






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 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			List[Any]=1_0    )						->      str:





    """simple docstring"""

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



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Any=1_0    )						->      Optional[Any]:





    """simple docstring"""

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

                __A         =	torch.load(__lowercase    )
                scheduler.load_state_dict(__lowercase    )
    return lrs


@require_torch
class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			Any ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			Optional[Any]				):


         """simple docstring"""



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




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         __A         =	torch.tensor([0.1, -0.2, -0.1] ,	requires_grad=UpperCamelCase_				)
         __A         =	torch.tensor([0.4, 0.2, -0.5]				)
         __A         =	nn.MSELoss()
         # No warmup, constant schedule, no gradient clipping
         __A         =	AdamW(params=[w] ,	lr=2e-1 ,	weight_decay=0.0				)
         for _ in range(100				):
             __A         =	criterion(UpperCamelCase_ ,	UpperCamelCase_				)
             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       :			List[str]				):


         """simple docstring"""



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







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				nn.Linear(50			,      50  ) if is_torch_available() else None
     SCREAMING_SNAKE_CASE   						=				AdamW(m.parameters()			,      lr=10.0  ) if is_torch_available() else None
     SCREAMING_SNAKE_CASE   						=				10




     def 		lowerCAmelCase_     (				self       :			List[str] ,	UpperCamelCase_       :			Optional[int] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			List[Any] ,	UpperCamelCase_       :			Dict=None				):


         """simple docstring"""



         self.assertEqual(len(UpperCamelCase_				) ,	len(UpperCamelCase_				)				)
         for a, b in zip(UpperCamelCase_ ,	UpperCamelCase_				):
             self.assertAlmostEqual(UpperCamelCase_ ,	UpperCamelCase_ ,	delta=UpperCamelCase_ ,	msg=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			List[Any]				):


         """simple docstring"""



         __A         =	{"""num_warmup_steps""": 2, """num_training_steps""": 10}
         # schedulers doct format
         # function: (sched_args_dict, expected_learning_rates)
         __A         =	{
             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():
             __A    ,	__A         =	data

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

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


class       __lowercase      :







     '''simple docstring'''




     def __init__(				self       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[int]				):


         """simple docstring"""



         __A         =	fn




     def __call__(				self       :			Optional[Any] ,	*UpperCamelCase_       :			Any ,	**UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



         return self.fn(*UpperCamelCase_ ,	**UpperCamelCase_				)





     @classmethod
     def 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



         __A         =	list(map(self ,	scheduler.lr_lambdas				)				)












import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str    )						->      str:





    """simple docstring"""

    if "cls_token" in name:
        __A         =	name.replace("""cls_token"""			,				"""vit.embeddings.cls_token"""    )
    if "mask_token" in name:
        __A         =	name.replace("""mask_token"""			,				"""decoder.mask_token"""    )
    if "decoder_pos_embed" in name:
        __A         =	name.replace("""decoder_pos_embed"""			,				"""decoder.decoder_pos_embed"""    )
    if "pos_embed" in name and "decoder" not in name:
        __A         =	name.replace("""pos_embed"""			,				"""vit.embeddings.position_embeddings"""    )
    if "patch_embed.proj" in name:
        __A         =	name.replace("""patch_embed.proj"""			,				"""vit.embeddings.patch_embeddings.projection"""    )
    if "patch_embed.norm" in name:
        __A         =	name.replace("""patch_embed.norm"""			,				"""vit.embeddings.norm"""    )
    if "decoder_blocks" in name:
        __A         =	name.replace("""decoder_blocks"""			,				"""decoder.decoder_layers"""    )
    if "blocks" in name:
        __A         =	name.replace("""blocks"""			,				"""vit.encoder.layer"""    )
    if "attn.proj" in name:
        __A         =	name.replace("""attn.proj"""			,				"""attention.output.dense"""    )
    if "attn" in name:
        __A         =	name.replace("""attn"""			,				"""attention.self"""    )
    if "norm1" in name:
        __A         =	name.replace("""norm1"""			,				"""layernorm_before"""    )
    if "norm2" in name:
        __A         =	name.replace("""norm2"""			,				"""layernorm_after"""    )
    if "mlp.fc1" in name:
        __A         =	name.replace("""mlp.fc1"""			,				"""intermediate.dense"""    )
    if "mlp.fc2" in name:
        __A         =	name.replace("""mlp.fc2"""			,				"""output.dense"""    )
    if "decoder_embed" in name:
        __A         =	name.replace("""decoder_embed"""			,				"""decoder.decoder_embed"""    )
    if "decoder_norm" in name:
        __A         =	name.replace("""decoder_norm"""			,				"""decoder.decoder_norm"""    )
    if "decoder_pred" in name:
        __A         =	name.replace("""decoder_pred"""			,				"""decoder.decoder_pred"""    )
    if "norm.weight" in name and "decoder" not in name:
        __A         =	name.replace("""norm.weight"""			,				"""vit.layernorm.weight"""    )
    if "norm.bias" in name and "decoder" not in name:
        __A         =	name.replace("""norm.bias"""			,				"""vit.layernorm.bias"""    )

