






'''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


UpperCamelCase							=logging.get_logger(__name__)




class     A    (      _UpperCAmelCase						):






 """simple docstring"""





 __a			:						Union[str, Any]      =						["""pixel_values"""]
 def __init__(							self			,     __lowerCAmelCase = True			,     __lowerCAmelCase = None			,     __lowerCAmelCase = PILImageResampling.BILINEAR			,     __lowerCAmelCase = True			,     __lowerCAmelCase = 1 / 2_55			,     __lowerCAmelCase = True			,     __lowerCAmelCase = None			,     __lowerCAmelCase = True			,     **__lowerCAmelCase			,     ):
       super().__init__(**lowercase__ )
       UpperCamelCase_				:   Dict					    =							size if size is not None else {"""shortest_edge""": 2_24}
       UpperCamelCase_				:   Optional[Any]					    =							get_size_dict(lowercase__			,     default_to_square=lowercase__ )
       UpperCamelCase_				:   Union[str, Any]					    =							crop_size if crop_size is not None else {"""height""": 2_56, """width""": 2_56}
       UpperCamelCase_				:   Tuple					    =							get_size_dict(lowercase__			,     param_name="""crop_size""" )

       UpperCamelCase_				:   Dict					    =							do_resize
       UpperCamelCase_				:   Optional[Any]					    =							size
       UpperCamelCase_				:   Optional[Any]					    =							resample
       UpperCamelCase_				:   List[Any]					    =							do_rescale
       UpperCamelCase_				:   int					    =							rescale_factor
       UpperCamelCase_				:   Dict					    =							do_center_crop
       UpperCamelCase_				:   Union[str, Any]					    =							crop_size
       UpperCamelCase_				:   str					    =							do_flip_channel_order
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = PIL.Image.BILINEAR			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   Tuple					    =							get_size_dict(lowercase__			,     default_to_square=lowercase__ )
       if "shortest_edge" not in size:
             raise ValueError(F"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}" )
       UpperCamelCase_				:   int					    =							get_resize_output_image_size(lowercase__			,     size=size["""shortest_edge"""]			,     default_to_square=lowercase__ )
       return resize(lowercase__			,     size=lowercase__			,     resample=lowercase__			,     data_format=lowercase__			,     **lowercase__ )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   List[Any]					    =							get_size_dict(lowercase__ )
       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(lowercase__			,     size=(size["""height"""], size["""width"""])			,     data_format=lowercase__			,     **lowercase__ )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):
       return rescale(lowercase__			,     scale=lowercase__			,     data_format=lowercase__			,     **lowercase__ )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       return flip_channel_order(lowercase__			,     data_format=lowercase__ )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     __lowerCAmelCase = ChannelDimension.FIRST			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   List[str]					    =							do_resize if do_resize is not None else self.do_resize
       UpperCamelCase_				:   Tuple					    =							resample if resample is not None else self.resample
       UpperCamelCase_				:   Optional[Any]					    =							do_rescale if do_rescale is not None else self.do_rescale
       UpperCamelCase_				:   int					    =							rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCamelCase_				:   int					    =							do_center_crop if do_center_crop is not None else self.do_center_crop
       UpperCamelCase_				:   Tuple					    =							(
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       UpperCamelCase_				:   Any					    =							size if size is not None else self.size
       UpperCamelCase_				:   int					    =							get_size_dict(lowercase__			,     default_to_square=lowercase__ )
       UpperCamelCase_				:   Optional[int]					    =							crop_size if crop_size is not None else self.crop_size
       UpperCamelCase_				:   str					    =							get_size_dict(lowercase__			,     param_name="""crop_size""" )

       UpperCamelCase_				:   List[str]					    =							make_list_of_images(lowercase__ )

       if not valid_images(lowercase__ ):
             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.
       UpperCamelCase_				:   Union[str, Any]					    =							[to_numpy_array(lowercase__ ) for image in images]

       if do_resize:
             UpperCamelCase_				:   str					    =							[self.resize(image=lowercase__			,     size=lowercase__			,     resample=lowercase__ ) for image in images]

       if do_center_crop:
             UpperCamelCase_				:   Any					    =							[self.center_crop(image=lowercase__			,     size=lowercase__ ) for image in images]

       if do_rescale:
             UpperCamelCase_				:   str					    =							[self.rescale(image=lowercase__			,     scale=lowercase__ ) for image in images]

