





'''simple docstring'''






from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
      import PIL

if is_torch_available():
      import torch


A__				:   str      =logging.get_logger(__name__)




class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			Optional[int]						     =   ['''pixel_values''']




      def __init__(			self			: List[Any]			,			__snake_case			: bool = True			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: PILImageResampling = PILImageResampling.BILINEAR			,			__snake_case			: bool = True			,			__snake_case			: Union[int, float] = 1 / 2_55			,			__snake_case			: bool = True			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: bool = True			,			**__snake_case			: Dict			,			)				->      None:
            super().__init__(**__snake_case  )
            _lowerCAmelCase							     =							size if size is not None else {"""shortest_edge""": 2_24}
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			default_to_square=__snake_case  )
            _lowerCAmelCase							     =							crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			param_name="""crop_size"""  )

            _lowerCAmelCase							     =							do_resize
            _lowerCAmelCase							     =							size
            _lowerCAmelCase							     =							resample
            _lowerCAmelCase							     =							do_rescale
            _lowerCAmelCase							     =							rescale_factor
            _lowerCAmelCase							     =							do_center_crop
            _lowerCAmelCase							     =							crop_size
            _lowerCAmelCase							     =							do_flip_channel_order




      def 				lowercase__    (			self			: Tuple			,			__snake_case			: np.ndarray			,			__snake_case			: Dict[str, int]			,			__snake_case			: PILImageResampling = PIL.Image.BILINEAR			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None			,			**__snake_case			: Union[str, Any]			,			)				->      np.ndarray:
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			default_to_square=__snake_case  )
            if "shortest_edge" not in size:
                  raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"  )
            _lowerCAmelCase							     =							get_resize_output_image_size(__snake_case			,			size=size["""shortest_edge"""]			,			default_to_square=__snake_case  )
            return resize(__snake_case			,			size=__snake_case			,			resample=__snake_case			,			data_format=__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: np.ndarray			,			__snake_case			: Dict[str, int]			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None			,			**__snake_case			: int			,			)				->      np.ndarray:
            _lowerCAmelCase							     =							get_size_dict(__snake_case  )
            if "height" not in size or "width" not in size:
                  raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"  )
            return center_crop(__snake_case			,			size=(size["""height"""], size["""width"""])			,			data_format=__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: Dict			,			__snake_case			: np.ndarray			,			__snake_case			: Union[int, float]			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None			,			**__snake_case			: Optional[Any]			,			)				->      Optional[Any]:
            return rescale(__snake_case			,			scale=__snake_case			,			data_format=__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: str			,			__snake_case			: np.ndarray			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None  )				->      np.ndarray:
            return flip_channel_order(__snake_case			,			data_format=__snake_case  )




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: ImageInput			,			__snake_case			: bool = None			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: PILImageResampling = None			,			__snake_case			: bool = None			,			__snake_case			: float = None			,			__snake_case			: bool = None			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: bool = None			,			__snake_case			: Optional[Union[str, TensorType]] = None			,			__snake_case			: ChannelDimension = ChannelDimension.FIRST			,			**__snake_case			: Optional[int]			,			)				->      PIL.Image.Image:
            _lowerCAmelCase							     =							do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase							     =							resample if resample is not None else self.resample
            _lowerCAmelCase							     =							do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase							     =							rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase							     =							do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase							     =							(
                do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
            )

            _lowerCAmelCase							     =							size if size is not None else self.size
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			default_to_square=__snake_case  )
            _lowerCAmelCase							     =							crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			param_name="""crop_size"""  )

            _lowerCAmelCase							     =							make_list_of_images(__snake_case  )

            if not valid_images(__snake_case  ):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""  )

            if do_resize and size is None:
                  raise ValueError("""Size must be specified if do_resize is True."""  )

            if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

            if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

            # All transformations expect numpy arrays.
            _lowerCAmelCase							     =							[to_numpy_array(__snake_case  ) for image in images]

            if do_resize:
                  _lowerCAmelCase							     =							[self.resize(image=__snake_case			,			size=__snake_case			,			resample=__snake_case  ) for image in images]

            if do_center_crop:
                  _lowerCAmelCase							     =							[self.center_crop(image=__snake_case			,			size=__snake_case  ) for image in images]

            if do_rescale:
                  _lowerCAmelCase							     =							[self.rescale(image=__snake_case			,			scale=__snake_case  ) for image in images]

            # the pretrained checkpoints assume images are BGR, not RGB
            if do_flip_channel_order:
                  _lowerCAmelCase							     =							[self.flip_channel_order(image=__snake_case  ) for image in images]

            _lowerCAmelCase							     =							[to_channel_dimension_format(__snake_case			,			__snake_case  ) for image in images]

            _lowerCAmelCase							     =							{"""pixel_values""": images}
            return BatchFeature(data=__snake_case			,			tensor_type=__snake_case  )




      def 				lowercase__    (			self			: int			,			__snake_case			: str			,			__snake_case			: List[Tuple] = None  )				->      Union[str, Any]:
            _lowerCAmelCase							     =							outputs.logits

            # Resize logits and compute semantic segmentation maps
            if target_sizes is not None:
                  if len(__snake_case  ) != len(__snake_case  ):
                        raise ValueError(
                            """Make sure that you pass in as many target sizes as the batch dimension of the logits"""  )

                  if is_torch_tensor(__snake_case  ):
                        _lowerCAmelCase							     =							target_sizes.numpy()

                  _lowerCAmelCase							     =							[]

                  for idx in range(len(__snake_case  )  ):
                        _lowerCAmelCase							     =							torch.nn.functional.interpolate(
                            logits[idx].unsqueeze(dim=0  )			,			size=target_sizes[idx]			,			mode="""bilinear"""			,			align_corners=__snake_case  )
                        _lowerCAmelCase							     =							resized_logits[0].argmax(dim=0  )
                        semantic_segmentation.append(__snake_case  )
            else:
                  _lowerCAmelCase							     =							logits.argmax(dim=1  )
                  _lowerCAmelCase							     =							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

            return semantic_segmentation









"""simple docstring"""


import pytest

from datasets import inspect_metric, list_metrics, load_metric






@pytest.fixture
def     a__    (    __SCREAMING_SNAKE_CASE )	->	Union[str, Any]:
		monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"					, set() )






@pytest.fixture
def     a__    (    __SCREAMING_SNAKE_CASE )	->	str:





		class     snake_case	:

						def __init__(	self							:				int     ,  UpperCamelCase__							:				Optional[int])->   Union[str, Any]:



								'''simple docstring'''


								__lowerCAmelCase:       str          =			metric_id





		class     snake_case	:
						SCREAMING_SNAKE_CASE_   :      List[Any]         =  [MetricMock(__snake_case  ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]

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



								'''simple docstring'''


								return self._metrics


		monkeypatch.setattr("datasets.inspect.huggingface_hub"					, HfhMock() )






@pytest.mark.parametrize(
    "func, args"					, [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		if "tmp_path" in args:
				__lowerCAmelCase:       Tuple          =			tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
		with pytest.warns(__SCREAMING_SNAKE_CASE					, match="https://huggingface.co/docs/evaluate" ):
				func(*__SCREAMING_SNAKE_CASE )


