




'''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 ViTImageProcessor


class        _UpperCAmelCase						(		unittest.TestCase			):




    def __init__(			self  :       List[str]  ,     a  :       int  ,     a  :       int=1_3  ,     a  :       int=3  ,     a  :       str=2_2_4  ,     a  :       str=3_0  ,     a  :       Dict=4_0_0  ,     a  :       Any=True  ,     a  :       Dict=None  ,     a  :       Optional[int]=True  ,     a  :       Optional[Any]=[0.5, 0.5, 0.5]  ,     a  :       Optional[int]=[0.5, 0.5, 0.5]  ,     ):






       '''simple docstring'''






       lowercase_	:    Union[str, Any]           =     size if size is not None else {'''height''': 1_8, '''width''': 1_8}
       lowercase_	:    Optional[Any]           =     parent
       lowercase_	:    int           =     batch_size
       lowercase_	:    Union[str, Any]           =     num_channels
       lowercase_	:    List[str]           =     image_size
       lowercase_	:    Optional[int]           =     min_resolution
       lowercase_	:    str           =     max_resolution
       lowercase_	:    Union[str, Any]           =     do_resize
       lowercase_	:    Optional[int]           =     size
       lowercase_	:    Tuple           =     do_normalize
       lowercase_	:    Optional[int]           =     image_mean
       lowercase_	:    Union[str, Any]           =     image_std





    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }


@require_torch
@require_vision
class        _UpperCAmelCase						(		lowercase__				,					unittest.TestCase			):
    __lowerCamelCase:   List[str]		       =    ViTImageProcessor if is_vision_available() else None




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Any           =     EfficientFormerImageProcessorTester(self			)




    @property
    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       return self.image_proc_tester.prepare_image_processor_dict()




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     self.image_processing_class(**self.image_processor_dict			)
       self.assertTrue(hasattr(UpperCAmelCase__  ,     "image_mean"			)			)
       self.assertTrue(hasattr(UpperCAmelCase__  ,     "image_std"			)			)
       self.assertTrue(hasattr(UpperCAmelCase__  ,     "do_normalize"			)			)
       self.assertTrue(hasattr(UpperCAmelCase__  ,     "do_resize"			)			)
       self.assertTrue(hasattr(UpperCAmelCase__  ,     "size"			)			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       pass




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.image_processing_class(**self.image_processor_dict			)
       # create random PIL images
       lowercase_	:    List[str]           =     prepare_image_inputs(self.image_proc_tester  ,     equal_resolution=UpperCAmelCase__			)
       for image in image_inputs:
          self.assertIsInstance(UpperCAmelCase__  ,     Image.Image			)

       # Test not batched input
       lowercase_	:    int           =     image_processor(image_inputs[0]  ,     return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )  ,     )

       # Test batched
       lowercase_	:    int           =     image_processor(UpperCAmelCase__  ,     return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )  ,     )




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     self.image_processing_class(**self.image_processor_dict			)
       # create random numpy tensors
       lowercase_	:    List[Any]           =     prepare_image_inputs(self.image_proc_tester  ,     equal_resolution=UpperCAmelCase__  ,     numpify=UpperCAmelCase__			)
       for image in image_inputs:
          self.assertIsInstance(UpperCAmelCase__  ,     np.ndarray			)

       # Test not batched input
       lowercase_	:    str           =     image_processor(image_inputs[0]  ,     return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )  ,     )

       # Test batched
       lowercase_	:    Dict           =     image_processor(UpperCAmelCase__  ,     return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )  ,     )





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     self.image_processing_class(**self.image_processor_dict			)
       # create random PyTorch tensors
       lowercase_	:    Optional[int]           =     prepare_image_inputs(self.image_proc_tester  ,     equal_resolution=UpperCAmelCase__  ,     torchify=UpperCAmelCase__			)
       for image in image_inputs:
          self.assertIsInstance(UpperCAmelCase__  ,     torch.Tensor			)

       # Test not batched input
       lowercase_	:    int           =     image_processor(image_inputs[0]  ,     return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               1,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.size["width"],
           )  ,     )

       # Test batched
       lowercase_	:    Tuple           =     image_processor(UpperCAmelCase__  ,     return_tensors="pt"			).pixel_values
       self.assertEqual(
           encoded_images.shape  ,     (
               self.image_proc_tester.batch_size,
               self.image_proc_tester.num_channels,
               self.image_proc_tester.size["height"],
               self.image_proc_tester.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


UpperCamelCase__ =			logging.get_logger(__name__)


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   Tuple		       =    'linear'
    __lowerCamelCase:   Any		       =    'cosine'
    __lowerCamelCase:   Optional[Any]		       =    'cosine_with_restarts'
    __lowerCamelCase:   Tuple		       =    'polynomial'
    __lowerCamelCase:   int		       =    'constant'
    __lowerCamelCase:   Optional[Any]		       =    'constant_with_warmup'
    __lowerCamelCase:   List[str]		       =    'piecewise_constant'




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = -1							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     {}
   lowercase_	:    Dict           =     step_rules.split(","							)
   for rule_str in rule_list[:-1]:
      lowercase_    ,		lowercase_	:    Any           =     rule_str.split(":"							)
      lowercase_	:    List[Any]           =     int(_UpperCamelCase							)
      lowercase_	:    int           =     float(_UpperCamelCase							)
      lowercase_	:    Optional[int]           =     value
   lowercase_	:    Union[str, Any]           =     float(rule_list[-1]							)

   def create_rules_function(_UpperCamelCase						,		_UpperCamelCase							):
      def rule_func(_UpperCamelCase							) -> float:
         lowercase_	:    Optional[Any]           =     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

   lowercase_	:    Optional[int]           =     create_rules_function(_UpperCamelCase						,		_UpperCamelCase							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		last_epoch=_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=-1							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 0.5						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    List[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(_UpperCamelCase							) * 2.0 * progress							))							)

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = 1						,		_UpperCamelCase = -1							):


   """simple docstring"""




   def lr_lambda(_UpperCamelCase							):
      if current_step < num_warmup_steps:
         return float(_UpperCamelCase							) / float(max(1						,		_UpperCamelCase							)							)
      lowercase_	:    Dict           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=1e-7						,		_UpperCamelCase=1.0						,		_UpperCamelCase=-1							):


   """simple docstring"""




   lowercase_	:    Dict           =     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:
         lowercase_	:    int           =     lr_init - lr_end
         lowercase_	:    Optional[int]           =     num_training_steps - num_warmup_steps
         lowercase_	:    Optional[Any]           =     1 - (current_step - num_warmup_steps) / decay_steps
         lowercase_	:    List[Any]           =     lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)


UpperCamelCase__ =			{
    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					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = None						,		_UpperCamelCase = 1						,		_UpperCamelCase = 1.0						,		_UpperCamelCase = -1						,		):


   """simple docstring"""




   lowercase_	:    Any           =     SchedulerType(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     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							)




