'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase				:		List[str]       			=		{
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Any       			=		["CamembertTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		str       			=		["CamembertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		List[str]       			=		[
         "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CamembertForCausalLM",
         "CamembertForMaskedLM",
         "CamembertForMultipleChoice",
         "CamembertForQuestionAnswering",
         "CamembertForSequenceClassification",
         "CamembertForTokenClassification",
         "CamembertModel",
         "CamembertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCamelCase				:		Optional[int]       			=		[
         "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFCamembertForCausalLM",
         "TFCamembertForMaskedLM",
         "TFCamembertForMultipleChoice",
         "TFCamembertForQuestionAnswering",
         "TFCamembertForSequenceClassification",
         "TFCamembertForTokenClassification",
         "TFCamembertModel",
         "TFCamembertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert import CamembertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_camembert_fast import CamembertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_camembert import (
              CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              CamembertForCausalLM,
              CamembertForMaskedLM,
              CamembertForMultipleChoice,
              CamembertForQuestionAnswering,
              CamembertForSequenceClassification,
              CamembertForTokenClassification,
              CamembertModel,
              CamembertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_camembert import (
              TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFCamembertForCausalLM,
              TFCamembertForMaskedLM,
              TFCamembertForMultipleChoice,
              TFCamembertForQuestionAnswering,
              TFCamembertForSequenceClassification,
              TFCamembertForTokenClassification,
              TFCamembertModel,
              TFCamembertPreTrainedModel,
          )

else:
     import sys

     _UpperCamelCase				:		Any       			=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image

class   _lowercase(     unittest.TestCase   ,_lowerCamelCase				):



   """simple docstring"""





   def 			snake_case	(							self:    Optional[int]	):
    __UpperCAmelCase         =					load_tool('image-captioning'	)
    self.tool.setup()
    __UpperCAmelCase         =					load_tool('image-captioning'			,remote=a	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Optional[Any]	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)





   def 			snake_case	(							self:    Tuple	):
    __UpperCAmelCase         =					Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'	)	) / '000000039769.png'	)
    __UpperCAmelCase         =					self.remote_tool(image=a	)
    self.assertEqual(a			,'two cats sleeping on a couch'	)



