





'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class     lowerCAmelCase__ (				UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=DiTPipeline
       _lowerCamelCase								=CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       _lowerCamelCase								=PipelineTesterMixin.required_optional_params - {
           "latents",
           "num_images_per_prompt",
           "callback",
           "callback_steps",
       }
       _lowerCamelCase								=CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       _lowerCamelCase								=False

       def 			__snake_case      ( self					:				int						):
              torch.manual_seed(0						)
              UpperCAmelCase  =						TransformeraDModel(
                  sample_size=16       ,     num_layers=2       ,     patch_size=4       ,     attention_head_dim=8       ,     num_attention_heads=2       ,     in_channels=4       ,     out_channels=8       ,     attention_bias=a__       ,     activation_fn='''gelu-approximate'''       ,     num_embeds_ada_norm=1000       ,     norm_type='''ada_norm_zero'''       ,     norm_elementwise_affine=a__       ,     )
              UpperCAmelCase  =						AutoencoderKL()
              UpperCAmelCase  =						DDIMScheduler()
              UpperCAmelCase  =						{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
              return components

       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				List[str]       ,     a__					:				str=0						):
              if str(a__						).startswith('''mps'''						):
                     UpperCAmelCase  =						torch.manual_seed(a__						)
              else:
                     UpperCAmelCase  =						torch.Generator(device=a__						).manual_seed(a__						)
              UpperCAmelCase  =						{
                  '''class_labels''': [1],
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''numpy''',
              }
              return inputs

       def 			__snake_case      ( self					:				int						):
              UpperCAmelCase  =						'''cpu'''

              UpperCAmelCase  =						self.get_dummy_components()
              UpperCAmelCase  =						self.pipeline_class(**a__						)
              pipe.to(a__						)
              pipe.set_progress_bar_config(disable=a__						)

              UpperCAmelCase  =						self.get_dummy_inputs(a__						)
              UpperCAmelCase  =						pipe(**a__						).images
              UpperCAmelCase  =						image[0, -3:, -3:, -1]

              self.assertEqual(image.shape       ,     (1, 16, 16, 3)						)
              UpperCAmelCase  =						np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]						)
              UpperCAmelCase  =						np.abs(image_slice.flatten() - expected_slice						).max()
              self.assertLessEqual(a__       ,     1e-3						)

       def 			__snake_case      ( self					:				Optional[int]						):
              self._test_inference_batch_single_identical(relax_max_difference=a__       ,     expected_max_diff=1e-3						)



       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available()       ,     reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,     )
       def 			__snake_case      ( self					:				Dict						):
              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)






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

       '''simple docstring'''

       def 			__snake_case      ( self					:				Any						):
              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()

       def 			__snake_case      ( self					:				Any						):
              UpperCAmelCase  =						torch.manual_seed(0						)

              UpperCAmelCase  =						DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''						)
              pipe.to('''cuda'''						)

              UpperCAmelCase  =						['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
              UpperCAmelCase  =						pipe.get_label_ids(a__						)

              UpperCAmelCase  =						pipe(a__       ,     generator=a__       ,     num_inference_steps=40       ,     output_type='''np'''						).images

              for word, image in zip(a__       ,     a__						):
                     UpperCAmelCase  =						load_numpy(
                         f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"						)
                     assert np.abs((expected_image - image).max()						) < 1e-2



       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''						)
              UpperCAmelCase  =						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
              pipe.to('''cuda'''						)

              UpperCAmelCase  =						['''vase''', '''umbrella''']
              UpperCAmelCase  =						pipe.get_label_ids(a__						)

              UpperCAmelCase  =						torch.manual_seed(0						)
              UpperCAmelCase  =						pipe(a__       ,     generator=a__       ,     num_inference_steps=25       ,     output_type='''np'''						).images

              for word, image in zip(a__       ,     a__						):
                     UpperCAmelCase  =						load_numpy(
                         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                         f"/dit/{word}_512.npy"						)

                     assert np.abs((expected_image - image).max()						) < 1e-1




import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				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 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(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 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

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

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

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

				print(F'''Saving model to {pytorch_dump_folder_path}'''   )
				model.save_pretrained(snake_case__   )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
				image_processor.save_pretrained(snake_case__   )


if __name__ == "__main__":
						__a         =   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         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




