from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


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


if is_vision_available():
   import PIL







class __snake_case							(		UpperCamelCase__						):
   lowerCAmelCase_								=				["pixel_values"]




   def __init__(       self     :							int			,      _lowercase     :							Dict = True			,      _lowercase     :							Tuple = None			,      _lowercase     :							Union[str, Any] = PILImageResampling.BILINEAR			,      _lowercase     :							Any = True			,      _lowercase     :							List[Any] = False			,      _lowercase     :							int = True			,      _lowercase     :							int = True			,      _lowercase     :							int = 1 / 2_55			,      _lowercase     :							Optional[int] = True			,      _lowercase     :							List[str] = None			,      _lowercase     :							Optional[int] = None			,      **_lowercase     :							Any			,      ):



     """simple docstring"""






     super().__init__(**_UpperCAmelCase )

     SCREAMING_SNAKE_CASE__     				= size if size is not None else {'''height''': 25_60, '''width''': 19_20}
     if isinstance(_UpperCAmelCase			,      (tuple, list) ):
       # The previous feature extractor size parameter was in (width, height) format
       SCREAMING_SNAKE_CASE__     				= size[::-1]
     SCREAMING_SNAKE_CASE__     				= get_size_dict(_UpperCAmelCase )

     SCREAMING_SNAKE_CASE__     				= do_resize
     SCREAMING_SNAKE_CASE__     				= size
     SCREAMING_SNAKE_CASE__     				= resample
     SCREAMING_SNAKE_CASE__     				= do_thumbnail
     SCREAMING_SNAKE_CASE__     				= do_align_long_axis
     SCREAMING_SNAKE_CASE__     				= do_pad
     SCREAMING_SNAKE_CASE__     				= do_rescale
     SCREAMING_SNAKE_CASE__     				= rescale_factor
     SCREAMING_SNAKE_CASE__     				= do_normalize
     SCREAMING_SNAKE_CASE__     				= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE__     				= image_std if image_std is not None else IMAGENET_STANDARD_STD




   def       __a		(       self     :							Dict			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[str]			,      _lowercase     :							List[Any] = None ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= get_image_size(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= size['''height'''], size['''width''']

     if (output_width < output_height and input_width > input_height) or (
         output_width > output_height and input_width < input_height
     ):
       SCREAMING_SNAKE_CASE__     				= np.rotaa(_UpperCAmelCase			,      3 )

     if data_format is not None:
       SCREAMING_SNAKE_CASE__     				= to_channel_dimension_format(_UpperCAmelCase			,      _UpperCAmelCase )

     return image




   def       __a		(       self     :							Union[str, Any]			,      _lowercase     :							Tuple			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any] = False			,      _lowercase     :							List[str] = None			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= size['''height'''], size['''width''']
     SCREAMING_SNAKE_CASE__     				= get_image_size(_UpperCAmelCase )

     SCREAMING_SNAKE_CASE__     				= output_width - input_width
     SCREAMING_SNAKE_CASE__     				= output_height - input_height

     if random_padding:
       SCREAMING_SNAKE_CASE__     				= np.random.randint(low=0			,      high=delta_height + 1 )
       SCREAMING_SNAKE_CASE__     				= np.random.randint(low=0			,      high=delta_width + 1 )
     else:
       SCREAMING_SNAKE_CASE__     				= delta_height // 2
       SCREAMING_SNAKE_CASE__     				= delta_width // 2

     SCREAMING_SNAKE_CASE__     				= delta_height - pad_top
     SCREAMING_SNAKE_CASE__     				= delta_width - pad_left

     SCREAMING_SNAKE_CASE__     				= ((pad_top, pad_bottom), (pad_left, pad_right))
     return pad(_UpperCAmelCase			,      _UpperCAmelCase			,      data_format=_UpperCAmelCase )




   def       __a		(       self     :							str			,      *_lowercase     :							Any			,      **_lowercase     :							Union[str, Any] ):



