





'''simple docstring'''


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs


@pytest.mark.parametrize(
    "kwargs, expected"		,       [
        ({"num_shards": 0, "max_num_jobs": 1}, []),
        ({"num_shards": 10, "max_num_jobs": 1}, [range(10    )]),
        ({"num_shards": 10, "max_num_jobs": 10}, [range(SCREAMING_SNAKE_CASE_		,       i + 1    ) for i in range(10    )]),
        ({"num_shards": 1, "max_num_jobs": 10}, [range(1    )]),
        ({"num_shards": 10, "max_num_jobs": 3}, [range(0		,       4    ), range(4		,       7    ), range(7		,       10    )]),
        ({"num_shards": 3, "max_num_jobs": 10}, [range(0		,       1    ), range(1		,       2    ), range(2		,       3    )]),
    ]		,       )
def 	__a(SCREAMING_SNAKE_CASE_  :					Optional[int]		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    _lowerCAmelCase				=      _distribute_shards(**SCREAMING_SNAKE_CASE_    )
    assert out == expected


@pytest.mark.parametrize(
    "gen_kwargs, max_num_jobs, expected"		,       [
        ({"foo": 0}, 10, [{"foo": 0}]),
        ({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
        ({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
        ({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
        ({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
    ]		,       )
def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					List[str]		,       SCREAMING_SNAKE_CASE_  :					Any    ):



    '''simple docstring'''





    _lowerCAmelCase				=      _split_gen_kwargs(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    assert out == expected





@pytest.mark.parametrize(
    "gen_kwargs, expected"		,       [
        ({"foo": 0}, 1),
        ({"shards": [0]}, 1),
        ({"shards": [0, 1, 2, 3]}, 4),
        ({"shards": [0, 1, 2, 3], "foo": 0}, 4),
        ({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
        ({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
    ]		,       )
def 	__a(SCREAMING_SNAKE_CASE_  :					List[Any]		,       SCREAMING_SNAKE_CASE_  :					Optional[int]    ):



    '''simple docstring'''





    if expected is RuntimeError:
        with pytest.raises(SCREAMING_SNAKE_CASE_    ):
            _number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE_    )
    else:
        _lowerCAmelCase				=      _number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE_    )
        assert out == expected









'''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


