
import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




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

						return name




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     int:
						for key in orig_state_dict.copy().keys():
												_lowercase					       =					orig_state_dict.pop(SCREAMING_SNAKE_CASE_       )

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

												else:
																		_lowercase					       =					val

						return orig_state_dict




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	List[str]       )  ->     Any:
						_lowercase					       =					ViTMAEConfig()
						if "large" in checkpoint_url:
												_lowercase					       =					10_24
												_lowercase					       =					40_96
												_lowercase					       =					24
												_lowercase					       =					16
						elif "huge" in checkpoint_url:
												_lowercase					       =					14
												_lowercase					       =					12_80
												_lowercase					       =					51_20
												_lowercase					       =					32
												_lowercase					       =					16

						_lowercase					       =					ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_       )

						_lowercase					       =					torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_		,   map_location="""cpu"""       )["""model"""]

						_lowercase					       =					ViTMAEImageProcessor(size=config.image_size       )

						_lowercase					       =					convert_state_dict(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )

						model.load_state_dict(SCREAMING_SNAKE_CASE_       )
						model.eval()

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

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

						# forward pass
						torch.manual_seed(2       )
						_lowercase					       =					model(**SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					outputs.logits

						if "large" in checkpoint_url:
												_lowercase					       =					torch.tensor(
												    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]       )
						elif "huge" in checkpoint_url:
												_lowercase					       =					torch.tensor(
												    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]       )
						else:
												_lowercase					       =					torch.tensor(
												    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]       )

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

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

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


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[Any]								=     parser.parse_args()
		convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

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_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VivitImageProcessor



class    a_ (			unittest.TestCase    ):






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase=7				,			__UpperCamelCase=3				,			__UpperCamelCase=10				,			__UpperCamelCase=18				,			__UpperCamelCase=30				,			__UpperCamelCase=400				,			__UpperCamelCase=True				,			__UpperCamelCase=None				,			__UpperCamelCase=True				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=[0.5, 0.5, 0.5]				,			__UpperCamelCase=None				,			):
									_lowercase					       =					size if size is not None else {"""shortest_edge""": 18}
									_lowercase					       =					crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									_lowercase					       =					parent
									_lowercase					       =					batch_size
									_lowercase					       =					num_channels
									_lowercase					       =					num_frames
									_lowercase					       =					image_size
									_lowercase					       =					min_resolution
									_lowercase					       =					max_resolution
									_lowercase					       =					do_resize
									_lowercase					       =					size
									_lowercase					       =					do_normalize
									_lowercase					       =					image_mean
									_lowercase					       =					image_std
									_lowercase					       =					crop_size






			def    UpperCamelCase_				(      self  ):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}







@require_torch
@require_vision
class    a_ (			_a					,			unittest.TestCase    ):
			a	:			str					   =							VivitImageProcessor if is_vision_available() else None






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					VivitImageProcessingTester(self  )






			@property
			def    UpperCamelCase_				(      self  ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					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				,			"""do_center_crop"""  )  )
									self.assertTrue(hasattr(__UpperCamelCase				,			"""size"""  )  )






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 18}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 18, """width""": 18}  )

									_lowercase					       =					self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84  )
									self.assertEqual(image_processor.size				,			{"""shortest_edge""": 42}  )
									self.assertEqual(image_processor.crop_size				,			{"""height""": 84, """width""": 84}  )






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PIL videos
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			Image.Image  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random numpy tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			numpify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			np.ndarray  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)






			def    UpperCamelCase_				(      self  ):
									# Initialize image_processing
									_lowercase					       =					self.image_processing_class(**self.image_processor_dict  )
									# create random PyTorch tensors
									_lowercase					       =					prepare_video_inputs(self.image_processor_tester				,			equal_resolution=__UpperCamelCase				,			torchify=__UpperCamelCase  )
									for video in video_inputs:
															self.assertIsInstance(__UpperCamelCase				,			__UpperCamelCase  )
															self.assertIsInstance(video[0]				,			torch.Tensor  )

									# Test not batched input
									_lowercase					       =					image_processing(video_inputs[0]				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        1,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)

									# Test batched
									_lowercase					       =					image_processing(__UpperCamelCase				,			return_tensors="""pt"""  ).pixel_values
									self.assertEqual(
									    encoded_videos.shape				,			(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_frames,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["""height"""],
									        self.image_processor_tester.crop_size["""width"""],
									    )				,			)