





"""simple docstring"""


import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[str]						  =       BioGptTokenizer
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       False

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					List[str]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _UpperCamelCase   :						Optional[Any]    =							[
            "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>",
        ]
        _UpperCamelCase   :						List[Any]    =							dict(zip(__a       , range(len(__a						)						)						)						)
        _UpperCamelCase   :						int    =							["l o 123", "lo w 1456", "e r</w> 1789", ""]

        _UpperCamelCase   :						str    =							os.path.join(self.tmpdirname       , VOCAB_FILES_NAMES["vocab_file"]						)
        _UpperCamelCase   :						Optional[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  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							str						)		->					Optional[int]:
        _UpperCamelCase   :						int    =							"lower newer"
        _UpperCamelCase   :						List[Any]    =							"lower newer"
        return input_text, output_text

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Union[str, Any]:
        _UpperCamelCase   :						str    =							BioGptTokenizer(self.vocab_file       , self.merges_file						)

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

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


  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Dict:
        _UpperCamelCase   :						Tuple    =							BioGptTokenizer.from_pretrained("microsoft/biogpt"						)

        _UpperCamelCase   :						List[Any]    =							tokenizer.encode("sequence builders"       , add_special_tokens=__a						)
        _UpperCamelCase   :						List[str]    =							tokenizer.encode("multi-sequence build"       , add_special_tokens=__a						)

        _UpperCamelCase   :						Dict    =							tokenizer.build_inputs_with_special_tokens(__a						)
        _UpperCamelCase   :						Optional[int]    =							tokenizer.build_inputs_with_special_tokens(__a       , __a						)

        self.assertTrue(encoded_sentence == [2] + text						)
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a						)







"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__				:Dict						  =       "AutoImageProcessor"
  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "AutoTokenizer"

  def __init__(		self :							Dict       , __a :							Tuple       , __a :							str						)		->					Optional[Any]:
        super().__init__(__a       , __a						)
        _UpperCamelCase   :						Tuple    =							self.image_processor

  def __call__(		self :							Tuple       , __a :							Tuple=None       , __a :							str=None       , __a :							Any=None       , **__a :							Optional[Any]						)		->					Union[str, Any]:

        if text is None and images is None:
              raise ValueError("You have to specify either text or images. Both cannot be none."						)

        if text is not None:
              _UpperCamelCase   :						List[str]    =							self.tokenizer(__a       , return_tensors=__a       , **__a						)

        if images is not None:
              _UpperCamelCase   :						str    =							self.image_processor(__a       , return_tensors=__a       , **__a						)

        if text is not None and images is not None:
              _UpperCamelCase   :						Optional[int]    =							image_features.pixel_values
              return encoding
        elif text is not None:
              return encoding
        else:
              return BatchEncoding(data=dict(**__a						)       , tensor_type=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , *__a :							Optional[int]       , **__a :							Optional[int]						)		->					List[Any]:
        return self.tokenizer.batch_decode(*__a       , **__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							int       , *__a :							Any       , **__a :							List[Any]						)		->					Tuple:
        return self.tokenizer.decode(*__a       , **__a						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Any:
        return ["input_ids", "attention_mask", "pixel_values"]