     """simple docstring"""






     logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead.""" )
     return self.pad_image(*_UpperCAmelCase			,      **_UpperCAmelCase )




   def       __a		(       self     :							Dict			,      _lowercase     :							List[str]			,      _lowercase     :							int			,      _lowercase     :							Tuple = PILImageResampling.BICUBIC			,      _lowercase     :							str = None			,      **_lowercase     :							Optional[int]			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= get_image_size(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= size['''height'''], size['''width''']

     # We always resize to the smallest of either the input or output size.
     SCREAMING_SNAKE_CASE__     				= min(_UpperCAmelCase			,      _UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= min(_UpperCAmelCase			,      _UpperCAmelCase )

     if height == input_height and width == input_width:
       return image

     if input_height > input_width:
       SCREAMING_SNAKE_CASE__     				= int(input_width * height / input_height )
     elif input_width > input_height:
       SCREAMING_SNAKE_CASE__     				= int(input_height * width / input_width )

     return resize(
         _UpperCAmelCase			,      size=(height, width)			,      resample=_UpperCAmelCase			,      reducing_gap=2.0			,      data_format=_UpperCAmelCase			,      **_UpperCAmelCase )




   def       __a		(       self     :							int			,      _lowercase     :							Any			,      _lowercase     :							Dict			,      _lowercase     :							List[Any] = PILImageResampling.BICUBIC			,      _lowercase     :							Tuple = None			,      **_lowercase     :							int			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= get_size_dict(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= min(size["""height"""]			,      size["""width"""] )
     SCREAMING_SNAKE_CASE__     				= get_resize_output_image_size(_UpperCAmelCase			,      size=_UpperCAmelCase			,      default_to_square=_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= resize(_UpperCAmelCase			,      size=_UpperCAmelCase			,      resample=_UpperCAmelCase			,      data_format=_UpperCAmelCase			,      **_UpperCAmelCase )
     return resized_image




   def       __a		(       self     :							str			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[str]			,      _lowercase     :							Tuple = None			,      **_lowercase     :							Optional[Any]			,      ):



     """simple docstring"""






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




   def       __a		(       self     :							List[str]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Dict			,      _lowercase     :							List[str]			,      _lowercase     :							Optional[int] = None			,      **_lowercase     :							List[Any]			,      ):



     """simple docstring"""






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




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Optional[Any] = None			,      _lowercase     :							str = None			,      _lowercase     :							Union[str, Any] = None			,      _lowercase     :							str = None			,      _lowercase     :							Optional[int] = None			,      _lowercase     :							Optional[Any] = None			,      _lowercase     :							List[Any] = False			,      _lowercase     :							Optional[Any] = None			,      _lowercase     :							List[str] = None			,      _lowercase     :							Union[str, Any] = None			,      _lowercase     :							Optional[Any] = None			,      _lowercase     :							str = None			,      _lowercase     :							Any = None			,      _lowercase     :							List[str] = ChannelDimension.FIRST			,      **_lowercase     :							Tuple			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__     				= size if size is not None else self.size
     if isinstance(_UpperCAmelCase			,      (tuple, list) ):
       # Previous feature extractor had size in (width, height) format
       SCREAMING_SNAKE_CASE__     				= size[::-1]
     SCREAMING_SNAKE_CASE__     				= get_size_dict(_UpperCAmelCase )
     SCREAMING_SNAKE_CASE__     				= resample if resample is not None else self.resample
     SCREAMING_SNAKE_CASE__     				= do_thumbnail if do_thumbnail is not None else self.do_thumbnail
     SCREAMING_SNAKE_CASE__     				= do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
     SCREAMING_SNAKE_CASE__     				= do_pad if do_pad is not None else self.do_pad
     SCREAMING_SNAKE_CASE__     				= do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__     				= rescale_factor if rescale_factor is not None else self.rescale_factor
     SCREAMING_SNAKE_CASE__     				= do_normalize if do_normalize is not None else self.do_normalize
     SCREAMING_SNAKE_CASE__     				= image_mean if image_mean is not None else self.image_mean
     SCREAMING_SNAKE_CASE__     				= image_std if image_std is not None else self.image_std

