




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	if "cls_token" in name:
		A_  :				Optional[int]	   =		name.replace('''cls_token'''  ,      '''vit.embeddings.cls_token'''	)
	if "mask_token" in name:
		A_  :				Optional[int]	   =		name.replace('''mask_token'''  ,      '''decoder.mask_token'''	)
	if "decoder_pos_embed" in name:
		A_  :				int	   =		name.replace('''decoder_pos_embed'''  ,      '''decoder.decoder_pos_embed'''	)
	if "pos_embed" in name and "decoder" not in name:
		A_  :				str	   =		name.replace('''pos_embed'''  ,      '''vit.embeddings.position_embeddings'''	)
	if "patch_embed.proj" in name:
		A_  :				str	   =		name.replace('''patch_embed.proj'''  ,      '''vit.embeddings.patch_embeddings.projection'''	)
	if "patch_embed.norm" in name:
		A_  :				int	   =		name.replace('''patch_embed.norm'''  ,      '''vit.embeddings.norm'''	)
	if "decoder_blocks" in name:
		A_  :				List[Any]	   =		name.replace('''decoder_blocks'''  ,      '''decoder.decoder_layers'''	)
	if "blocks" in name:
		A_  :				str	   =		name.replace('''blocks'''  ,      '''vit.encoder.layer'''	)
	if "attn.proj" in name:
		A_  :				Optional[Any]	   =		name.replace('''attn.proj'''  ,      '''attention.output.dense'''	)
	if "attn" in name:
		A_  :				Union[str, Any]	   =		name.replace('''attn'''  ,      '''attention.self'''	)
	if "norm1" in name:
		A_  :				Optional[Any]	   =		name.replace('''norm1'''  ,      '''layernorm_before'''	)
	if "norm2" in name:
		A_  :				int	   =		name.replace('''norm2'''  ,      '''layernorm_after'''	)
	if "mlp.fc1" in name:
		A_  :				int	   =		name.replace('''mlp.fc1'''  ,      '''intermediate.dense'''	)
	if "mlp.fc2" in name:
		A_  :				Dict	   =		name.replace('''mlp.fc2'''  ,      '''output.dense'''	)
	if "decoder_embed" in name:
		A_  :				List[str]	   =		name.replace('''decoder_embed'''  ,      '''decoder.decoder_embed'''	)
	if "decoder_norm" in name:
		A_  :				Tuple	   =		name.replace('''decoder_norm'''  ,      '''decoder.decoder_norm'''	)
	if "decoder_pred" in name:
		A_  :				Any	   =		name.replace('''decoder_pred'''  ,      '''decoder.decoder_pred'''	)
	if "norm.weight" in name and "decoder" not in name:
		A_  :				List[str]	   =		name.replace('''norm.weight'''  ,      '''vit.layernorm.weight'''	)
	if "norm.bias" in name and "decoder" not in name:
		A_  :				List[str]	   =		name.replace('''norm.bias'''  ,      '''vit.layernorm.bias'''	)

	return name

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	for key in orig_state_dict.copy().keys():
		A_  :				Optional[int]	   =		orig_state_dict.pop(_UpperCAmelCase	)

		if "qkv" in key:
			A_  :				Dict	   =		key.split('''.'''	)
			A_  :				Optional[Any]	   =		int(key_split[1]	)
			if "decoder_blocks" in key:
				A_  :				int	   =		config.decoder_hidden_size
				A_  :				Optional[int]	   =		"""decoder.decoder_layers."""
				if "weight" in key:
					A_  :				Dict	   =		val[:dim, :]
					A_  :				Tuple	   =		val[dim : dim * 2, :]
					A_  :				str	   =		val[-dim:, :]
				elif "bias" in key:
					A_  :				List[Any]	   =		val[:dim]
					A_  :				Tuple	   =		val[dim : dim * 2]
					A_  :				List[Any]	   =		val[-dim:]
			else:
				A_  :				Any	   =		config.hidden_size
				A_  :				Union[str, Any]	   =		"""vit.encoder.layer."""
				if "weight" in key:
					A_  :				Optional[Any]	   =		val[:dim, :]
					A_  :				Optional[Any]	   =		val[dim : dim * 2, :]
					A_  :				Dict	   =		val[-dim:, :]
				elif "bias" in key:
					A_  :				Optional[Any]	   =		val[:dim]
					A_  :				Any	   =		val[dim : dim * 2]
					A_  :				Optional[Any]	   =		val[-dim:]

		else:
			A_  :				Tuple	   =		val

	return orig_state_dict

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Dict	   =		ViTMAEConfig()
	if "large" in checkpoint_url:
		A_  :				List[Any]	   =		1024
		A_  :				Dict	   =		4096
		A_  :				Tuple	   =		24
		A_  :				int	   =		16
	elif "huge" in checkpoint_url:
		A_  :				Union[str, Any]	   =		14
		A_  :				Any	   =		1280
		A_  :				Dict	   =		5120
		A_  :				Optional[int]	   =		32
		A_  :				Optional[Any]	   =		16

	A_  :				Tuple	   =		ViTMAEForPreTraining(_UpperCAmelCase	)

	A_  :				Optional[int]	   =		torch.hub.load_state_dict_from_url(_UpperCAmelCase  ,      map_location='''cpu'''	)["""model"""]

	A_  :				Optional[Any]	   =		ViTMAEImageProcessor(size=config.image_size	)

	A_  :				str	   =		convert_state_dict(_UpperCAmelCase  ,      _UpperCAmelCase	)

	model.load_state_dict(_UpperCAmelCase	)
	model.eval()

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

	A_  :				List[Any]	   =		Image.open(requests.get(_UpperCAmelCase  ,      stream=_UpperCAmelCase	).raw	)
	A_  :				int	   =		ViTMAEImageProcessor(size=config.image_size	)
	A_  :				int	   =		image_processor(images=_UpperCAmelCase  ,      return_tensors='''pt'''	)