    return name



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Any			,				__lowercase   :			Dict    )						->      str:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A         =	orig_state_dict.pop(__lowercase    )

        if "qkv" in key:
            __A         =	key.split("""."""    )
            __A         =	int(key_split[1]    )
            if "decoder_blocks" in key:
                __A         =	config.decoder_hidden_size
                __A         =	"""decoder.decoder_layers."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]
            else:
                __A         =	config.hidden_size
                __A         =	"""vit.encoder.layer."""
                if "weight" in key:
                    __A         =	val[:dim, :]
                    __A         =	val[dim : dim * 2, :]
                    __A         =	val[-dim:, :]
                elif "bias" in key:
                    __A         =	val[:dim]
                    __A         =	val[dim : dim * 2]
                    __A         =	val[-dim:]

        else:
            __A         =	val

    return orig_state_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			str    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	ViTMAEConfig()
    if "large" in checkpoint_url:
        __A         =	1_0_2_4
        __A         =	4_0_9_6
        __A         =	2_4
        __A         =	1_6
    elif "huge" in checkpoint_url:
        __A         =	1_4
        __A         =	1_2_8_0
        __A         =	5_1_2_0
        __A         =	3_2
        __A         =	1_6

    __A         =	ViTMAEForPreTraining(__lowercase    )

    __A         =	torch.hub.load_state_dict_from_url(__lowercase			,				map_location="""cpu"""    )["""model"""]

    __A         =	ViTMAEImageProcessor(size=config.image_size    )

    __A         =	convert_state_dict(__lowercase			,				__lowercase    )

    model.load_state_dict(__lowercase    )
    model.eval()

    __A         =	"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

    __A         =	Image.open(requests.get(__lowercase			,				stream=__lowercase    ).raw    )
    __A         =	ViTMAEImageProcessor(size=config.image_size    )
    __A         =	image_processor(images=__lowercase			,				return_tensors="""pt"""    )

    # forward pass
    torch.manual_seed(2    )
    __A         =	model(**__lowercase    )
    __A         =	outputs.logits

    if "large" in checkpoint_url:
        __A         =	torch.tensor(
            [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
    elif "huge" in checkpoint_url:
        __A         =	torch.tensor(
            [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
    else:
        __A         =	torch.tensor(
            [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

    # verify logits
    assert torch.allclose(logits[0, :3, :3]			,				__lowercase			,				atol=1E-4    )

    print(f"Saving model to {pytorch_dump_folder_path}"    )
    model.save_pretrained(__lowercase    )

    print(f"Saving image processor to {pytorch_dump_folder_path}"    )
    image_processor.save_pretrained(__lowercase    )


if __name__ == "__main__":
  __a      :       List[str]				  =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--checkpoint_url",
      default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
      type=str,
      help="URL of the checkpoint you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
  )

  __a      :       List[str]				  =   parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




