

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

__UpperCAmelCase						=					logging.get_logger(__name__)



@add_end_docstrings(__A					)
class SCREAMING_SNAKE_CASE      (    __A					):
  """simple docstring"""






  def __init__(     self					:       List[Any]			,				*lowerCAmelCase					:       Optional[Any]			,				**lowerCAmelCase					:       Optional[Any]       )				->      List[Any]:






     """simple docstring"""







     super().__init__(*lowerCAmelCase			,				**lowerCAmelCase       )
     requires_backends(self			,				"""vision"""       )
     self.check_model_type(lowerCAmelCase       )







  def __call__(     self					:       int			,				lowerCAmelCase					:       Union[str, List[str], "Image.Image", List["Image.Image"]]			,				**lowerCAmelCase					:       Optional[int]       )				->      List[Any]:






     """simple docstring"""







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







  def 			SCREAMING_SNAKE_CASE       (     self					:       Tuple			,				**lowerCAmelCase					:       Any       )				->      Optional[Any]:






     """simple docstring"""







     return {}, {}, {}







  def 			SCREAMING_SNAKE_CASE       (     self					:       int			,				lowerCAmelCase					:       List[Any]       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     str          =		load_image(lowerCAmelCase       )
     __lowerCAmelCase    :     Union[str, Any]          =		image.size
     __lowerCAmelCase    :     List[str]          =		self.image_processor(images=lowerCAmelCase			,				return_tensors=self.framework       )
     return model_inputs







  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[int]			,				lowerCAmelCase					:       Any       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     Dict          =		self.model(**lowerCAmelCase       )
     return model_outputs







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[str]			,				lowerCAmelCase					:       Tuple       )				->      List[Any]:






     """simple docstring"""







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





from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap

def 			lowerCamelCase__			(       _a):
	return getitem, k

def 			lowerCamelCase__			(       _a				,      _a):
	return setitem, k, v

def 			lowerCamelCase__			(       _a):
	return delitem, k

def 			lowerCamelCase__			(       _a				,      _a				,      *_a):
	try:
		return fun(_a				,      *_a), None
	except Exception as e:
		return None, e


a_      =     (
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
)

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_a', 'val_b'),
]

a_      =     [
    _set('key_a', 'val_a'),
    _set('key_b', 'val_b'),
    _del('key_a'),
    _del('key_b'),
    _set('key_a', 'val_a'),
    _del('key_a'),
]

a_      =     [
    _get('key_a'),
    _del('key_a'),
    _set('key_a', 'val_a'),
    _del('key_a'),
    _del('key_a'),
    _get('key_a'),
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a_      =     [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set('key_a', 'val_b'),
]

@pytest.mark.parametrize(
    "operations"				,      (
        pytest.param(_add_items				,      id="add items"),
        pytest.param(_overwrite_items				,      id="overwrite items"),
        pytest.param(_delete_items				,      id="delete items"),
        pytest.param(_access_absent_items				,      id="access absent items"),
        pytest.param(_add_with_resize_up				,      id="add with resize up"),
        pytest.param(_add_with_resize_down				,      id="add with resize down"),
    )				,      )
def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Dict					  =							HashMap(initial_block_size=4)
	SCREAMING_SNAKE_CASE :   List[str]					  =							{}
	for _, (fun, *args) in enumerate(_a):
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							_run_operation(_a				,      _a				,      *_a)
		SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[Any]					  =							_run_operation(_a				,      _a				,      *_a)
		assert my_res == py_res
		assert str(_a) == str(_a)
		assert set(_a) == set(_a)
		assert len(_a) == len(_a)
		assert set(my.items()) == set(py.items())


def 			lowerCamelCase__			(       ):
	def is_public(_a) -> bool:
		return not name.startswith("_")

	SCREAMING_SNAKE_CASE :   List[str]					  =							{name for name in dir({}) if is_public(_a)}
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							{name for name in dir(HashMap()) if is_public(_a)}

	assert dict_public_names > hash_public_names