





'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


__magic_name__   :						int    =			logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[str]      =   ['''pixel_values''']

     def __init__(					self						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = 0.9						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = 1 / 255						,   lowerCamelCase = True						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       _snake_case     		=				size if size is not None else {"shortest_edge": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else {"height": 224, "width": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"	)

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				crop_pct
       _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 IMAGENET_DEFAULT_MEAN
       _snake_case     		=				image_std if image_std is not None else IMAGENET_DEFAULT_STD

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size and ("height" not in size or "width" not in size):
         raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''	)
       if crop_pct is not None:
         if "shortest_edge" in size:
           _snake_case     		=				int(size["shortest_edge"] / crop_pct	)
         elif "height" in size and "width" in size:
           if size["height"] == size["width"]:
             _snake_case     		=				int(size["height"] / crop_pct	)
           else:
             _snake_case     		=				(int(size["height"] / crop_pct	), int(size["width"] / crop_pct	))
         else:
           raise ValueError("Invalid size for resize: {}".format(lowerCamelCase	)	)

         _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=lowerCamelCase						,   default_to_square=lowerCamelCase	)
       else:
         if "shortest_edge" in size:
           _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
         elif "height" in size and "width" in size:
           _snake_case     		=				(size["height"], size["width"])
         else:
           raise ValueError("Invalid size for resize: {}".format(lowerCamelCase	)	)

       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = ChannelDimension.FIRST						,   **lowerCamelCase						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				crop_pct if crop_pct is not None else self.crop_pct
       _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     		=				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     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"	)

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       if not valid_images(lowerCamelCase	):
         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 or resample is None:
         raise ValueError("Size and resample must be specified if do_resize is True."	)

       if do_center_crop and crop_pct is None:
         raise ValueError("Crop_pct 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."	)

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

       if do_resize:
         _snake_case     		=				[self.resize(image=lowerCamelCase						,   size=lowerCamelCase						,   crop_pct=lowerCamelCase						,   resample=lowerCamelCase	) for image in images]

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

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

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

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)









'''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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
  return lrs




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for step in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	"schedule.bin"       )
        torch.save(scheduler.state_dict()					,	SCREAMING_SNAKE_CASE__       )

        _snake_case     		=				torch.load(SCREAMING_SNAKE_CASE__       )
        scheduler.load_state_dict(SCREAMING_SNAKE_CASE__       )
  return lrs


@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				AdamW(params=[w]						,   lr=2e-1						,   weight_decay=0.0	)
       for _ in range(100	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				Adafactor(
           params=[w]						,   lr=1e-2						,   eps=(1e-30, 1e-3)						,   clip_threshold=1.0						,   decay_rate=-0.8						,   betaa=lowerCamelCase						,   weight_decay=0.0						,   relative_step=lowerCamelCase						,   scale_parameter=lowerCamelCase						,   warmup_init=lowerCamelCase						,   )
       for _ in range(1_000	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   nn.Linear(50					,    50			) if is_torch_available() else None
     UpperCAmelCase__       :						str      =   AdamW(m.parameters()					,    lr=10.0			) if is_torch_available() else None
     UpperCAmelCase__       :						Optional[Any]      =   10

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase						,   msg=lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _snake_case     		=				{
           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, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {"num_warmup_steps": 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
         _snake_case		,		_snake_case     		=				data

         _snake_case     		=				scheduler_func(self.optimizer						,   **lowerCamelCase	)
         self.assertEqual(len([scheduler.get_lr()[0]]	)						,   1	)
         _snake_case     		=				unwrap_schedule(lowerCamelCase						,   self.num_steps	)
         self.assertListAlmostEqual(
             lowerCamelCase						,   lowerCamelCase						,   tol=1e-2						,   msg=F'''failed for {scheduler_func} in normal scheduler'''						,   )

         _snake_case     		=				scheduler_func(self.optimizer						,   **lowerCamelCase	)
         if scheduler_func.__name__ != "get_constant_schedule":
           LambdaScheduleWrapper.wrap_scheduler(lowerCamelCase	)  # wrap to test picklability of the schedule
         _snake_case     		=				unwrap_and_save_reload_schedule(lowerCamelCase						,   self.num_steps	)
         self.assertListEqual(lowerCamelCase						,   lowerCamelCase						,   msg=F'''failed for {scheduler_func} in save and reload'''	)







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				fn

     def __call__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       return self.fn(*lowerCamelCase						,   **lowerCamelCase	)






     @classmethod
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(map(self						,   scheduler.lr_lambdas	)	)


