repo_description: Probabilistic time series modeling in Python
repo_topics: artificial-intelligence aws data-science deep-learning forecasting machine-learning mxnet neural-networks pytorch sagemaker time-series time-series-forecasting time-series-prediction timeseries torch
code: # Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License").
  # You may not use this file except in compliance with the License.
  # A copy of the License is located at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # or in the "license" file accompanying this file. This file is distributed
  # on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
  # express or implied. See the License for the specific language governing
  # permissions and limitations under the License.
  
  
  """
  This modules contains pydantic imports, which are used throughout the codebase.
  """
  
  from pydantic import __version__
  
  PYDANTIC_V2 = __version__.startswith("2")
  
  if PYDANTIC_V2:
      import pydantic.v1 as pydantic
      from pydantic.v1 import (
          BaseConfig,
          BaseModel,
          create_model,
          root_validator,
          PositiveInt,
          PrivateAttr,
          Field,
          parse_obj_as,
          PositiveFloat,
          BaseSettings,
      )
      from pydantic.v1.error_wrappers import ValidationError, display_errors
      from pydantic.v1.utils import deep_update
      from pydantic.v1.dataclasses import dataclass
  else:
      import pydantic  # type: ignore[no-redef]
      from pydantic import (  # type: ignore[no-redef, assignment]
          BaseConfig,
          BaseModel,
          create_model,
          root_validator,
          PositiveInt,
          PrivateAttr,
          Field,
          parse_obj_as,
          PositiveFloat,
          BaseSettings,
      )
      from pydantic.error_wrappers import ValidationError, display_errors  # type: ignore[no-redef]
      from pydantic.utils import deep_update  # type: ignore[no-redef]
      from pydantic.dataclasses import dataclass  # type: ignore[no-redef]
  
  
  __all__ = [
      "BaseConfig",
      "BaseModel",
      "BaseSettings",
      "Field",
      "PositiveFloat",
      "PositiveInt",
      "PrivateAttr",
      "ValidationError",
      "__version__",
      "create_model",
      "dataclass",
      "deep_update",
      "display_errors",
      "parse_obj_as",
      "pydantic",
      "root_validator",
  ]
  