




"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import LevitImageProcessor




class  _UpperCAmelCase (    unittest.TestCase):

 def __init__(  self	: Union[str, Any]						,  lowercase_	: str						,  lowercase_	: Optional[int]=7						,  lowercase_	: List[str]=3						,  lowercase_	: str=18						,  lowercase_	: Tuple=30						,  lowercase_	: Optional[int]=400						,  lowercase_	: Optional[int]=True						,  lowercase_	: Any=None						,  lowercase_	: Any=True						,  lowercase_	: str=None						,  lowercase_	: int=True						,  lowercase_	: List[Any]=[0.5, 0.5, 0.5]						,  lowercase_	: Any=[0.5, 0.5, 0.5]						,  ):
     snake_case_				:  Dict       =			size if size is not None else {'''shortest_edge''': 18}
     snake_case_				:  Optional[int]       =			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     snake_case_				:  Any       =			parent
     snake_case_				:  Any       =			batch_size
     snake_case_				:  str       =			num_channels
     snake_case_				:  List[Any]       =			image_size
     snake_case_				:  Dict       =			min_resolution
     snake_case_				:  Dict       =			max_resolution
     snake_case_				:  int       =			do_resize
     snake_case_				:  Any       =			size
     snake_case_				:  Optional[Any]       =			do_center_crop
     snake_case_				:  int       =			crop_size
     snake_case_				:  List[str]       =			do_normalize
     snake_case_				:  List[Any]       =			image_mean
     snake_case_				:  Any       =			image_std






 def 		_snake_case      (  self	: Optional[int]       ):
     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "do_center_crop": self.do_center_crop,
         "size": self.size,
         "crop_size": self.crop_size,
     }





@require_torch
@require_vision
class  _UpperCAmelCase (    lowerCAmelCase__					,  unittest.TestCase):
 _lowerCAmelCase     :    Any	      =	LevitImageProcessor if is_vision_available() else None

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Any       =			LevitImageProcessingTester(self       )

 @property
 def 		_snake_case      (  self	: str       ):
     return self.image_processor_tester.prepare_image_processor_dict()

 def 		_snake_case      (  self	: Union[str, Any]       ):
     snake_case_				:  Optional[int]       =			self.image_processing_class(**self.image_processor_dict       )
     self.assertTrue(hasattr(lowercase_						,  '''image_mean'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''image_std'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_normalize'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_resize'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''do_center_crop'''       )       )
     self.assertTrue(hasattr(lowercase_						,  '''size'''       )       )

 def 		_snake_case      (  self	: Dict       ):
     snake_case_				:  Optional[int]       =			self.image_processing_class.from_dict(self.image_processor_dict       )
     self.assertEqual(image_processor.size						,  {'''shortest_edge''': 18}       )
     self.assertEqual(image_processor.crop_size						,  {'''height''': 18, '''width''': 18}       )

     snake_case_				:  Optional[int]       =			self.image_processing_class.from_dict(self.image_processor_dict						,  size=42						,  crop_size=84       )
     self.assertEqual(image_processor.size						,  {'''shortest_edge''': 42}       )
     self.assertEqual(image_processor.crop_size						,  {'''height''': 84, '''width''': 84}       )

 def 		_snake_case      (  self	: Optional[Any]       ):
     pass

 def 		_snake_case      (  self	: Union[str, Any]       ):
     # Initialize image_processing
     snake_case_				:  List[Any]       =			self.image_processing_class(**self.image_processor_dict       )
     # create random PIL images
     snake_case_				:  Any       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_       )
     for image in image_inputs:
         self.assertIsInstance(lowercase_						,  Image.Image       )

     # Test not batched input
     snake_case_				:  Optional[int]       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

