



"""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   ):
    lowercase__      =   BioGptTokenizer
    lowercase__      =   False

    def 					_UpperCAmelCase	(				self			:				Union[str, Any]):







      """simple docstring"""







      super().setUp()

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

      lowercase_								= os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""vocab_file"""])
      lowercase_								= os.path.join(self.tmpdirname ,  VOCAB_FILES_NAMES["""merges_file"""])
      with open(self.vocab_file ,  """w""") as fp:
        fp.write(json.dumps(lowerCAmelCase_))
      with open(self.merges_file ,  """w""") as fp:
        fp.write("""\n""".join(lowerCAmelCase_))

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				str):







      """simple docstring"""







      lowercase_								= """lower newer"""
      lowercase_								= """lower newer"""
      return input_text, output_text

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= BioGptTokenizer(self.vocab_file ,  self.merges_file)

      lowercase_								= """lower"""
      lowercase_								= ["""low""", """er</w>"""]
      lowercase_								= tokenizer.tokenize(lowerCAmelCase_)
      self.assertListEqual(lowerCAmelCase_ ,  lowerCAmelCase_)

      lowercase_								= tokens + ["""<unk>"""]
      lowercase_								= [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_) ,  lowerCAmelCase_)




    @slow
    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= BioGptTokenizer.from_pretrained("""microsoft/biogpt""")

      lowercase_								= tokenizer.encode("""sequence builders""" ,  add_special_tokens=lowerCAmelCase_)
      lowercase_								= tokenizer.encode("""multi-sequence build""" ,  add_special_tokens=lowerCAmelCase_)

      lowercase_								= tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_)
      lowercase_								= tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ ,  lowerCAmelCase_)

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






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

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


UpperCAmelCase						:		Any			= {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Any			= ["BlipImageProcessor"]


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		Optional[int]			= [
           "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlipModel",
           "BlipPreTrainedModel",
           "BlipForConditionalGeneration",
           "BlipForQuestionAnswering",
           "BlipVisionModel",
           "BlipTextModel",
           "BlipForImageTextRetrieval",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFBlipModel",
           "TFBlipPreTrainedModel",
           "TFBlipForConditionalGeneration",
           "TFBlipForQuestionAnswering",
           "TFBlipVisionModel",
           "TFBlipTextModel",
           "TFBlipForImageTextRetrieval",
       ]

if TYPE_CHECKING:
       from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
       from .processing_blip import BlipProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_blip import BlipImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blip import (
                  BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlipForConditionalGeneration,
                  BlipForImageTextRetrieval,
                  BlipForQuestionAnswering,
                  BlipModel,
                  BlipPreTrainedModel,
                  BlipTextModel,
                  BlipVisionModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blip import (
                  TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFBlipForConditionalGeneration,
                  TFBlipForImageTextRetrieval,
                  TFBlipForQuestionAnswering,
                  TFBlipModel,
                  TFBlipPreTrainedModel,
                  TFBlipTextModel,
                  TFBlipVisionModel,
              )

else:
       import sys

       UpperCAmelCase						:		Any			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

