


'''simple docstring'''







from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

a__  :					Optional[int]        =       logging.get_logger(__name__)
@add_end_docstrings(a__   )
class 					lowercase_     (    a__   ):


    def __init__(     self  ,					*a  ,					**a    ):
          super().__init__(*a  ,					**a    )
          requires_backends(self  ,					"vision"    )
          self.check_model_type(a    )


    def __call__(     self  ,					a  ,					**a    ):
          return super().__call__(a  ,					**a    )


    def    __a							(     self  ,					**a    ):
          return {}, {}, {}


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				load_image(a    )
          UpperCamelCase__										=				image.size
          UpperCamelCase__										=				self.image_processor(images=a  ,					return_tensors=self.framework    )
          return model_inputs


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				self.model(**a    )
          return model_outputs


    def    __a							(     self  ,					a    ):
          UpperCamelCase__										=				model_outputs.predicted_depth
          UpperCamelCase__										=				torch.nn.functional.interpolate(
              predicted_depth.unsqueeze(1    )  ,					size=self.image_size[::-1]  ,					mode="bicubic"  ,					align_corners=a    )
          UpperCamelCase__										=				prediction.squeeze().cpu().numpy()
          UpperCamelCase__										=				(output * 2_55 / np.max(a    )).astype("uint8"    )
          UpperCamelCase__										=				Image.fromarray(a    )
          UpperCamelCase__										=				{}
          UpperCamelCase__										=				predicted_depth
          UpperCamelCase__										=				depth
          return output_dict













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    '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

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






