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

lowerCAmelCase__     						=			logging.get_logger(__name__)
@add_end_docstrings(__UpperCamelCase						)
class     a_			(						__UpperCamelCase						):






 '''simple docstring'''






 def __init__(      self :  Tuple      ,   *lowercase__ :  Dict      ,   **lowercase__ :  Optional[int]):







        '''simple docstring'''



        super().__init__(*lowercase__      ,   **lowercase__)
        requires_backends(self      ,   'vision')
        self.check_model_type(lowercase__)




 def __call__(      self :  List[Any]      ,   lowercase__ :  Union[str, List[str], "Image.Image", List["Image.Image"]]      ,   **lowercase__ :  Dict):







        '''simple docstring'''



        return super().__call__(lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  Optional[int]      ,   **lowercase__ :  Tuple):







        '''simple docstring'''



        return {}, {}, {}




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Tuple):







        '''simple docstring'''



        lowerCAmelCase__					     = load_image(lowercase__)
        lowerCAmelCase__					     = image.size
        lowerCAmelCase__					     = self.image_processor(images=lowercase__      ,   return_tensors=self.framework)
        return model_inputs




 def 		__snake_case						(      self :  Optional[int]      ,   lowercase__ :  Dict):







        '''simple docstring'''



        lowerCAmelCase__					     = self.model(**lowercase__)
        return model_outputs




 def 		__snake_case						(      self :  List[Any]      ,   lowercase__ :  Any):







        '''simple docstring'''



        lowerCAmelCase__					     = model_outputs.predicted_depth
        lowerCAmelCase__					     = torch.nn.functional.interpolate(
            predicted_depth.unsqueeze(1)      ,   size=self.image_size[::-1]      ,   mode='bicubic'      ,   align_corners=lowercase__)
        lowerCAmelCase__					     = prediction.squeeze().cpu().numpy()
        lowerCAmelCase__					     = (output * 255 / np.max(lowercase__)).astype('uint8')
        lowerCAmelCase__					     = Image.fromarray(lowercase__)
        lowerCAmelCase__					     = {}
        lowerCAmelCase__					     = predicted_depth
        lowerCAmelCase__					     = depth
        return output_dict


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase__     						=			{
    'configuration_x_clip': [
        'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XCLIPConfig',
        'XCLIPTextConfig',
        'XCLIPVisionConfig',
    ],
    'processing_x_clip': ['XCLIPProcessor'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__     						=			[
        'XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
        'XCLIPModel',
        'XCLIPPreTrainedModel',
        'XCLIPTextModel',
        'XCLIPVisionModel',
    ]

if TYPE_CHECKING:
    from .configuration_x_clip import (
        XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
        XCLIPConfig,
        XCLIPTextConfig,
        XCLIPVisionConfig,
    )
    from .processing_x_clip import XCLIPProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_x_clip import (
            XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
            XCLIPModel,
            XCLIPPreTrainedModel,
            XCLIPTextModel,
            XCLIPVisionModel,
        )

else:
    import sys

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