


import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


_A			=						logging.get_logger(__name__)






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Tuple              =  "linear"
    UpperCAmelCase__						:		Dict              =  "cosine"
    UpperCAmelCase__						:		Tuple              =  "cosine_with_restarts"
    UpperCAmelCase__						:		str              =  "polynomial"
    UpperCAmelCase__						:		str              =  "constant"
    UpperCAmelCase__						:		List[str]              =  "constant_with_warmup"
    UpperCAmelCase__						:		List[str]              =  "piecewise_constant"

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optimizer      ,					SCREAMING_SNAKE_CASE__      : int = -1    ):
     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					lambda SCREAMING_SNAKE_CASE__    : 1      ,					last_epoch=SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optimizer      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int = -1    ):

     def lr_lambda(SCREAMING_SNAKE_CASE__      : int    ):
          if current_step < num_warmup_steps:
               return float(SCREAMING_SNAKE_CASE__    ) / float(max(1.0      ,					SCREAMING_SNAKE_CASE__    )    )
          return 1.0

     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optimizer      ,					SCREAMING_SNAKE_CASE__      : str      ,					SCREAMING_SNAKE_CASE__      : int = -1    ):
     __UpperCamelCase   							={}
     __UpperCamelCase   							=step_rules.split(','    )
     for rule_str in rule_list[:-1]:
          __UpperCamelCase							,				__UpperCamelCase   							=rule_str.split(':'    )
          __UpperCamelCase   							=int(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=float(SCREAMING_SNAKE_CASE__    )
          __UpperCamelCase   							=value
     __UpperCamelCase   							=float(rule_list[-1]    )

     def create_rules_function(SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : str    ):
          def rule_func(SCREAMING_SNAKE_CASE__      : int    ) -> float:
               __UpperCamelCase   							=sorted(rules_dict.keys()    )
               for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE__    ):
                    if steps < sorted_step:
                         return rules_dict[sorted_steps[i]]
               return last_lr_multiple

          return rule_func

     __UpperCamelCase   							=create_rules_function(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : int=-1    ):

     def lr_lambda(SCREAMING_SNAKE_CASE__      : int    ):
          if current_step < num_warmup_steps:
               return float(SCREAMING_SNAKE_CASE__    ) / float(max(1      ,					SCREAMING_SNAKE_CASE__    )    )
          return max(
              0.0      ,					float(num_training_steps - current_step    ) / float(max(1      ,					num_training_steps - num_warmup_steps    )    )    )

     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optimizer      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : float = 0.5      ,					SCREAMING_SNAKE_CASE__      : int = -1    ):

     def lr_lambda(SCREAMING_SNAKE_CASE__      : List[Any]    ):
          if current_step < num_warmup_steps:
               return float(SCREAMING_SNAKE_CASE__    ) / float(max(1      ,					SCREAMING_SNAKE_CASE__    )    )
          __UpperCamelCase   							=float(current_step - num_warmup_steps    ) / float(max(1      ,					num_training_steps - num_warmup_steps    )    )
          return max(0.0      ,					0.5 * (1.0 + math.cos(math.pi * float(SCREAMING_SNAKE_CASE__    ) * 2.0 * progress    ))    )

     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optimizer      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int      ,					SCREAMING_SNAKE_CASE__      : int = 1      ,					SCREAMING_SNAKE_CASE__      : int = -1    ):

     def lr_lambda(SCREAMING_SNAKE_CASE__      : Tuple    ):
          if current_step < num_warmup_steps:
               return float(SCREAMING_SNAKE_CASE__    ) / float(max(1      ,					SCREAMING_SNAKE_CASE__    )    )
          __UpperCamelCase   							=float(current_step - num_warmup_steps    ) / float(max(1      ,					num_training_steps - num_warmup_steps    )    )
          if progress >= 1.0:
               return 0.0
          return max(0.0      ,					0.5 * (1.0 + math.cos(math.pi * ((float(SCREAMING_SNAKE_CASE__    ) * progress) % 1.0)    ))    )