     # Test batched
     snake_case_				:  Optional[int]       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

 def 		_snake_case      (  self	: Union[str, Any]       ):
     # Initialize image_processing
     snake_case_				:  List[Any]       =			self.image_processing_class(**self.image_processor_dict       )
     # create random numpy tensors
     snake_case_				:  Optional[int]       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_						,  numpify=lowercase_       )
     for image in image_inputs:
         self.assertIsInstance(lowercase_						,  np.ndarray       )

     # Test not batched input
     snake_case_				:  str       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

     # Test batched
     snake_case_				:  Dict       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )






 def 		_snake_case      (  self	: Union[str, Any]       ):
     # Initialize image_processing
     snake_case_				:  Union[str, Any]       =			self.image_processing_class(**self.image_processor_dict       )
     # create random PyTorch tensors
     snake_case_				:  List[Any]       =			prepare_image_inputs(self.image_processor_tester						,  equal_resolution=lowercase_						,  torchify=lowercase_       )
     for image in image_inputs:
         self.assertIsInstance(lowercase_						,  torch.Tensor       )

     # Test not batched input
     snake_case_				:  Optional[int]       =			image_processing(image_inputs[0]						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )

     # Test batched
     snake_case_				:  List[Any]       =			image_processing(lowercase_						,  return_tensors='''pt'''       ).pixel_values
     self.assertEqual(
         encoded_images.shape						,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )						,  )










"""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


lowercase__					:						List[Any]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Tuple	      =	"""linear"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine"""
 _lowerCAmelCase     :    Optional[int]	      =	"""cosine_with_restarts"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""polynomial"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant"""
 _lowerCAmelCase     :    Optional[Any]	      =	"""constant_with_warmup"""
 _lowerCAmelCase     :    Union[str, Any]	      =	"""piecewise_constant"""





def __lowercase    (							_a	,    _a = -1					):
    return LambdaLR(_a	,    lambda _a					: 1	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a = -1					):

    def lr_lambda(_a					):
        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 __lowercase    (							_a	,    _a	,    _a = -1					):
    snake_case_				:  Dict       =			{}
    snake_case_				:  Optional[Any]       =			step_rules.split(''','''					)
    for rule_str in rule_list[:-1]:
        snake_case_,			snake_case_				:  Union[str, Any]       =			rule_str.split(''':'''					)
        snake_case_				:  Dict       =			int(_a					)
        snake_case_				:  int       =			float(_a					)
        snake_case_				:  Any       =			value
    snake_case_				:  Any       =			float(rule_list[-1]					)

    def create_rules_function(_a	,    _a					):
        def rule_func(_a					) -> float:
            snake_case_				:  Union[str, Any]       =			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

    snake_case_				:  List[str]       =			create_rules_function(_a	,    _a					)

    return LambdaLR(_a	,    _a	,    last_epoch=_a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a=-1					):

    def lr_lambda(_a					):
        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 __lowercase    (							_a	,    _a	,    _a	,    _a = 0.5	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  str       =			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 __lowercase    (							_a	,    _a	,    _a	,    _a = 1	,    _a = -1					):

    def lr_lambda(_a					):
        if current_step < num_warmup_steps:
            return float(_a					) / float(max(1	,    _a					)					)
        snake_case_				:  int       =			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 __lowercase    (							_a	,    _a	,    _a	,    _a=1E-7	,    _a=1.0	,    _a=-1					):
    snake_case_				:  List[str]       =			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					):
        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:
            snake_case_				:  Tuple       =			lr_init - lr_end
            snake_case_				:  List[str]       =			num_training_steps - num_warmup_steps
            snake_case_				:  str       =			1 - (current_step - num_warmup_steps) / decay_steps
            snake_case_				:  Optional[int]       =			lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(_a	,    _a	,    _a					)


lowercase__					:						Dict												=			{
    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 __lowercase    (							_a	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = 1	,    _a = 1.0	,    _a = -1	,    ):
    snake_case_				:  Union[str, Any]       =			SchedulerType(_a					)
    snake_case_				:  Any       =			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					)





