




'''simple docstring'''



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,
    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, logging


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						['''pixel_values''']



   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Optional[Dict[str, int]] = None  ,				__lowerCamelCase :     PILImageResampling = PILImageResampling.BILINEAR  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Union[int, float] = 1 / 2_5_5  ,				__lowerCamelCase :     bool = True  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				**__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				size if size is not None else {"shortest_edge": 2_5_6}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				do_resize
      _SCREAMING_SNAKE_CASE	=				size
      _SCREAMING_SNAKE_CASE	=				resample
      _SCREAMING_SNAKE_CASE	=				do_center_crop
      _SCREAMING_SNAKE_CASE	=				crop_size
      _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 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Dict[str, int]  ,				__lowerCamelCase :     PILImageResampling = PILImageResampling.BICUBIC  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     Dict  ,				):

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



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Dict[str, int]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     List[Any]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      return center_crop(__lowerCamelCase  ,				size=(size["height"], size["width"])  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     float  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      return rescale(__lowerCamelCase  ,				scale=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     np.ndarray  ,				__lowerCamelCase :     Union[float, List[float]]  ,				__lowerCamelCase :     Union[float, List[float]]  ,				__lowerCamelCase :     Optional[Union[str, ChannelDimension]] = None  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      return normalize(__lowerCamelCase  ,				mean=__lowerCamelCase  ,				std=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     ImageInput  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     PILImageResampling = None  ,				__lowerCamelCase :     bool = None  ,				__lowerCamelCase :     Dict[str, int] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Optional[float] = None  ,				__lowerCamelCase :     Optional[bool] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[float, List[float]]] = None  ,				__lowerCamelCase :     Optional[Union[str, TensorType]] = None  ,				__lowerCamelCase :     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """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
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				resample if resample is not None else self.resample
      _SCREAMING_SNAKE_CASE	=				do_center_crop if do_center_crop is not None else self.do_center_crop
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else self.crop_size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      _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(__lowerCamelCase   )

      if not valid_images(__lowerCamelCase   ):
         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.
      _SCREAMING_SNAKE_CASE	=				[to_numpy_array(__lowerCamelCase   ) for image in images]

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

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

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

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

      _SCREAMING_SNAKE_CASE	=				[to_channel_dimension_format(__lowerCamelCase  ,				__lowerCamelCase   ) for image in images]

      _SCREAMING_SNAKE_CASE	=				{"pixel_values": images}
      return BatchFeature(data=__lowerCamelCase  ,				tensor_type=__lowerCamelCase   )







'''simple docstring'''



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


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''linear'''
   lowerCamelCase_      =						'''cosine'''
   lowerCamelCase_      =						'''cosine_with_restarts'''
   lowerCamelCase_      =						'''polynomial'''
   lowerCamelCase_      =						'''constant'''
   lowerCamelCase_      =						'''constant_with_warmup'''
   lowerCamelCase_      =						'''piecewise_constant'''


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int = -1  )			->			Optional[int]:
   return LambdaLR(__A	,					lambda __A  : 1	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int = -1  )			->			Dict:

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

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			str	,					__A    :			int = -1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				{}
   _SCREAMING_SNAKE_CASE	=				step_rules.split(","  )
   for rule_str in rule_list[:-1]:
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				rule_str.split(":"  )
      _SCREAMING_SNAKE_CASE	=				int(__A  )
      _SCREAMING_SNAKE_CASE	=				float(__A  )
      _SCREAMING_SNAKE_CASE	=				value
   _SCREAMING_SNAKE_CASE	=				float(rule_list[-1]  )

   def create_rules_function(__A    :			Tuple	,					__A    :			List[Any]  ):
      def rule_func(__A    :			int  ) -> float:
         _SCREAMING_SNAKE_CASE	=				sorted(rules_dict.keys()  )
         for i, sorted_step in enumerate(__A  ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   _SCREAMING_SNAKE_CASE	=				create_rules_function(__A	,					__A  )

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Union[str, Any]=-1  )			->			str:

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

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			float = 0.5	,					__A    :			int = -1  )			->			Any:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				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(__A  ) * 2.0 * progress  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			int = 1	,					__A    :			int = -1  )			->			str:

   def lr_lambda(__A    :			Optional[int]  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _SCREAMING_SNAKE_CASE	=				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(__A  ) * progress) % 1.0)  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Any=1e-7	,					__A    :			Optional[Any]=1.0	,					__A    :			List[Any]=-1  )			->			Tuple:
   _SCREAMING_SNAKE_CASE	=				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(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         _SCREAMING_SNAKE_CASE	=				lr_init - lr_end
         _SCREAMING_SNAKE_CASE	=				num_training_steps - num_warmup_steps
         _SCREAMING_SNAKE_CASE	=				1 - (current_step - num_warmup_steps) / decay_steps
         _SCREAMING_SNAKE_CASE	=				lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__A	,					__A	,					__A  )


lowerCamelCase_			=						{
    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 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, SchedulerType]	,					__A    :			Optimizer	,					__A    :			Optional[str] = None	,					__A    :			Optional[int] = None	,					__A    :			Optional[int] = None	,					__A    :			int = 1	,					__A    :			float = 1.0	,					__A    :			int = -1	,					)			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				SchedulerType(__A  )
   _SCREAMING_SNAKE_CASE	=				TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__A	,					last_epoch=__A  )

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

   # 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(__A	,					num_warmup_steps=__A	,					last_epoch=__A  )

   # 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(
          __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					num_cycles=__A	,					last_epoch=__A	,					)

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

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

