






'''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

UpperCamelCase							=logging.get_logger(__name__)




@add_end_docstrings(_SCREAMING_SNAKE_CASE						)
class     A    (      _SCREAMING_SNAKE_CASE						):






 """simple docstring"""





 def __init__(							self			,     *__lowerCAmelCase			,     **__lowerCAmelCase ):
       super().__init__(*A_			,     **A_ )
       requires_backends(self			,     """vision""" )
       self.check_model_type(A_ )
 def __call__(							self			,     __lowerCAmelCase			,     **__lowerCAmelCase ):
       return super().__call__(A_			,     **A_ )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return {}, {}, {}
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							load_image(A_ )
       UpperCamelCase_				:   List[Any]					    =							image.size
       UpperCamelCase_				:   int					    =							self.image_processor(images=A_			,     return_tensors=self.framework )
       return model_inputs
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							self.model(**A_ )
       return model_outputs





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














'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase							=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase							=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase							=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase							=re.compile(R"(_{2,})")

UpperCamelCase							=R"^\w+(\.\w+)*$"

UpperCamelCase							=R"<>:/\|?*"



def 							snake_case					(     a_							: str	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							_uppercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      UpperCamelCase_				:   List[Any]					    =							_lowercase_uppercase_re.sub(r"""\1_\2"""    ,   a_	)
      return name.lower()



def 							snake_case					(     a_							: Any	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							_single_underscore_re.split(a_	)
      UpperCamelCase_				:   Tuple					    =							[_multiple_underscores_re.split(a_	) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_	) if n != """"""	)



def 							snake_case					(     a_							: Optional[int]	)       ->				str:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      return camelcase_to_snakecase(a_	)



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Optional[Any]:





      """simple docstring"""

      if os.path.basename(a_	) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"	)
      if not re.match(_split_re    ,   a_	):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."	)
      return f"{filename_prefix_for_name(a_	)}-{split}"



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: int    ,   a_							: List[Any]    ,   a_							: Union[str, Any]=None	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							filename_prefix_for_split(a_    ,   a_	)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_				:   str					    =							os.path.join(a_    ,   a_	)
      return f"{filepath}*"



def 							snake_case					(     a_							: Tuple    ,   a_							: Any    ,   a_							: Optional[int]    ,   a_							: Union[str, Any]=None    ,   a_							: Optional[Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							filename_prefix_for_split(a_    ,   a_	)
      UpperCamelCase_				:   Dict					    =							os.path.join(a_    ,   a_	)

      if shard_lengths:
            UpperCamelCase_				:   Optional[int]					    =							len(a_	)
            UpperCamelCase_				:   Tuple					    =							[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_	)]
            if filetype_suffix:
                  UpperCamelCase_				:   Tuple					    =							[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_				:   Dict					    =							prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






