

from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		bool							):





    """simple docstring"""


    if not inplace:
        SCREAMING_SNAKE_CASE_						=    ma + ma
    else:
        ma += ma

    return ma


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		List[int]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    -1 * len(_SCREAMING_SNAKE_CASE							)
    SCREAMING_SNAKE_CASE_						=    list(range(len(tensor.shape[:zero_index]							)							)							)
    return tensor.permute(first_inds + [zero_index + i for i in inds]							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		int							):





    """simple docstring"""


    return t.reshape(t.shape[:-no_dims] + (-1,)							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		float = 1E-4							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    mask.expand(*value.shape							)
    return torch.sum(mask * value			, dim=_SCREAMING_SNAKE_CASE							) / (eps + torch.sum(_SCREAMING_SNAKE_CASE			, dim=_SCREAMING_SNAKE_CASE							))


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		torch.types.Number = 2.3125			, _SCREAMING_SNAKE_CASE       :		torch.types.Number = 21.6875			, _SCREAMING_SNAKE_CASE       :		int = 64							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    torch.linspace(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, no_bins - 1			, device=pts.device							)
    SCREAMING_SNAKE_CASE_						=    torch.sqrt(torch.sum((pts.unsqueeze(-2							) - pts.unsqueeze(-3							)) ** 2			, dim=-1							)							)
    return torch.bucketize(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Callable[[list], Any]			, _SCREAMING_SNAKE_CASE       :		List[dict]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    dicts[0]
    SCREAMING_SNAKE_CASE_						=    {}
    for k, v in first.items():
        SCREAMING_SNAKE_CASE_						=    [d[k] for d in dicts]
        if isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
            SCREAMING_SNAKE_CASE_						=    dict_multimap(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        else:
            SCREAMING_SNAKE_CASE_						=    fn(_SCREAMING_SNAKE_CASE							)

    return new_dict


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		torch.Tensor							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    v_bins.view(((1,) * len(x.shape							)) + (len(_SCREAMING_SNAKE_CASE							),)							)
    SCREAMING_SNAKE_CASE_						=    x[..., None] - reshaped_bins
    SCREAMING_SNAKE_CASE_						=    torch.argmin(torch.abs(_SCREAMING_SNAKE_CASE							)			, dim=-1							)
    return nn.functional.one_hot(_SCREAMING_SNAKE_CASE			, num_classes=len(_SCREAMING_SNAKE_CASE							)							).float()


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		torch.Tensor			, _SCREAMING_SNAKE_CASE       :		int = 0			, _SCREAMING_SNAKE_CASE       :		int = 0							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    []
    for i, s in enumerate(data.shape[:no_batch_dims]							):
        SCREAMING_SNAKE_CASE_						=    torch.arange(_SCREAMING_SNAKE_CASE							)
        SCREAMING_SNAKE_CASE_						=    r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape							) - i - 1)))							)
        ranges.append(_SCREAMING_SNAKE_CASE							)

    SCREAMING_SNAKE_CASE_						=    [slice(_SCREAMING_SNAKE_CASE							) for _ in range(len(data.shape							) - no_batch_dims							)]
    SCREAMING_SNAKE_CASE_						=    inds
    ranges.extend(_SCREAMING_SNAKE_CASE							)
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(_SCREAMING_SNAKE_CASE							)]


UpperCamelCase__	:   List[Any]								=     TypeVar("T")


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Callable[[T], Any]			, _SCREAMING_SNAKE_CASE       :		Dict[Any, Union[dict, list, tuple, T]]			, _SCREAMING_SNAKE_CASE       :		Type[T]							):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_						=    {}
    for k, v in dic.items():
        if isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
            SCREAMING_SNAKE_CASE_						=    dict_map(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
        else:
            SCREAMING_SNAKE_CASE_						=    tree_map(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)

    return new_dict


@overload
def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Callable[[T], Any]			, _SCREAMING_SNAKE_CASE       :		T			, _SCREAMING_SNAKE_CASE       :		Type[T]							):





    """simple docstring"""


    ...


@overload
def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Callable[[T], Any]			, _SCREAMING_SNAKE_CASE       :		dict			, _SCREAMING_SNAKE_CASE       :		Type[T]							):





    """simple docstring"""


    ...


@overload
def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Callable[[T], Any]			, _SCREAMING_SNAKE_CASE       :		list			, _SCREAMING_SNAKE_CASE       :		Type[T]							):





    """simple docstring"""


    ...


@overload
def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		Callable[[T], Any]			, _SCREAMING_SNAKE_CASE       :		tuple			, _SCREAMING_SNAKE_CASE       :		Type[T]							):





    """simple docstring"""


    ...


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		int			, _SCREAMING_SNAKE_CASE       :		List[str]			, _SCREAMING_SNAKE_CASE       :		Dict							):





    """simple docstring"""


    if isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        return dict_map(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							)
    elif isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        return [tree_map(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							) for x in tree]
    elif isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        return tuple(tree_map(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							) for x in tree							)
    elif isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        return fn(_SCREAMING_SNAKE_CASE							)
    else:
        print(type(_SCREAMING_SNAKE_CASE							)							)
        raise ValueError('Not supported'							)





UpperCamelCase__	:   List[str]								=     partial(tree_map, leaf_type=torch.Tensor)





import warnings

from ...utils import logging
from .image_processing_vilt import ViltImageProcessor


UpperCamelCase__	:   Optional[Any]								=     logging.get_logger(__name__)



class    __snake_case	(     lowerCAmelCase__							):

  def __init__(       self    ,		*_A    ,		**_A):
      warnings.warn(
          'The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use ViltImageProcessor instead.'    ,		_A    ,		)
      super().__init__(*_A    ,		**_A)