	# forward pass
	torch.manual_seed(2	)
	A_  :				Optional[Any]	   =		model(**_UpperCAmelCase	)
	A_  :				Optional[int]	   =		outputs.logits

	if "large" in checkpoint_url:
		A_  :				Dict	   =		torch.tensor(
		    [[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]]	)
	elif "huge" in checkpoint_url:
		A_  :				Tuple	   =		torch.tensor(
		    [[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]]	)
	else:
		A_  :				Any	   =		torch.tensor(
		    [[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]]	)

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

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

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


if __name__ == "__main__":
		_lowerCamelCase  :			Union[str, Any]        =       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.'
		)

		_lowerCamelCase  :			Any        =       parser.parse_args()
		convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)











"""simple docstring"""

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


_lowerCamelCase  :			Any        =       False

class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[str]       ):



					"""simple docstring"""

					return 12







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					return 32







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Union[str, Any]	   =		VQModel(
					    block_out_channels=[32, 64]      ,     in_channels=3      ,     out_channels=3      ,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,     latent_channels=3      ,     num_vq_embeddings=self.num_embed      ,     vq_embed_dim=3      ,     )
					return model







				@property
				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				int	   =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
					return tokenizer







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					torch.manual_seed(0       )
					A_  :				Optional[int]	   =		CLIPTextConfig(
					    bos_token_id=0      ,     eos_token_id=2      ,     hidden_size=self.text_embedder_hidden_size      ,     intermediate_size=37      ,     layer_norm_eps=1E-05      ,     num_attention_heads=4      ,     num_hidden_layers=5      ,     pad_token_id=1      ,     vocab_size=10_00      ,     )
					return CLIPTextModel(_lowerCamelCase       )







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					torch.manual_seed(0       )

					A_  :				Optional[Any]	   =		12
					A_  :				Optional[int]	   =		12

					A_  :				int	   =		{
					    '''attention_bias''': True,
					    '''cross_attention_dim''': 32,
					    '''attention_head_dim''': height * width,
					    '''num_attention_heads''': 1,
					    '''num_vector_embeds''': self.num_embed,
					    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
					    '''norm_num_groups''': 32,
					    '''sample_size''': width,
					    '''activation_fn''': '''geglu-approximate''',
					}

					A_  :				Tuple	   =		TransformeraDModel(**_lowerCamelCase       )
					return model







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				Union[str, Any]	   =		self.dummy_vqvae
					A_  :				str	   =		self.dummy_text_encoder
					A_  :				List[Any]	   =		self.dummy_tokenizer
					A_  :				int	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[Any]	   =		LearnedClassifierFreeSamplingEmbeddings(learnable=_lowerCamelCase       )

					A_  :				Dict	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				List[Any]	   =		'''teddy bear playing in the pool'''

					A_  :				List[Any]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Any	   =		output.images

					A_  :				List[str]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[Any]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

					A_  :				Optional[int]	   =		image[0, -3:, -3:, -1]
					A_  :				Optional[int]	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					A_  :				Optional[int]	   =		np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		'''cpu'''

					A_  :				int	   =		self.dummy_vqvae
					A_  :				List[str]	   =		self.dummy_text_encoder
					A_  :				Optional[Any]	   =		self.dummy_tokenizer
					A_  :				Any	   =		self.dummy_transformer
					A_  :				Any	   =		VQDiffusionScheduler(self.num_embed       )
					A_  :				Optional[int]	   =		LearnedClassifierFreeSamplingEmbeddings(
					    learnable=_lowerCamelCase      ,     hidden_size=self.text_embedder_hidden_size      ,     length=tokenizer.model_max_length       )

					A_  :				int	   =		VQDiffusionPipeline(
					    vqvae=_lowerCamelCase      ,     text_encoder=_lowerCamelCase      ,     tokenizer=_lowerCamelCase      ,     transformer=_lowerCamelCase      ,     scheduler=_lowerCamelCase      ,     learned_classifier_free_sampling_embeddings=_lowerCamelCase      ,     )
					A_  :				List[Any]	   =		pipe.to(_lowerCamelCase       )
					pipe.set_progress_bar_config(disable=_lowerCamelCase       )

					A_  :				Any	   =		'''teddy bear playing in the pool'''

					A_  :				str	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Optional[Any]	   =		pipe([prompt]      ,     generator=_lowerCamelCase      ,     num_inference_steps=2      ,     output_type='''np'''       )
					A_  :				Tuple	   =		output.images

					A_  :				Optional[int]	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				List[str]	   =		pipe(
					    [prompt]      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     return_dict=_lowerCamelCase      ,     num_inference_steps=2       )[0]

					A_  :				Optional[int]	   =		image[0, -3:, -3:, -1]
					A_  :				Optional[Any]	   =		image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 24, 24, 3)

					A_  :				str	   =		np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]       )

					assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2.0
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2

@slow
@require_torch_gpu
class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				str	   =		load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''       )

					A_  :				int	   =		VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''       )
					A_  :				Tuple	   =		pipeline.to(_lowerCamelCase       )
					pipeline.set_progress_bar_config(disable=_lowerCamelCase       )

					# requires GPU generator for gumbel softmax
					# don't use GPU generator in tests though
					A_  :				Dict	   =		torch.Generator(device=_lowerCamelCase       ).manual_seed(0       )
					A_  :				Union[str, Any]	   =		pipeline(
					    '''teddy bear playing in the pool'''      ,     num_images_per_prompt=1      ,     generator=_lowerCamelCase      ,     output_type='''np'''      ,     )

					A_  :				Optional[int]	   =		output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image       ).max() < 2.0





