






'''simple docstring'''

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def 				snake_case       (  a_				:			Dict						)							->					Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_					: str            =				SwinConfig(image_size=192						)

      if "base" in model_name:
            UpperCamelCase_					: List[Any]            =				6
            UpperCamelCase_					: List[str]            =				128
            UpperCamelCase_					: int            =				(2, 2, 18, 2)
            UpperCamelCase_					: List[str]            =				(4, 8, 16, 32)
      elif "large" in model_name:
            UpperCamelCase_					: Optional[Any]            =				12
            UpperCamelCase_					: str            =				192
            UpperCamelCase_					: int            =				(2, 2, 18, 2)
            UpperCamelCase_					: List[str]            =				(6, 12, 24, 48)
      else:
            raise ValueError("""Model not supported, only supports base and large variants"""						)

      UpperCamelCase_					: Union[str, Any]            =				window_size
      UpperCamelCase_					: Any            =				embed_dim
      UpperCamelCase_					: Optional[Any]            =				depths
      UpperCamelCase_					: str            =				num_heads

      return config



def 				snake_case       (  a_				:			int						)							->					str:





      """simple docstring"""

      if "encoder.mask_token" in name:
            UpperCamelCase_					: List[Any]            =				name.replace("""encoder.mask_token"""     , """embeddings.mask_token"""						)
      if "encoder.patch_embed.proj" in name:
            UpperCamelCase_					: Optional[int]            =				name.replace("""encoder.patch_embed.proj"""     , """embeddings.patch_embeddings.projection"""						)
      if "encoder.patch_embed.norm" in name:
            UpperCamelCase_					: List[str]            =				name.replace("""encoder.patch_embed.norm"""     , """embeddings.norm"""						)
      if "attn.proj" in name:
            UpperCamelCase_					: int            =				name.replace("""attn.proj"""     , """attention.output.dense"""						)
      if "attn" in name:
            UpperCamelCase_					: int            =				name.replace("""attn"""     , """attention.self"""						)
      if "norm1" in name:
            UpperCamelCase_					: List[str]            =				name.replace("""norm1"""     , """layernorm_before"""						)
      if "norm2" in name:
            UpperCamelCase_					: int            =				name.replace("""norm2"""     , """layernorm_after"""						)
      if "mlp.fc1" in name:
            UpperCamelCase_					: Optional[int]            =				name.replace("""mlp.fc1"""     , """intermediate.dense"""						)
      if "mlp.fc2" in name:
            UpperCamelCase_					: List[Any]            =				name.replace("""mlp.fc2"""     , """output.dense"""						)

      if name == "encoder.norm.weight":
            UpperCamelCase_					: Optional[int]            =				"""layernorm.weight"""
      if name == "encoder.norm.bias":
            UpperCamelCase_					: int            =				"""layernorm.bias"""

      if "decoder" in name:
            pass
      else:
            UpperCamelCase_					: Dict            =				"""swin.""" + name

      return name



def 				snake_case       (  a_				:			int     , a_				:			List[str]						)							->					Any:





      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            UpperCamelCase_					: Dict            =				orig_state_dict.pop(a_						)

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

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

      return orig_state_dict



def 				snake_case       (  a_				:			List[str]     , a_				:			str     , a_				:			Union[str, Any]     , a_				:			Optional[int]						)							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: List[Any]            =				torch.load(a_     , map_location="""cpu"""						)["""model"""]

      UpperCamelCase_					: Dict            =				get_swin_config(a_						)
      UpperCamelCase_					: Dict            =				SwinForMaskedImageModeling(a_						)
      model.eval()

      UpperCamelCase_					: Tuple            =				convert_state_dict(a_     , a_						)
      model.load_state_dict(a_						)

      UpperCamelCase_					: Optional[Any]            =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""

      UpperCamelCase_					: Dict            =				ViTImageProcessor(size={"""height""": 192, """width""": 192}						)
      UpperCamelCase_					: Dict            =				Image.open(requests.get(a_     , stream=a_						).raw						)
      UpperCamelCase_					: Tuple            =				image_processor(images=a_     , return_tensors="""pt"""						)

      with torch.no_grad():
            UpperCamelCase_					: Dict            =				model(**a_						).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(a_						)

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

      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__":
      UpperCamelCase      					=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."
      )

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














'''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


UpperCamelCase      					=False




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 32
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: 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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
       return tokenizer
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Any            =				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 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)

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

       UpperCamelCase_					: Optional[Any]            =				{
           """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""",
       }

       UpperCamelCase_					: Optional[Any]            =				TransformeraDModel(**__lowerCAmelCase			)
       return model
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"""cpu"""

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

       UpperCamelCase_					: Tuple            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: Union[str, Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: Dict            =				output.images

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Dict            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: Any            =				image_from_tuple[0, -3:, -3:, -1]

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

       UpperCamelCase_					: List[str]            =				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]			)

       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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: int            =				"""cpu"""

       UpperCamelCase_					: List[Any]            =				self.dummy_vqvae
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_text_encoder
       UpperCamelCase_					: int            =				self.dummy_tokenizer
       UpperCamelCase_					: Tuple            =				self.dummy_transformer
       UpperCamelCase_					: Tuple            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: List[Any]            =				LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCAmelCase      ,  hidden_size=self.text_embedder_hidden_size      ,  length=tokenizer.model_max_length			)

       UpperCamelCase_					: Optional[Any]            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: List[str]            =				output.images

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

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

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

       UpperCamelCase_					: List[Any]            =				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]			)

       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      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

       UpperCamelCase_					: Optional[int]            =				VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
       UpperCamelCase_					: str            =				pipeline.to(__lowerCAmelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

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

       UpperCamelCase_					: Union[str, Any]            =				output.images[0]

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