     SCREAMING_SNAKE_CASE__     				= make_list_of_images(_UpperCAmelCase )

     if not valid_images(_UpperCAmelCase ):
       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_pad and size is None:
       raise ValueError("""Size must be specified if do_pad 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.
     SCREAMING_SNAKE_CASE__     				= [to_numpy_array(_UpperCAmelCase ) for image in images]

     if do_align_long_axis:
       SCREAMING_SNAKE_CASE__     				= [self.align_long_axis(_UpperCAmelCase			,      size=_UpperCAmelCase ) for image in images]

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

     if do_thumbnail:
       SCREAMING_SNAKE_CASE__     				= [self.thumbnail(image=_UpperCAmelCase			,      size=_UpperCAmelCase ) for image in images]

     if do_pad:
       SCREAMING_SNAKE_CASE__     				= [self.pad_image(image=_UpperCAmelCase			,      size=_UpperCAmelCase			,      random_padding=_UpperCAmelCase ) for image in images]

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

     if do_normalize:
       SCREAMING_SNAKE_CASE__     				= [self.normalize(image=_UpperCAmelCase			,      mean=_UpperCAmelCase			,      std=_UpperCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__     				= [to_channel_dimension_format(_UpperCAmelCase			,      _UpperCAmelCase ) for image in images]

     SCREAMING_SNAKE_CASE__     				= {'''pixel_values''': images}
     return BatchFeature(data=_UpperCAmelCase			,      tensor_type=_UpperCAmelCase )








'''simple docstring'''







import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 					__UpperCAmelCase (		nn.Linear	):

      '''simple docstring'''





      def __init__(					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase = True  ,		_UpperCAmelCase = "constant"  ,		_UpperCAmelCase = 0.0  ,		_UpperCAmelCase = "topK"  ,		):
             super(_UpperCAmelCase  ,		self						).__init__(in_features=_UpperCAmelCase  ,		out_features=_UpperCAmelCase  ,		bias=_UpperCAmelCase						)
             assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
             UpperCAmelCase__	:   Union[str, Any]												=	pruning_method

             if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                    UpperCAmelCase__	:   Union[str, Any]												=	mask_scale
                    UpperCAmelCase__	:   Optional[int]												=	mask_init
                    UpperCAmelCase__	:   List[str]												=	nn.Parameter(torch.empty(self.weight.size()						)						)
                    self.init_mask()




      def 							lowerCamelCase     (					self						):
             if self.mask_init == "constant":
                    init.constant_(self.mask_scores  ,		val=self.mask_scale						)
             elif self.mask_init == "uniform":
                    init.uniform_(self.mask_scores  ,		a=-self.mask_scale  ,		b=self.mask_scale						)
             elif self.mask_init == "kaiming":
                    init.kaiming_uniform_(self.mask_scores  ,		a=math.sqrt(5						)						)




      def 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase						):
             # Get the mask
             if self.pruning_method == "topK":
                    UpperCAmelCase__	:   Union[str, Any]												=	TopKBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase						)
             elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                    UpperCAmelCase__	:   List[Any]												=	'''sigmoied''' in self.pruning_method
                    UpperCAmelCase__	:   Tuple												=	ThresholdBinarizer.apply(self.mask_scores  ,		_UpperCAmelCase  ,		_UpperCAmelCase						)
             elif self.pruning_method == "magnitude":
                    UpperCAmelCase__	:   Optional[int]												=	MagnitudeBinarizer.apply(self.weight  ,		_UpperCAmelCase						)
             elif self.pruning_method == "l0":
                    UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   int												=	-0.1, 1.1, 2 / 3
                    if self.training:
                           UpperCAmelCase__	:   Optional[int]												=	torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01  ,		0.99_99						)
                           UpperCAmelCase__	:   Tuple												=	torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                    else:
                           UpperCAmelCase__	:   Any												=	torch.sigmoid(self.mask_scores						)
                    UpperCAmelCase__	:   List[Any]												=	s * (r - l) + l
                    UpperCAmelCase__	:   Optional[int]												=	s_bar.clamp(min=0.0  ,		max=1.0						)
             # Mask weights with computed mask
             UpperCAmelCase__	:   Union[str, Any]												=	mask * self.weight
             # Compute output (linear layer) with masked weights
             return nn.functional.linear(_UpperCAmelCase  ,		_UpperCAmelCase  ,		self.bias						)