





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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
    import torch


__lowerCamelCase       :     Tuple   =     logging.get_logger(__name__)



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Tuple						    =   ['pixel_values']





      def __init__( self       ,							A_ = True       ,							A_ = None       ,							A_ = PILImageResampling.BILINEAR       ,							A_ = True       ,							A_ = None       ,							A_ = True       ,							A_ = 1 / 255       ,							A_ = True       ,							A_ = None       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(**A_   )
       UpperCamelCase	:					Optional[Any]       	=   size if size is not None else {"shortest_edge": 256}
       UpperCamelCase	:					List[Any]       	=   get_size_dict(A_       ,							default_to_square=A_   )
       UpperCamelCase	:					Dict       	=   crop_size if crop_size is not None else {"height": 224, "width": 224}
       UpperCamelCase	:					List[Any]       	=   get_size_dict(A_       ,							param_name="crop_size"   )
       UpperCamelCase	:					Optional[int]       	=   do_resize
       UpperCamelCase	:					str       	=   size
       UpperCamelCase	:					str       	=   resample
       UpperCamelCase	:					Optional[int]       	=   do_center_crop
       UpperCamelCase	:					List[Any]       	=   crop_size
       UpperCamelCase	:					Optional[int]       	=   do_rescale
       UpperCamelCase	:					Any       	=   rescale_factor
       UpperCamelCase	:					Optional[Any]       	=   do_normalize
       UpperCamelCase	:					Tuple       	=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCamelCase	:					str       	=   image_std if image_std is not None else IMAGENET_STANDARD_STD





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = PILImageResampling.BICUBIC       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   get_size_dict(A_       ,							default_to_square=A_   )
       if "shortest_edge" not in size:
        raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""   )
       UpperCamelCase	:					Any       	=   get_resize_output_image_size(A_       ,							size=size["shortest_edge"]       ,							default_to_square=A_   )
       return resize(A_       ,							size=A_       ,							resample=A_       ,							data_format=A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   get_size_dict(A_   )
       if "height" not in size or "width" not in size:
        raise ValueError(F"""The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"""   )
       return center_crop(A_       ,							size=(size["height"], size["width"])       ,							data_format=A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None       ,							**A_   ):
       '''simple docstring'''




       return rescale(A_       ,							scale=A_       ,							data_format=A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							**A_       ,							):
       '''simple docstring'''




       return normalize(A_       ,							mean=A_       ,							std=A_       ,							data_format=A_       ,							**A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = None       ,							A_ = ChannelDimension.FIRST       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   do_resize if do_resize is not None else self.do_resize
       UpperCamelCase	:					Tuple       	=   size if size is not None else self.size
       UpperCamelCase	:					Any       	=   get_size_dict(A_       ,							default_to_square=A_   )
       UpperCamelCase	:					Optional[Any]       	=   resample if resample is not None else self.resample
       UpperCamelCase	:					List[str]       	=   do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase	:					str       	=   crop_size if crop_size is not None else self.crop_size
       UpperCamelCase	:					List[Any]       	=   get_size_dict(A_       ,							param_name="crop_size"   )
       UpperCamelCase	:					Tuple       	=   do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase	:					List[Any]       	=   rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase	:					List[Any]       	=   do_normalize if do_normalize is not None else self.do_normalize
       UpperCamelCase	:					List[Any]       	=   image_mean if image_mean is not None else self.image_mean
       UpperCamelCase	:					Optional[Any]       	=   image_std if image_std is not None else self.image_std

       UpperCamelCase	:					List[Any]       	=   make_list_of_images(A_   )

       if not valid_images(A_   ):
        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_center_crop and crop_size is None:
        raise ValueError("Crop size must be specified if do_center_crop is True."   )

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

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True."   )

       # All transformations expect numpy arrays.
       UpperCamelCase	:					Tuple       	=   [to_numpy_array(A_   ) for image in images]

       if do_resize:
        UpperCamelCase	:					Union[str, Any]       	=   [self.resize(image=A_       ,							size=A_       ,							resample=A_   ) for image in images]

       if do_center_crop:
        UpperCamelCase	:					Optional[Any]       	=   [self.center_crop(image=A_       ,							size=A_   ) for image in images]

       if do_rescale:
        UpperCamelCase	:					int       	=   [self.rescale(image=A_       ,							scale=A_   ) for image in images]

       if do_normalize:
        UpperCamelCase	:					str       	=   [self.normalize(image=A_       ,							mean=A_       ,							std=A_   ) for image in images]

       UpperCamelCase	:					List[str]       	=   [to_channel_dimension_format(A_       ,							A_   ) for image in images]

       UpperCamelCase	:					List[Any]       	=   {"pixel_values": images}
       return BatchFeature(data=A_       ,							tensor_type=A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   outputs.logits

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

        if is_torch_tensor(A_   ):
         UpperCamelCase	:					Tuple       	=   target_sizes.numpy()

        UpperCamelCase	:					Tuple       	=   []

        for idx in range(len(A_   )   ):
         UpperCamelCase	:					List[str]       	=   torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0   )       ,							size=target_sizes[idx]       ,							mode="bilinear"       ,							align_corners=A_   )
         UpperCamelCase	:					Union[str, Any]       	=   resized_logits[0].argmax(dim=0   )
         semantic_segmentation.append(A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   logits.argmax(dim=1   )
        UpperCamelCase	:					int       	=   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

       return semantic_segmentation






'''simple docstring'''







from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


A     =logging.get_logger(__name__)


A     =TypeVar('DatasetType', Dataset, IterableDataset)





def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[List[float]] = None    ,   _a	:							Optional[int] = None    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,   ):
    from .arrow_dataset import Dataset
    from .iterable_dataset import IterableDataset

    if not datasets:
        raise ValueError('''Unable to interleave an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
        raise ValueError(F"{stopping_strategy} is not supported. Please enter a valid stopping_strategy."     )
    if dataset_type is Dataset:
        return _interleave_map_style_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )
    else:
        return _interleave_iterable_datasets(
            _a    ,   _a    ,   _a    ,   info=_a    ,   split=_a    ,   stopping_strategy=_a     )







def 						snake_case_							(_a	:							List[DatasetType]    ,   _a	:							Optional[DatasetInfo] = None    ,   _a	:							Optional[NamedSplit] = None    ,   _a	:							int = 0    ,   ):

    if not dsets:
        raise ValueError('''Unable to concatenate an empty list of datasets.'''     )
    for i, dataset in enumerate(_a     ):
        if not isinstance(_a    ,   (Dataset, IterableDataset)     ):
            if isinstance(_a    ,   (DatasetDict, IterableDatasetDict)     ):
                if not dataset:
                    raise ValueError(
                        F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
                        '''is an empty dataset dictionary.'''     )
                raise ValueError(
                    F"Dataset at position {i} has at least one split: {list(_a     )}\n"
                    F"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_a     )     )}']"     )
            raise ValueError(
                F"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_a     ).__name__}."     )
        if i == 0:
            UpperCAmelCase       ,     UpperCAmelCase			       =		(
                (Dataset, IterableDataset) if isinstance(_a    ,   _a     ) else (IterableDataset, Dataset)
            )
        elif not isinstance(_a    ,   _a     ):
            raise ValueError(
                F"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."     )
    if dataset_type is Dataset:
        return _concatenate_map_style_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )
    else:
        return _concatenate_iterable_datasets(_a    ,   info=_a    ,   split=_a    ,   axis=_a     )




