






'''simple docstring'''







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor





def 		_UpperCAmelCase (				__A				: List[Any]   ):
				a_		:	int									=   SwinConfig(image_size=1_92   )

				if "base" in model_name:
								a_		:	Dict									=   6
								a_		:	Any									=   1_28
								a_		:	List[Any]									=   (2, 2, 18, 2)
								a_		:	Dict									=   (4, 8, 16, 32)
				elif "large" in model_name:
								a_		:	List[Any]									=   12
								a_		:	List[Any]									=   1_92
								a_		:	Optional[Any]									=   (2, 2, 18, 2)
								a_		:	List[Any]									=   (6, 12, 24, 48)
				else:
								raise ValueError('''Model not supported, only supports base and large variants'''   )

				a_		:	str									=   window_size
				a_		:	Dict									=   embed_dim
				a_		:	List[str]									=   depths
				a_		:	Tuple									=   num_heads

				return config





def 		_UpperCAmelCase (				__A				: Dict   ):
				if "encoder.mask_token" in name:
								a_		:	List[str]									=   name.replace('''encoder.mask_token'''    ,		'''embeddings.mask_token'''   )
				if "encoder.patch_embed.proj" in name:
								a_		:	Optional[int]									=   name.replace('''encoder.patch_embed.proj'''    ,		'''embeddings.patch_embeddings.projection'''   )
				if "encoder.patch_embed.norm" in name:
								a_		:	Optional[int]									=   name.replace('''encoder.patch_embed.norm'''    ,		'''embeddings.norm'''   )
				if "attn.proj" in name:
								a_		:	Union[str, 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[int]									=   name.replace('''norm1'''    ,		'''layernorm_before'''   )
				if "norm2" in name:
								a_		:	Any									=   name.replace('''norm2'''    ,		'''layernorm_after'''   )
				if "mlp.fc1" in name:
								a_		:	List[str]									=   name.replace('''mlp.fc1'''    ,		'''intermediate.dense'''   )
				if "mlp.fc2" in name:
								a_		:	str									=   name.replace('''mlp.fc2'''    ,		'''output.dense'''   )

				if name == "encoder.norm.weight":
								a_		:	List[Any]									=   'layernorm.weight'
				if name == "encoder.norm.bias":
								a_		:	Optional[Any]									=   'layernorm.bias'

				if "decoder" in name:
								pass
				else:
								a_		:	Any									=   'swin.' + name

				return name





def 		_UpperCAmelCase (				__A				: List[Any]    ,		__A				: Tuple   ):
				for key in orig_state_dict.copy().keys():
								a_		:	Tuple									=   orig_state_dict.pop(lowerCAmelCase__   )

								if "attn_mask" in key:
												pass
								elif "qkv" in key:
												a_		:	Any									=   key.split('''.'''   )
												a_		:	int									=   int(key_split[2]   )
												a_		:	Optional[Any]									=   int(key_split[4]   )
												a_		:	List[Any]									=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																a_		:	Optional[int]									=   val[:dim, :]
																a_		:	str									=   val[
																    dim : dim * 2, :
																]
																a_		:	Optional[int]									=   val[-dim:, :]
												else:
																a_		:	Tuple									=   val[
																    :dim
																]
																a_		:	List[str]									=   val[
																    dim : dim * 2
																]
																a_		:	Dict									=   val[
																    -dim:
																]
								else:
												a_		:	List[str]									=   val

				return orig_state_dict





def 		_UpperCAmelCase (				__A				: Any    ,		__A				: str    ,		__A				: List[Any]    ,		__A				: Union[str, Any]   ):
				a_		:	Optional[Any]									=   torch.load(lowerCAmelCase__    ,		map_location='''cpu'''   )['model']

				a_		:	Tuple									=   get_swin_config(lowerCAmelCase__   )
				a_		:	Optional[Any]									=   SwinForMaskedImageModeling(lowerCAmelCase__   )
				model.eval()

				a_		:	int									=   convert_state_dict(lowerCAmelCase__    ,		lowerCAmelCase__   )
				model.load_state_dict(lowerCAmelCase__   )

				a_		:	Union[str, Any]									=   'http://images.cocodataset.org/val2017/000000039769.jpg'

				a_		:	Any									=   ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}   )
				a_		:	int									=   Image.open(requests.get(lowerCAmelCase__    ,		stream=lowerCAmelCase__   ).raw   )
				a_		:	Optional[int]									=   image_processor(images=lowerCAmelCase__    ,		return_tensors='''pt'''   )

				with torch.no_grad():
								a_		:	List[Any]									=   model(**lowerCAmelCase__   ).logits

				print(outputs.keys()   )
				print('''Looks ok!'''   )

				if pytorch_dump_folder_path is not None:
								print(f'Saving model {model_name} to {pytorch_dump_folder_path}'   )
								model.save_pretrained(lowerCAmelCase__   )

								print(f'Saving image processor to {pytorch_dump_folder_path}'   )
								image_processor.save_pretrained(lowerCAmelCase__   )

				if push_to_hub:
								print(f'Pushing model and image processor for {model_name} to hub'   )
								model.push_to_hub(f'microsoft/{model_name}'   )
								image_processor.push_to_hub(f'microsoft/{model_name}'   )