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

       UpperCamelCase_				:   Any					    =							[to_channel_dimension_format(lowercase__			,     lowercase__ ) for image in images]

       UpperCamelCase_				:   Tuple					    =							{"""pixel_values""": images}
       return BatchFeature(data=lowercase__			,     tensor_type=lowercase__ )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = None ):
       UpperCamelCase_				:   List[str]					    =							outputs.logits

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

             if is_torch_tensor(lowercase__ ):
                   UpperCamelCase_				:   str					    =							target_sizes.numpy()

             UpperCamelCase_				:   int					    =							[]

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

       return semantic_segmentation














'''simple docstring'''

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
      import transformers



def 							snake_case					(     a_							: int	)       ->				Union[str, Any]:





      """simple docstring"""

      random.seed(a_	)
      np.random.seed(a_	)
      torch.manual_seed(a_	)
      torch.cuda.manual_seed_all(a_	)
      # ^^ safe to call this function even if cuda is not available




class     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase = 0.99_99			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = False			,     __lowerCAmelCase = 1.0			,     __lowerCAmelCase = 2 / 3			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):

       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   Dict					    =							(
                 """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                 """Please pass the parameters of the module instead."""
             )
             deprecate(
                 """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase			,     )
             UpperCamelCase_				:   str					    =							parameters.parameters()

             # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
             UpperCamelCase_				:   Optional[int]					    =							True

       if kwargs.get("""max_value"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   Tuple					    =							"""The `max_value` argument is deprecated. Please use `decay` instead."""
             deprecate("""max_value"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             UpperCamelCase_				:   str					    =							kwargs["""max_value"""]

       if kwargs.get("""min_value"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   Dict					    =							"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
             deprecate("""min_value"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             UpperCamelCase_				:   Optional[Any]					    =							kwargs["""min_value"""]

       UpperCamelCase_				:   Optional[Any]					    =							list(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							[p.clone().detach() for p in parameters]

       if kwargs.get("""device"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   str					    =							"""The `device` argument is deprecated. Please use `to` instead."""
             deprecate("""device"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             self.to(device=kwargs["""device"""] )

       UpperCamelCase_				:   List[Any]					    =							None

       UpperCamelCase_				:   Optional[Any]					    =							decay
       UpperCamelCase_				:   List[str]					    =							min_decay
       UpperCamelCase_				:   int					    =							update_after_step
       UpperCamelCase_				:   Optional[int]					    =							use_ema_warmup
       UpperCamelCase_				:   Optional[int]					    =							inv_gamma
       UpperCamelCase_				:   Any					    =							power
       UpperCamelCase_				:   str					    =							0
       UpperCamelCase_				:   List[str]					    =							None  # set in `step()`

       UpperCamelCase_				:   Union[str, Any]					    =							model_cls
       UpperCamelCase_				:   Any					    =							model_config
 @classmethod
 def   _UpperCAmelCase       (							cls			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							model_cls.load_config(__lowerCAmelCase			,     return_unused_kwargs=__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							model_cls.from_pretrained(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							cls(model.parameters()			,     model_cls=__lowerCAmelCase			,     model_config=model.config )

       ema_model.load_state_dict(__lowerCAmelCase )
       return ema_model
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.model_cls is None:
             raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__.""" )

       if self.model_config is None:
             raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__.""" )

       UpperCamelCase_				:   int					    =							self.model_cls.from_config(self.model_config )
       UpperCamelCase_				:   List[Any]					    =							self.state_dict()
       state_dict.pop("""shadow_params"""			,     __lowerCAmelCase )

       model.register_to_config(**__lowerCAmelCase )
       self.copy_to(model.parameters() )
       model.save_pretrained(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							max(0			,     optimization_step - self.update_after_step - 1 )

       if step <= 0:
             return 0.0

       if self.use_ema_warmup:
             UpperCamelCase_				:   List[Any]					    =							1 - (1 + step / self.inv_gamma) ** -self.power
       else:
             UpperCamelCase_				:   List[Any]					    =							(1 + step) / (10 + step)

