





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:

    for param in module.parameters():
        lowerCamelCase__					:   int      				=	False


def  SCREAMING_SNAKE_CASE   ( )    ->  List[str]:
    lowerCamelCase__					:   str      				=	'cuda' if torch.cuda.is_available() else 'cpu'
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        lowerCamelCase__					:   List[Any]      				=	'mps'
    if device == "mps":
        print(
            'WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'
            ' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'
            ' with generations.'				)
    return device


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[str]:
    lowerCamelCase__					:   str      				=	plt.imshow(UpperCAmelCase__				)
    fig.axes.get_xaxis().set_visible(UpperCAmelCase__				)
    fig.axes.get_yaxis().set_visible(UpperCAmelCase__				)
    plt.show()


def  SCREAMING_SNAKE_CASE   ( )    ->  List[Any]:
    lowerCamelCase__					:   Optional[Any]      				=	datetime.now()
    lowerCamelCase__					:   int      				=	current_time.strftime('%H:%M:%S'				)
    return timestamp












from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase       : List[str]			     =       {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCAmelCase       : List[Any]			     =       [
        """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """AutoformerForPrediction""",
        """AutoformerModel""",
        """AutoformerPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_autoformer import (
        AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
        AutoformerConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_autoformer import (
            AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            AutoformerForPrediction,
            AutoformerModel,
            AutoformerPreTrainedModel,
        )

else:
    import sys

    _UpperCAmelCase       : Dict			     =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





