




'''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,
    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    :							Dict										=    logging.get_logger(__name__)


if is_vision_available():
							import PIL






class 		a							(  _lowerCamelCase	):
				snake_case_           =						["pixel_values"]


				def __init__(						self       :   Any			,							lowercase_       :   bool = True			,							lowercase_       :   Dict[str, int] = None			,							lowercase_       :   PILImageResampling = PILImageResampling.BICUBIC			,							lowercase_       :   bool = True			,							lowercase_       :   Dict[str, int] = None			,							lowercase_       :   bool = True			,							lowercase_       :   Union[int, float] = 1 / 255			,							lowercase_       :   bool = True			,							lowercase_       :   Optional[Union[float, List[float]]] = None			,							lowercase_       :   Optional[Union[float, List[float]]] = None			,							lowercase_       :   bool = True			,							**lowercase_       :   int			,							):
									super().__init__(**lowercase_   )
									snake_case_		     =							size if size is not None else {'''shortest_edge''': 224}
									snake_case_		     =							get_size_dict(lowercase_			,							default_to_square=lowercase_   )
									snake_case_		     =							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
									snake_case_		     =							get_size_dict(lowercase_			,							default_to_square=lowercase_			,							param_name='''crop_size'''   )

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


				def     A_					(						self       :   str			,							lowercase_       :   np.ndarray			,							lowercase_       :   Dict[str, int]			,							lowercase_       :   PILImageResampling = PILImageResampling.BICUBIC			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   Optional[Any]			,							):
									snake_case_		     =							get_size_dict(lowercase_			,							default_to_square=lowercase_   )
									if "shortest_edge" not in size:
														raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"   )
									snake_case_		     =							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     A_					(						self       :   List[str]			,							lowercase_       :   np.ndarray			,							lowercase_       :   Dict[str, int]			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   Optional[Any]			,							):
									snake_case_		     =							get_size_dict(lowercase_   )
									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(lowercase_			,							size=(size['''height'''], size['''width'''])			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   List[str]			,							lowercase_       :   np.ndarray			,							lowercase_       :   Union[int, float]			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   Tuple			,							):
									return rescale(lowercase_			,							scale=lowercase_			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   Union[str, Any]			,							lowercase_       :   np.ndarray			,							lowercase_       :   Union[float, List[float]]			,							lowercase_       :   Union[float, List[float]]			,							lowercase_       :   Optional[Union[str, ChannelDimension]] = None			,							**lowercase_       :   Optional[Any]			,							):
									return normalize(lowercase_			,							mean=lowercase_			,							std=lowercase_			,							data_format=lowercase_			,							**lowercase_   )


				def     A_					(						self       :   List[Any]			,							lowercase_       :   ImageInput			,							lowercase_       :   bool = None			,							lowercase_       :   Dict[str, int] = None			,							lowercase_       :   PILImageResampling = None			,							lowercase_       :   bool = None			,							lowercase_       :   int = None			,							lowercase_       :   bool = None			,							lowercase_       :   float = None			,							lowercase_       :   bool = None			,							lowercase_       :   Optional[Union[float, List[float]]] = None			,							lowercase_       :   Optional[Union[float, List[float]]] = None			,							lowercase_       :   bool = None			,							lowercase_       :   Optional[Union[str, TensorType]] = None			,							lowercase_       :   Optional[ChannelDimension] = ChannelDimension.FIRST			,							**lowercase_       :   Dict			,							):
									snake_case_		     =							do_resize if do_resize is not None else self.do_resize
									snake_case_		     =							size if size is not None else self.size
									snake_case_		     =							get_size_dict(lowercase_			,							param_name='''size'''			,							default_to_square=lowercase_   )
									snake_case_		     =							resample if resample is not None else self.resample
									snake_case_		     =							do_center_crop if do_center_crop is not None else self.do_center_crop
									snake_case_		     =							crop_size if crop_size is not None else self.crop_size
									snake_case_		     =							get_size_dict(lowercase_			,							param_name='''crop_size'''			,							default_to_square=lowercase_   )
									snake_case_		     =							do_rescale if do_rescale is not None else self.do_rescale
									snake_case_		     =							rescale_factor if rescale_factor is not None else self.rescale_factor
									snake_case_		     =							do_normalize if do_normalize is not None else self.do_normalize
									snake_case_		     =							image_mean if image_mean is not None else self.image_mean
									snake_case_		     =							image_std if image_std is not None else self.image_std
									snake_case_		     =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

									snake_case_		     =							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_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:
														snake_case_		     =							[convert_to_rgb(lowercase_   ) for image in images]

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

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

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

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

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

									snake_case_		     =							[to_channel_dimension_format(lowercase_			,							lowercase_   ) for image in images]

									snake_case_		     =							{'''pixel_values''': images}
									return BatchFeature(data=lowercase_			,							tensor_type=lowercase_   )







'''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


a    :							Any										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"linear"
				snake_case_           =						"cosine"
				snake_case_           =						"cosine_with_restarts"
				snake_case_           =						"polynomial"
				snake_case_           =						"constant"
				snake_case_           =						"constant_with_warmup"
				snake_case_           =						"piecewise_constant"







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					return LambdaLR(__UpperCAmelCase, lambda __UpperCAmelCase      : 1, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							{}
					snake_case_		     =							step_rules.split(''','''      )
					for rule_str in rule_list[:-1]:
										snake_case_     ,snake_case_		     =							rule_str.split(''':'''      )
										snake_case_		     =							int(__UpperCAmelCase      )
										snake_case_		     =							float(__UpperCAmelCase      )
										snake_case_		     =							value
					snake_case_		     =							float(rule_list[-1]      )

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

										return rule_func

					snake_case_		     =							create_rules_function(__UpperCAmelCase, __UpperCAmelCase      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=-1      )						->   List[Any]:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.5, __UpperCAmelCase = -1      )						->   Optional[Any]:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										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(__UpperCAmelCase      ) * 2.0 * progress      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 1, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										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(__UpperCAmelCase      ) * progress) % 1.0)      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1e-7, __UpperCAmelCase=1.0, __UpperCAmelCase=-1      )						->   str:

					'''simple docstring'''

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

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


a    :							Tuple										=    {
    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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 1, __UpperCAmelCase = 1.0, __UpperCAmelCase = -1, )						->   int:

					'''simple docstring'''

					snake_case_		     =							SchedulerType(__UpperCAmelCase      )
					snake_case_		     =							TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

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

					# 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(__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

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

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

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

