






'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          Adafactor,
          AdamW,
          get_constant_schedule,
          get_constant_schedule_with_warmup,
          get_cosine_schedule_with_warmup,
          get_cosine_with_hard_restarts_schedule_with_warmup,
          get_inverse_sqrt_schedule,
          get_linear_schedule_with_warmup,
          get_polynomial_decay_schedule_with_warmup,
      )






def 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Union[str, Any]=1_0							)			->			List[str]:





      """simple docstring"""
      lowercase_      :   int        =			[]
      for _ in range(a							):
            lrs.append(scheduler.get_lr()[0]							)
            scheduler.step()
      return lrs






def 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	Union[str, Any]=1_0							)			->			Optional[int]:





      """simple docstring"""
      lowercase_      :   Optional[Any]        =			[]
      for step in range(a							):
            lrs.append(scheduler.get_lr()[0]							)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        lowercase_      :   Dict        =			os.path.join(a						,			'schedule.bin'							)
                        torch.save(scheduler.state_dict()						,			a							)

                        lowercase_      :   Tuple        =			torch.load(a							)
                        scheduler.load_state_dict(a							)
      return lrs






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







 """simple docstring"""



 def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  Optional[Any]:
       self.assertEqual(len(_lowercase       )  ,      len(_lowercase       )       )
       for a, b in zip(_lowercase  ,      _lowercase       ):
             self.assertAlmostEqual(_lowercase  ,      _lowercase  ,      delta=_lowercase       )
 def        lowerCamelCase__		(							self       )						->  int:
       lowercase_      :   int        =			torch.tensor([0.1, -0.2, -0.1]  ,      requires_grad=_lowercase       )
       lowercase_      :   List[Any]        =			torch.tensor([0.4, 0.2, -0.5]       )
       lowercase_      :   List[Any]        =			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       lowercase_      :   int        =			AdamW(params=[w]  ,      lr=2E-1  ,      weight_decay=0.0       )
       for _ in range(100       ):
             lowercase_      :   Optional[Any]        =			criterion(_lowercase  ,      _lowercase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()  ,      [0.4, 0.2, -0.5]  ,      tol=1E-2       )







 def        lowerCamelCase__		(							self       )						->  Dict:
       lowercase_      :   List[Any]        =			torch.tensor([0.1, -0.2, -0.1]  ,      requires_grad=_lowercase       )
       lowercase_      :   Optional[Any]        =			torch.tensor([0.4, 0.2, -0.5]       )
       lowercase_      :   Dict        =			nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       lowercase_      :   Optional[Any]        =			Adafactor(
           params=[w]  ,      lr=1E-2  ,      eps=(1E-3_0, 1E-3)  ,      clip_threshold=1.0  ,      decay_rate=-0.8  ,      betaa=_lowercase  ,      weight_decay=0.0  ,      relative_step=_lowercase  ,      scale_parameter=_lowercase  ,      warmup_init=_lowercase  ,      )
       for _ in range(1000       ):
             lowercase_      :   str        =			criterion(_lowercase  ,      _lowercase       )
             loss.backward()
             optimizer.step()
             w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
             w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()  ,      [0.4, 0.2, -0.5]  ,      tol=1E-2       )






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







 """simple docstring"""



 SCREAMING_SNAKE_CASE_					:      Optional[int]        =  nn.Linear(5_0,						5_0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  AdamW(m.parameters(),						lr=10.0				) if is_torch_available() else None
 SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  1_0
 def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=None       )						->  Union[str, Any]:
       self.assertEqual(len(_lowercase       )  ,      len(_lowercase       )       )
       for a, b in zip(_lowercase  ,      _lowercase       ):
             self.assertAlmostEqual(_lowercase  ,      _lowercase  ,      delta=_lowercase  ,      msg=_lowercase       )







 def        lowerCamelCase__		(							self       )						->  int:
       lowercase_      :   int        =			{'num_warmup_steps': 2, 'num_training_steps': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       lowercase_      :   Dict        =			{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {'num_warmup_steps': 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, 'num_cycles': 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
               [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
           ),
           get_inverse_sqrt_schedule: (
               {'num_warmup_steps': 2},
               [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
           ),
       }

       for scheduler_func, data in scheds.items():
             lowercase_      :   Union[str, Any]        =			data

             lowercase_      :   Tuple        =			scheduler_func(self.optimizer  ,      **_lowercase       )
             self.assertEqual(len([scheduler.get_lr()[0]]       )  ,      1       )
             lowercase_      :   Union[str, Any]        =			unwrap_schedule(_lowercase  ,      self.num_steps       )
             self.assertListAlmostEqual(
                 _lowercase  ,      _lowercase  ,      tol=1E-2  ,      msg=f"failed for {scheduler_func} in normal scheduler"  ,      )

             lowercase_      :   int        =			scheduler_func(self.optimizer  ,      **_lowercase       )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(_lowercase       )  # wrap to test picklability of the schedule
             lowercase_      :   List[Any]        =			unwrap_and_save_reload_schedule(_lowercase  ,      self.num_steps       )
             self.assertListEqual(_lowercase  ,      _lowercase  ,      msg=f"failed for {scheduler_func} in save and reload"       )






class 							__magic_name__     :







 """simple docstring"""



 def __init__(							self  ,      _lowercase       )						->  int:
       lowercase_      :   Dict        =			fn
 def __call__(							self  ,      *_lowercase  ,      **_lowercase       )						->  List[str]:
       return self.fn(*_lowercase  ,      **_lowercase       )







 @classmethod
 def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Union[str, Any]:
       lowercase_      :   Union[str, Any]        =			list(map(self  ,      scheduler.lr_lambdas       )       )








'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A:      Dict  				=				logging.get_logger(__name__)

A:      Optional[Any]  				=				{
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Optional[Any]        =  'vit'
	def __init__(							self  ,      _lowercase=768  ,      _lowercase=12  ,      _lowercase=12  ,      _lowercase=3072  ,      _lowercase="gelu"  ,      _lowercase=0.0  ,      _lowercase=0.0  ,      _lowercase=0.02  ,      _lowercase=1E-1_2  ,      _lowercase=224  ,      _lowercase=16  ,      _lowercase=3  ,      _lowercase=True  ,      _lowercase=16  ,      **_lowercase  ,      )						->  List[str]:
							super().__init__(**_lowercase       )

							lowercase_      :   Optional[int]        =			hidden_size
							lowercase_      :   str        =			num_hidden_layers
							lowercase_      :   str        =			num_attention_heads
							lowercase_      :   int        =			intermediate_size
							lowercase_      :   List[Any]        =			hidden_act
							lowercase_      :   Any        =			hidden_dropout_prob
							lowercase_      :   List[str]        =			attention_probs_dropout_prob
							lowercase_      :   str        =			initializer_range
							lowercase_      :   List[str]        =			layer_norm_eps
							lowercase_      :   Any        =			image_size
							lowercase_      :   Tuple        =			patch_size
							lowercase_      :   Optional[Any]        =			num_channels
							lowercase_      :   str        =			qkv_bias
							lowercase_      :   List[str]        =			encoder_stride






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  version.parse('1.11'				)
	@property
	def        lowerCamelCase__		(							self       )						->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]       )







	@property
	def        lowerCamelCase__		(							self       )						->  float:
							return 1E-4
