

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[Any]   ,   *a				:			Optional[Any]   ,   **a				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.model(**a						)
				return model_outputs






			def 				__UpperCamelCase			(      self				:			List[str]   ,   a				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				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=a						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							prediction.squeeze().cpu().numpy()
				SCREAMING_SNAKE_CASE :   List[Any]					  =							(output * 255 / np.max(a						)).astype("uint8"						)
				SCREAMING_SNAKE_CASE :   Any					  =							Image.fromarray(a						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							{}
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							predicted_depth
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							depth
				return output_dict







'''simple docstring'''



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if not all(x.isalpha() for x in string     ):
      raise ValueError('String must only contain alphabetic characters.'     )

   __a					  =     sorted(string.lower()     )
   return len(SCREAMING_SNAKE_CASE__     ) == len(set(SCREAMING_SNAKE_CASE__     )     )


if __name__ == "__main__":
 __UpperCamelCase   :							int         =   input("""Enter a string """).strip()

 __UpperCamelCase   :							Tuple         =   is_isogram(input_str)
 print(f"""{input_str} is {"an" if isogram else "not an"} isogram.""")