



'''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				:    Any     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	List[str]     ,  *UpperCamelCase_				:	List[str]     ,  **UpperCamelCase_				:	Tuple					)  ->       Any:



						'''simple docstring'''





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




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



						'''simple docstring'''





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




					def 						__UpperCAmelCase     (  self				:	int     ,  **UpperCamelCase_				:	Dict					)  ->       Tuple:



						'''simple docstring'''





						return {}, {}, {}




					def 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	Tuple					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		load_image(UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		image.size
						_lowercase					:       Optional[Any]       =		self.image_processor(images=UpperCamelCase_     ,  return_tensors=self.framework					)
						return model_inputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Any:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self.model(**UpperCamelCase_					)
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Dict					)  ->       Dict:



						'''simple docstring'''





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





'''simple docstring'''

import os
from collections.abc import Iterator







def 						__UpperCamelCase				(							_lowercase = "."					) ->			Iterator[str]:
	for dir_path, dir_names, filenames in os.walk(_lowercase					):
		_lowercase					:       Optional[int]       =		[d for d in dir_names if d != 'scripts' and d[0] not in '._']
		for filename in filenames:
			if filename == "__init__.py":
				continue
			if os.path.splitext(_lowercase					)[1] in (".py", ".ipynb"):
				yield os.path.join(_lowercase,				_lowercase					).lstrip('./'					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[str]:
	return f'''{i * "  "}*''' if i else "\n##"







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			str:
	_lowercase					:       Optional[Any]       =		old_path.split(os.sep					)
	for i, new_part in enumerate(new_path.split(os.sep					)					):
		if (i + 1 > len(_lowercase					) or old_parts[i] != new_part) and new_part:
			print(f'''{md_prefix(_lowercase					)} {new_part.replace("_",				" "					).title()}'''					)
	return new_path







def 						__UpperCamelCase				(							_lowercase = "."					) ->			None:
	_lowercase					:       Dict       =		''
	for filepath in sorted(good_file_paths(_lowercase					)					):
		_lowercase  ,    _lowercase					:       Optional[Any]       =		os.path.split(_lowercase					)
		if filepath != old_path:
			_lowercase					:       Dict       =		print_path(_lowercase,				_lowercase					)
		_lowercase					:       Optional[int]       =		(filepath.count(os.sep					) + 1) if filepath else 0
		_lowercase					:       Dict       =		f'''{filepath}/{filename}'''.replace(' ',				'%20'					)
		_lowercase					:       Optional[int]       =		os.path.splitext(filename.replace('_',				' '					).title()					)[0]
		print(f'''{md_prefix(_lowercase					)} [{filename}]({url})'''					)


if __name__ == "__main__":
		print_directory_md('''.''')