       UpperCamelCase_				:   Optional[Any]					    =							min(__lowerCAmelCase			,     self.decay )
       # make sure decay is not smaller than min_decay
       UpperCamelCase_				:   Optional[Any]					    =							max(__lowerCAmelCase			,     self.min_decay )
       return cur_decay_value
 @torch.no_grad()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   str					    =							(
                 """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                 """Please pass the parameters of the module instead."""
             )
             deprecate(
                 """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase			,     )
             UpperCamelCase_				:   int					    =							parameters.parameters()

       UpperCamelCase_				:   Optional[int]					    =							list(__lowerCAmelCase )

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       UpperCamelCase_				:   Any					    =							self.get_decay(self.optimization_step )
       UpperCamelCase_				:   List[str]					    =							decay
       UpperCamelCase_				:   Any					    =							1 - decay

       UpperCamelCase_				:   Optional[int]					    =							contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             import deepspeed

       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   UpperCamelCase_				:   Optional[Any]					    =							deepspeed.zero.GatheredParameters(__lowerCAmelCase			,     modifier_rank=__lowerCAmelCase )

             with context_manager():
                   if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param) )
                   else:
                         s_param.copy_(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							list(__lowerCAmelCase )
       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             param.data.copy_(s_param.to(param.device ).data )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Union[str, Any]					    =							[
           p.to(device=__lowerCAmelCase			,     dtype=__lowerCAmelCase ) if p.is_floating_point() else p.to(device=__lowerCAmelCase )
           for p in self.shadow_params
       ]
 def   _UpperCAmelCase       (							self ):
       return {
           "decay": self.decay,
           "min_decay": self.min_decay,
           "optimization_step": self.optimization_step,
           "update_after_step": self.update_after_step,
           "use_ema_warmup": self.use_ema_warmup,
           "inv_gamma": self.inv_gamma,
           "power": self.power,
           "shadow_params": self.shadow_params,
       }
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							[param.detach().cpu().clone() for param in parameters]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.temp_stored_params is None:
             raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`""" )
       for c_param, param in zip(self.temp_stored_params			,     __lowerCAmelCase ):
             param.data.copy_(c_param.data )

       # Better memory-wise.
       UpperCamelCase_				:   List[Any]					    =							None





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							copy.deepcopy(__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							state_dict.get("""decay"""			,     self.decay )
       if self.decay < 0.0 or self.decay > 1.0:
             raise ValueError("""Decay must be between 0 and 1""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""min_decay"""			,     self.min_decay )
       if not isinstance(self.min_decay			,     __lowerCAmelCase ):
             raise ValueError("""Invalid min_decay""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""optimization_step"""			,     self.optimization_step )
       if not isinstance(self.optimization_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid optimization_step""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""update_after_step"""			,     self.update_after_step )
       if not isinstance(self.update_after_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid update_after_step""" )

       UpperCamelCase_				:   List[str]					    =							state_dict.get("""use_ema_warmup"""			,     self.use_ema_warmup )
       if not isinstance(self.use_ema_warmup			,     __lowerCAmelCase ):
             raise ValueError("""Invalid use_ema_warmup""" )

       UpperCamelCase_				:   Any					    =							state_dict.get("""inv_gamma"""			,     self.inv_gamma )
       if not isinstance(self.inv_gamma			,     (float, int) ):
             raise ValueError("""Invalid inv_gamma""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""power"""			,     self.power )
       if not isinstance(self.power			,     (float, int) ):
             raise ValueError("""Invalid power""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""shadow_params"""			,     __lowerCAmelCase )
       if shadow_params is not None:
             UpperCamelCase_				:   Any					    =							shadow_params
             if not isinstance(self.shadow_params			,     __lowerCAmelCase ):
                   raise ValueError("""shadow_params must be a list""" )
             if not all(isinstance(__lowerCAmelCase			,     torch.Tensor ) for p in self.shadow_params ):
                   raise ValueError("""shadow_params must all be Tensors""" )






