


'''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 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_=13 ,   lowerCamelCase_=3 ,   lowerCamelCase_=2_24 ,   lowerCamelCase_=30 ,   lowerCamelCase_=4_00 ,   lowerCamelCase_=True ,   lowerCamelCase_=None ,   lowerCamelCase_=True ,   lowerCamelCase_=[0.5, 0.5, 0.5] ,   lowerCamelCase_=[0.5, 0.5, 0.5] ,   )	->							Optional[int]:
														lowerCAmelCase__	       =     size if size is not None else {'''height''': 18, '''width''': 18}
														lowerCAmelCase__	       =     parent
														lowerCAmelCase__	       =     batch_size
														lowerCAmelCase__	       =     num_channels
														lowerCAmelCase__	       =     image_size
														lowerCAmelCase__	       =     min_resolution
														lowerCAmelCase__	       =     max_resolution
														lowerCAmelCase__	       =     do_resize
														lowerCAmelCase__	       =     size
														lowerCAmelCase__	       =     do_normalize
														lowerCAmelCase__	       =     image_mean
														lowerCAmelCase__	       =     image_std







							def   __SCREAMING_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,
														    "size": self.size,
														}




@require_torch
@require_vision
class 		a__    (							a__    ,    unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       List[Any]     						=				ViTImageProcessor if is_vision_available() else None







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     EfficientFormerImageProcessorTester(self       )







							@property
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Union[str, Any]:
														return self.image_proc_tester.prepare_image_processor_dict()







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(lowerCamelCase_ ,   '''image_mean'''       )       )
														self.assertTrue(hasattr(lowerCamelCase_ ,   '''image_std'''       )       )
														self.assertTrue(hasattr(lowerCamelCase_ ,   '''do_normalize'''       )       )
														self.assertTrue(hasattr(lowerCamelCase_ ,   '''do_resize'''       )       )
														self.assertTrue(hasattr(lowerCamelCase_ ,   '''size'''       )       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Any:
														pass







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														# Initialize image_processor
														lowerCAmelCase__	       =     self.image_processing_class(**self.image_processor_dict       )
														# create random PIL images
														lowerCAmelCase__	       =     prepare_image_inputs(self.image_proc_tester ,   equal_resolution=lowerCamelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase_ ,   Image.Image       )

														# Test not batched input
														lowerCAmelCase__	       =     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
														lowerCAmelCase__	       =     image_processor(lowerCamelCase_ ,   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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														# Initialize image_processor
														lowerCAmelCase__	       =     self.image_processing_class(**self.image_processor_dict       )
														# create random numpy tensors
														lowerCAmelCase__	       =     prepare_image_inputs(self.image_proc_tester ,   equal_resolution=lowerCamelCase_ ,   numpify=lowerCamelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase_ ,   np.ndarray       )

														# Test not batched input
														lowerCAmelCase__	       =     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
														lowerCAmelCase__	       =     image_processor(lowerCamelCase_ ,   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   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														# Initialize image_processor
														lowerCAmelCase__	       =     self.image_processing_class(**self.image_processor_dict       )
														# create random PyTorch tensors
														lowerCAmelCase__	       =     prepare_image_inputs(self.image_proc_tester ,   equal_resolution=lowerCamelCase_ ,   torchify=lowerCamelCase_       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase_ ,   torch.Tensor       )

														# Test not batched input
														lowerCAmelCase__	       =     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
														lowerCAmelCase__	       =     image_processor(lowerCamelCase_ ,   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 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       int     						=				"linear"
							lowercase__      :       Any     						=				"cosine"
							lowercase__      :       Optional[int]     						=				"cosine_with_restarts"
							lowercase__      :       Optional[Any]     						=				"polynomial"
							lowercase__      :       Tuple     						=				"constant"
							lowercase__      :       Optional[int]     						=				"constant_with_warmup"
							lowercase__      :       Optional[int]     						=				"piecewise_constant"


def  _snake_case       (					A			,   A = -1	)						->   Any:
							return LambdaLR(A			,   lambda A	: 1			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A = -1	)						->   Optional[Any]:

							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  _snake_case       (					A			,   A			,   A = -1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     {}
							lowerCAmelCase__	       =     step_rules.split(''','''	)
							for rule_str in rule_list[:-1]:
														lowerCAmelCase__       , lowerCAmelCase__	       =     rule_str.split(''':'''	)
														lowerCAmelCase__	       =     int(A	)
														lowerCAmelCase__	       =     float(A	)
														lowerCAmelCase__	       =     value
							lowerCAmelCase__	       =     float(rule_list[-1]	)

							def create_rules_function(A			,   A	):
														def rule_func(A	) -> float:
																					lowerCAmelCase__	       =     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

							lowerCAmelCase__	       =     create_rules_function(A			,   A	)

							return LambdaLR(A			,   A			,   last_epoch=A	)


def  _snake_case       (					A			,   A			,   A			,   A=-1	)						->   Optional[int]:

							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  _snake_case       (					A			,   A			,   A			,   A = 0.5			,   A = -1	)						->   List[str]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     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  _snake_case       (					A			,   A			,   A			,   A = 1			,   A = -1	)						->   Union[str, Any]:

							def lr_lambda(A	):
														if current_step < num_warmup_steps:
																					return float(A	) / float(max(1			,   A	)	)
														lowerCAmelCase__	       =     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  _snake_case       (					A			,   A			,   A			,   A=1E-7			,   A=1.0			,   A=-1	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     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:
																					lowerCAmelCase__	       =     lr_init - lr_end
																					lowerCAmelCase__	       =     num_training_steps - num_warmup_steps
																					lowerCAmelCase__	       =     1 - (current_step - num_warmup_steps) / decay_steps
																					lowerCAmelCase__	       =     lr_range * pct_remaining**power + lr_end
																					return decay / lr_init  # as LambdaLR multiplies by lr_init

							return LambdaLR(A			,   A			,   A	)


__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  _snake_case       (					A			,   A			,   A = None			,   A = None			,   A = None			,   A = 1			,   A = 1.0			,   A = -1			,   )						->   int:
							lowerCAmelCase__	       =     SchedulerType(A	)
							lowerCAmelCase__	       =     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	)