





'''simple docstring'''


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD


def 	__a(SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





    def wrapper(*SCREAMING_SNAKE_CASE_  :					Dict		,       **SCREAMING_SNAKE_CASE_  :					Dict    ):
        _lowerCAmelCase				=      timeit.default_timer()
        _lowerCAmelCase				=      func(*SCREAMING_SNAKE_CASE_		,       **SCREAMING_SNAKE_CASE_    )
        _lowerCAmelCase				=      timeit.default_timer() - starttime
        return delta

    _lowerCAmelCase				=      func.__name__

    return wrapper


def 	__a(SCREAMING_SNAKE_CASE_  :					dict		,       SCREAMING_SNAKE_CASE_  :					List[Any]=100		,       SCREAMING_SNAKE_CASE_  :					List[str]=None    ):



    '''simple docstring'''





    _lowerCAmelCase				=      []
    _lowerCAmelCase				=      seq_shapes or {}
    for i in range(SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      {}
        for col_id, (k, v) in enumerate(features.items()    ):
            if isinstance(SCREAMING_SNAKE_CASE_		,       _ArrayXD    ):
                _lowerCAmelCase				=      np.random.rand(*v.shape    ).astype(v.dtype    )
            elif isinstance(SCREAMING_SNAKE_CASE_		,       datasets.Value    ):
                if v.dtype == "string":
                    _lowerCAmelCase				=      "The small grey turtle was surprisingly fast when challenged."
                else:
                    _lowerCAmelCase				=      np.random.randint(10		,       size=1    ).astype(v.dtype    ).item()
            elif isinstance(SCREAMING_SNAKE_CASE_		,       datasets.Sequence    ):
                while isinstance(SCREAMING_SNAKE_CASE_		,       datasets.Sequence    ):
                    _lowerCAmelCase				=      v.feature
                _lowerCAmelCase				=      seq_shapes[k]
                _lowerCAmelCase				=      np.random.rand(*SCREAMING_SNAKE_CASE_    ).astype(v.dtype    )
            _lowerCAmelCase				=      data

        dummy_data.append((i, example)    )

    return dummy_data





def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					Union[str, Any]		,       SCREAMING_SNAKE_CASE_  :					Tuple=100		,       SCREAMING_SNAKE_CASE_  :					Dict=None    ):



    '''simple docstring'''





    _lowerCAmelCase				=      generate_examples(SCREAMING_SNAKE_CASE_		,       num_examples=SCREAMING_SNAKE_CASE_		,       seq_shapes=SCREAMING_SNAKE_CASE_    )

    with ArrowWriter(features=SCREAMING_SNAKE_CASE_		,       path=SCREAMING_SNAKE_CASE_    ) as writer:
        for key, record in dummy_data:
            _lowerCAmelCase				=      features.encode_example(SCREAMING_SNAKE_CASE_    )
            writer.write(SCREAMING_SNAKE_CASE_    )

        _lowerCAmelCase			,			_lowerCAmelCase				=      writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''    )

    _lowerCAmelCase				=      datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE_		,       info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_    )    )

    return dataset











'''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

_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

@add_end_docstrings(__magic_name__		)
class    lowerCAmelCase_		(		__magic_name__		):




 def __init__(	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							List[Any]:
     super().__init__(*_lowerCAmelCase			,     **_lowerCAmelCase		)
     requires_backends(self			,     "vision"		)
     self.check_model_type(_lowerCAmelCase		)




 def __call__(	self			,     _lowerCAmelCase			,     **_lowerCAmelCase		)    ->							str:
     return super().__call__(_lowerCAmelCase			,     **_lowerCAmelCase		)




 def 							_snake_case   (	self			,     **_lowerCAmelCase		)    ->							Tuple:
     return {}, {}, {}




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      load_image(_lowerCAmelCase		)
     _lowerCAmelCase				=      image.size
     _lowerCAmelCase				=      self.image_processor(images=_lowerCAmelCase			,     return_tensors=self.framework		)
     return model_inputs




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							Tuple:
     _lowerCAmelCase				=      self.model(**_lowerCAmelCase		)
     return model_outputs





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


