

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case         =  {
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case         =  [
         """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TimeSeriesTransformerForPrediction""",
         """TimeSeriesTransformerModel""",
         """TimeSeriesTransformerPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_time_series_transformer import (
         TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
         TimeSeriesTransformerConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_time_series_transformer import (
              TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TimeSeriesTransformerForPrediction,
              TimeSeriesTransformerModel,
              TimeSeriesTransformerPreTrainedModel,
          )

else:
     import sys

     _snake_case         =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









'''simple docstring'''
import os
import sys
import unittest


_snake_case         =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
_snake_case         =  os.path.join(git_repo_path, 'src', 'transformers')

_snake_case         =  '\n{0} = None\n'

_snake_case         =  '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


_snake_case         =  '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'


class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"  )
      self.assertIsNone(_UpperCamelCase  )

      _lowercase      :				Dict      					=       find_backend("    if not is_tokenizers_available():"  )
      self.assertEqual(_UpperCamelCase      ,    "tokenizers"  )

      _lowercase      :				str      					=       find_backend("    if not is_tensorflow_text_available():"  )
      self.assertEqual(_UpperCamelCase      ,    "tensorflow_text"  )

      _lowercase      :				Optional[Any]      					=       find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tokenizers"  )

      _lowercase      :				List[str]      					=       find_backend(
          "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tensorflow_text"  )

      _lowercase      :				Optional[Any]      					=       find_backend(
          "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"  )
      self.assertEqual(_UpperCamelCase      ,    "sentencepiece_and_tokenizers_and_vision"  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("torch"      ,    _UpperCamelCase  )
      self.assertIn("tensorflow_text"      ,    _UpperCamelCase  )
      self.assertIn("sentencepiece_and_tokenizers"      ,    _UpperCamelCase  )

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("BertModel"      ,    objects["torch"]  )
      self.assertIn("TFBertModel"      ,    objects["tf"]  )
      self.assertIn("FlaxBertModel"      ,    objects["flax"]  )
      self.assertIn("BertModel"      ,    objects["torch"]  )
      self.assertIn("TFBertTokenizer"      ,    objects["tensorflow_text"]  )
      self.assertIn("convert_slow_tokenizer"      ,    objects["sentencepiece_and_tokenizers"]  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				int      					=       create_dummy_object("CONSTANT"      ,    "'torch'"  )
      self.assertEqual(_UpperCamelCase      ,    "\nCONSTANT = None\n"  )

      _lowercase      :				Optional[Any]      					=       create_dummy_object("function"      ,    "'torch'"  )
      self.assertEqual(
          _UpperCamelCase      ,    "\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"  )

      _lowercase      :				Union[str, Any]      					=       "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n"
      _lowercase      :				List[str]      					=       create_dummy_object("FakeClass"      ,    "'torch'"  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n"
      _lowercase      :				str      					=       create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}  )
      self.assertEqual(dummy_files["torch"]      ,    _UpperCamelCase  )






