

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_						     =    logging.get_logger(__name__)






@add_end_docstrings(__A   )
class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      *a							: List[str]      ,      **a							: str	)							->       int:

				"""simple docstring"""

				super().__init__(*a      ,      **a	)
				requires_backends(self      ,      "vision"	)
				self.check_model_type(a	)
			def __call__(			self							: List[str]      ,      a							: Union[str, List[str], "Image.Image", List["Image.Image"]]      ,      **a							: int	)							->       int:

				"""simple docstring"""

				return super().__call__(a      ,      **a	)
			def 							__UpperCamelCase (			self							: Optional[int]      ,      **a							: Dict	)							->       Any:

				"""simple docstring"""

				return {}, {}, {}
			def 							__UpperCamelCase (			self							: Any      ,      a							: List[str]	)							->       str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       load_image(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       image.size
				SCREAMING_SNAKE_CASE   :      Any				     =       self.image_processor(images=a      ,      return_tensors=self.framework	)
				return model_inputs
			def 							__UpperCamelCase (			self							: Any      ,      a							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.model(**a	)
				return model_outputs






			def 							__UpperCamelCase (			self							: str      ,      a							: Any	)							->       Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       model_outputs.predicted_depth
				SCREAMING_SNAKE_CASE   :      int				     =       torch.nn.functional.interpolate(
				    predicted_depth.unsqueeze(1	)      ,      size=self.image_size[::-1]      ,      mode="bicubic"      ,      align_corners=a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       prediction.squeeze().cpu().numpy()
				SCREAMING_SNAKE_CASE   :      Any				     =       (output * 255 / np.max(a	)).astype("uint8"	)
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       Image.fromarray(a	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       {}
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       predicted_depth
				SCREAMING_SNAKE_CASE   :      int				     =       depth
				return output_dict


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_						     =    {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    ['BridgeTowerImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'BridgeTowerForContrastiveLearning',
			    'BridgeTowerForImageAndTextRetrieval',
			    'BridgeTowerForMaskedLM',
			    'BridgeTowerModel',
			    'BridgeTowerPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_bridgetower import (
			    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    BridgeTowerConfig,
			    BridgeTowerTextConfig,
			    BridgeTowerVisionConfig,
			)
			from .processing_bridgetower import BridgeTowerProcessor

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_bridgetower import BridgeTowerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bridgetower import (
						    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    BridgeTowerForContrastiveLearning,
						    BridgeTowerForImageAndTextRetrieval,
						    BridgeTowerForMaskedLM,
						    BridgeTowerModel,
						    BridgeTowerPreTrainedModel,
						)


else:
			import sys

			a_						     =    _LazyModule(__name__, globals()['__file__'], _import_structure)