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """TFGPTJForCausalLM""",
         """TFGPTJForQuestionAnswering""",
         """TFGPTJForSequenceClassification""",
         """TFGPTJModel""",
         """TFGPTJPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase_            =    [
         """FlaxGPTJForCausalLM""",
         """FlaxGPTJModel""",
         """FlaxGPTJPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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