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='swin-base-simmim-window6-192',
		    type=str,
		    choices=['swin-base-simmim-window6-192', 'swin-large-simmim-window12-192'],
		    help='Name of the Swin SimMIM model you\'d like to convert.',
		)
		parser.add_argument(
		    '--checkpoint_path',
		    default='/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth',
		    type=str,
		    help='Path to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
		)
		parser.add_argument(
		    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
		)

		__lowerCAmelCase									=   parser.parse_args()
		convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)











import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowercase		:  Any			       =False






@skip_mps
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						Optional[int]						    =					StableDiffusionAttendAndExcitePipeline
					_a      :						Union[str, Any]						    =					False
					_a      :						Dict						    =					TEXT_TO_IMAGE_PARAMS
					_a      :						List[str]						    =					TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}							)
					_a      :						Optional[int]						    =					TEXT_TO_IMAGE_IMAGE_PARAMS
					_a      :						List[Any]						    =					TEXT_TO_IMAGE_IMAGE_PARAMS



					@classmethod
					def   __a     (		cls     :     Tuple						):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     Tuple						):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							torch.manual_seed(0						)
							lowerCamelCase_	:     int 						=      UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,      layers_per_block=1       ,      sample_size=32       ,      in_channels=4       ,      out_channels=4       ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,      cross_attention_dim=32       ,      attention_head_dim=(2, 4)       ,      use_linear_projection=lowerCamelCase       ,      )
							lowerCamelCase_	:     Dict 						=      DDIMScheduler(
							    beta_start=0.00_085       ,      beta_end=0.012       ,      beta_schedule='scaled_linear'       ,      clip_sample=lowerCamelCase       ,      set_alpha_to_one=lowerCamelCase       ,      )
							torch.manual_seed(0						)
							lowerCamelCase_	:     int 						=      AutoencoderKL(
							    block_out_channels=[32, 64]       ,      in_channels=3       ,      out_channels=3       ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,      latent_channels=4       ,      sample_size=1_28       ,      )
							torch.manual_seed(0						)
							lowerCamelCase_	:     List[Any] 						=      CLIPTextConfig(
							    bos_token_id=0       ,      eos_token_id=2       ,      hidden_size=32       ,      intermediate_size=37       ,      layer_norm_eps=1E-05       ,      num_attention_heads=4       ,      num_hidden_layers=5       ,      pad_token_id=1       ,      vocab_size=10_00       ,      hidden_act='gelu'       ,      projection_dim=5_12       ,      )
							lowerCamelCase_	:     Any 						=      CLIPTextModel(lowerCamelCase						)
							lowerCamelCase_	:     int 						=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

							lowerCamelCase_	:     Optional[Any] 						=      {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]=0						):
							if str(lowerCamelCase						).startswith('mps'						):
									lowerCamelCase_	:     Union[str, Any] 						=      torch.manual_seed(lowerCamelCase						)
							else:
									lowerCamelCase_	:     Optional[Any] 						=      torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[Any] 						=      'cpu'

							lowerCamelCase_	:     List[Any] 						=      self.get_dummy_components()
							lowerCamelCase_	:     List[str] 						=      self.pipeline_class(**lowerCamelCase						)
							pipe.to(lowerCamelCase						)
							pipe.set_progress_bar_config(disable=lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      self.get_dummy_inputs(lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      pipe(**lowerCamelCase						).images
							lowerCamelCase_	:     Optional[Any] 						=      image[0, -3:, -3:, -1]

							self.assertEqual(image.shape       ,      (1, 64, 64, 3)						)
							lowerCamelCase_	:     Dict 						=      np.array(
							    [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]						)
							lowerCamelCase_	:     Any 						=      np.abs(image_slice.flatten() - expected_slice						).max()
							self.assertLessEqual(lowerCamelCase       ,      1E-3						)



					def   __a     (		self     :     Tuple						):
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4						)



					def   __a     (		self     :     Dict						):
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]						)



					def   __a     (		self     :     List[str]						):
							self._test_inference_batch_single_identical(batch_size=2       ,      expected_max_diff=7E-4						)



					def   __a     (		self     :     str						):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)



					def   __a     (		self     :     Optional[Any]						):
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4						)



					def   __a     (		self     :     Tuple						):
							super().test_save_load_local(expected_max_difference=5E-4						)





					def   __a     (		self     :     List[Any]						):
							super().test_save_load_optional_components(expected_max_difference=4E-4						)






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



					@classmethod
					def   __a     (		cls     :     Union[str, Any]						):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     List[Any]						):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					def   __a     (		self     :     List[str]						):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





					def   __a     (		self     :     str						):
							lowerCamelCase_	:     Optional[Any] 						=      torch.manual_seed(51						)

							lowerCamelCase_	:     Any 						=      StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,      safety_checker=lowerCamelCase       ,      torch_dtype=torch.floataa						)
							pipe.to('cuda'						)

							lowerCamelCase_	:     List[Any] 						=      'a painting of an elephant with glasses'
							lowerCamelCase_	:     Tuple 						=      [5, 7]

							lowerCamelCase_	:     List[str] 						=      pipe(
							    prompt=lowerCamelCase       ,      token_indices=lowerCamelCase       ,      guidance_scale=7.5       ,      generator=lowerCamelCase       ,      num_inference_steps=5       ,      max_iter_to_alter=5       ,      output_type='numpy'       ,      ).images[0]

							lowerCamelCase_	:     Optional[Any] 						=      load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'						)
							assert np.abs((expected_image - image).max()						) < 5E-1
