

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							:  Tuple   	=						logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase_	)
class   __lowercase				( lowerCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     str   ,						*lowerCAmelCase__  :     Any   ,						**lowerCAmelCase__  :     List[Any]):
									super().__init__(*lowerCAmelCase__   ,						**lowerCAmelCase__)
									requires_backends(self   ,						"vision")
									self.check_model_type(lowerCAmelCase__)



			def __call__( self  :     Tuple   ,						lowerCAmelCase__  :     Union[str, List[str], "Image.Image", List["Image.Image"]]   ,						**lowerCAmelCase__  :     List[Any]):
									return super().__call__(lowerCAmelCase__   ,						**lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						**lowerCAmelCase__  :     Union[str, Any]):
									return {}, {}, {}



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       load_image(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image.size
									SCREAMING_SNAKE_CASE_: Any  		=       self.image_processor(images=lowerCAmelCase__   ,						return_tensors=self.framework)
									return model_inputs



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: str  		=       self.model(**lowerCAmelCase__)
									return model_outputs




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











"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