     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[int]      ,					SCREAMING_SNAKE_CASE__      : Dict      ,					SCREAMING_SNAKE_CASE__      : Any      ,					SCREAMING_SNAKE_CASE__      : str=1E-7      ,					SCREAMING_SNAKE_CASE__      : Tuple=1.0      ,					SCREAMING_SNAKE_CASE__      : Optional[int]=-1    ):
     __UpperCamelCase   							=optimizer.defaults['lr']
     if not (lr_init > lr_end):
          raise ValueError(F'lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})'    )

     def lr_lambda(SCREAMING_SNAKE_CASE__      : int    ):
          if current_step < num_warmup_steps:
               return float(SCREAMING_SNAKE_CASE__    ) / float(max(1      ,					SCREAMING_SNAKE_CASE__    )    )
          elif current_step > num_training_steps:
               return lr_end / lr_init  # as LambdaLR multiplies by lr_init
          else:
               __UpperCamelCase   							=lr_init - lr_end
               __UpperCamelCase   							=num_training_steps - num_warmup_steps
               __UpperCamelCase   							=1 - (current_step - num_warmup_steps) / decay_steps
               __UpperCamelCase   							=lr_range * pct_remaining**power + lr_end
               return decay / lr_init  # as LambdaLR multiplies by lr_init

     return LambdaLR(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__    )


_A			=						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}





def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, SchedulerType]      ,					SCREAMING_SNAKE_CASE__      : Optimizer      ,					SCREAMING_SNAKE_CASE__      : Optional[str] = None      ,					SCREAMING_SNAKE_CASE__      : Optional[int] = None      ,					SCREAMING_SNAKE_CASE__      : Optional[int] = None      ,					SCREAMING_SNAKE_CASE__      : int = 1      ,					SCREAMING_SNAKE_CASE__      : float = 1.0      ,					SCREAMING_SNAKE_CASE__      : int = -1      ,					):
     __UpperCamelCase   							=SchedulerType(SCREAMING_SNAKE_CASE__    )
     __UpperCamelCase   							=TYPE_TO_SCHEDULER_FUNCTION[name]
     if name == SchedulerType.CONSTANT:
          return schedule_func(SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__    )

     if name == SchedulerType.PIECEWISE_CONSTANT:
          return schedule_func(SCREAMING_SNAKE_CASE__      ,					step_rules=SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__    )

     # All other schedulers require `num_warmup_steps`
     if num_warmup_steps is None:
          raise ValueError(F'{name} requires `num_warmup_steps`, please provide that argument.'    )

     if name == SchedulerType.CONSTANT_WITH_WARMUP:
          return schedule_func(SCREAMING_SNAKE_CASE__      ,					num_warmup_steps=SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__    )

     # All other schedulers require `num_training_steps`
     if num_training_steps is None:
          raise ValueError(F'{name} requires `num_training_steps`, please provide that argument.'    )

     if name == SchedulerType.COSINE_WITH_RESTARTS:
          return schedule_func(
              SCREAMING_SNAKE_CASE__      ,					num_warmup_steps=SCREAMING_SNAKE_CASE__      ,					num_training_steps=SCREAMING_SNAKE_CASE__      ,					num_cycles=SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__      ,					)

     if name == SchedulerType.POLYNOMIAL:
          return schedule_func(
              SCREAMING_SNAKE_CASE__      ,					num_warmup_steps=SCREAMING_SNAKE_CASE__      ,					num_training_steps=SCREAMING_SNAKE_CASE__      ,					power=SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__      ,					)

     return schedule_func(
         SCREAMING_SNAKE_CASE__      ,					num_warmup_steps=SCREAMING_SNAKE_CASE__      ,					num_training_steps=SCREAMING_SNAKE_CASE__      ,					last_epoch=SCREAMING_SNAKE_CASE__    )









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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A			=						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BICUBIC ,       A_ = True ,       A_ = None ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         __UpperCamelCase   							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_ ,       param_name='crop_size'   )

         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_center_crop
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase   							=do_convert_rgb






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=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   							=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 _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=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, width). Got {size.keys()}'   )
         return center_crop(A_ ,       size=(size['height'], size['width']) ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	Union[str, Any]:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         return normalize(A_ ,       mean=A_ ,       std=A_ ,       data_format=A_ ,       **A_   )






    def _a (      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_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='size' ,       default_to_square=A_   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase   							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='crop_size' ,       default_to_square=A_   )
         __UpperCamelCase   							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase   							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std
         __UpperCamelCase   							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase   							=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.'   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase   							=[convert_to_rgb(A_   ) for image in images]

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

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

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

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

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

         __UpperCamelCase   							=[to_channel_dimension_format(A_ ,       A_   ) for image in images]

         __UpperCamelCase   							={'pixel_values': images}
         return BatchFeature(data=A_ ,       tensor_type=A_   )





