

import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Tuple						)		->				Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      SwinConfig(image_size=1_92						)

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

 SCREAMING_SNAKE_CASE		    =      window_size
 SCREAMING_SNAKE_CASE		    =      embed_dim
 SCREAMING_SNAKE_CASE		    =      depths
 SCREAMING_SNAKE_CASE		    =      num_heads

 return config


def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]						)		->				List[Any]:
 '''simple docstring'''


 if "encoder.mask_token" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('encoder.mask_token'		,				'embeddings.mask_token'						)
 if "encoder.patch_embed.proj" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('encoder.patch_embed.proj'		,				'embeddings.patch_embeddings.projection'						)
 if "encoder.patch_embed.norm" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('encoder.patch_embed.norm'		,				'embeddings.norm'						)
 if "attn.proj" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('attn.proj'		,				'attention.output.dense'						)
 if "attn" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('attn'		,				'attention.self'						)
 if "norm1" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('norm1'		,				'layernorm_before'						)
 if "norm2" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('norm2'		,				'layernorm_after'						)
 if "mlp.fc1" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('mlp.fc1'		,				'intermediate.dense'						)
 if "mlp.fc2" in name:
  SCREAMING_SNAKE_CASE		    =      name.replace('mlp.fc2'		,				'output.dense'						)

 if name == "encoder.norm.weight":
  SCREAMING_SNAKE_CASE		    =      'layernorm.weight'
 if name == "encoder.norm.bias":
  SCREAMING_SNAKE_CASE		    =      'layernorm.bias'

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

 return name


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[int]						)		->				Optional[Any]:
 '''simple docstring'''


 for key in orig_state_dict.copy().keys():
  SCREAMING_SNAKE_CASE		    =      orig_state_dict.pop(_UpperCamelCase						)

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

   if "weight" in key:
    SCREAMING_SNAKE_CASE		    =      val[:dim, :]
    SCREAMING_SNAKE_CASE		    =      val[
        dim : dim * 2, :
    ]
    SCREAMING_SNAKE_CASE		    =      val[-dim:, :]
   else:
    SCREAMING_SNAKE_CASE		    =      val[
        :dim
    ]
    SCREAMING_SNAKE_CASE		    =      val[
        dim : dim * 2
    ]
    SCREAMING_SNAKE_CASE		    =      val[
        -dim:
    ]
  else:
   SCREAMING_SNAKE_CASE		    =      val

 return orig_state_dict


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Any		,				_UpperCamelCase		:	str		,				_UpperCamelCase		:	Union[str, Any]		,				_UpperCamelCase		:	Optional[int]						)		->				Any:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      torch.load(_UpperCamelCase		,				map_location='cpu'						)['model']

 SCREAMING_SNAKE_CASE		    =      get_swin_config(_UpperCamelCase						)
 SCREAMING_SNAKE_CASE		    =      SwinForMaskedImageModeling(_UpperCamelCase						)
 model.eval()

 SCREAMING_SNAKE_CASE		    =      convert_state_dict(_UpperCamelCase		,				_UpperCamelCase						)
 model.load_state_dict(_UpperCamelCase						)

 SCREAMING_SNAKE_CASE		    =      'http://images.cocodataset.org/val2017/000000039769.jpg'

 SCREAMING_SNAKE_CASE		    =      ViTImageProcessor(size={'height': 1_92, 'width': 1_92}						)
 SCREAMING_SNAKE_CASE		    =      Image.open(requests.get(_UpperCamelCase		,				stream=_UpperCamelCase						).raw						)
 SCREAMING_SNAKE_CASE		    =      image_processor(images=_UpperCamelCase		,				return_tensors='pt'						)

 with torch.no_grad():
  SCREAMING_SNAKE_CASE		    =      model(**_UpperCamelCase						).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(_UpperCamelCase						)

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

 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__":
      a_     :    List[Any]     		=   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."
      )

      a_     :    List[str]     		=   parser.parse_args()
      convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)




import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp
      from flax.jax_utils import replicate
      from flax.training.common_utils import shard

      from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline




@require_flax
class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmpdirname:
      # pipeline has Flax weights
      SCREAMING_SNAKE_CASE		    =      FlaxDiffusionPipeline.from_pretrained(
          'hf-internal-testing/tiny-stable-diffusion-pipe'				,     safety_checker=snake_case__				,     cache_dir=snake_case__     )

      SCREAMING_SNAKE_CASE		    =      [t[-1] for t in os.walk(os.path.join(snake_case__				,     os.listdir(snake_case__     )[0]				,     'snapshots'     )     )]
      SCREAMING_SNAKE_CASE		    =      [item for sublist in all_root_files for item in sublist]

      # None of the downloaded files should be a PyTorch file even if we have some here:
      # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
      assert not any(f.endswith('.bin'     ) for f in files     )




@slow
@require_flax
class   UpperCamelCase			(  unittest.TestCase ):

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'hf-internal-testing/tiny-stable-diffusion-pipe'				,     safety_checker=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      4

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 6_4, 6_4, 3)
     if jax.device_count() == 8:
      assert np.abs(np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 4.1_514_745     ) < 1E-3
      assert np.abs(np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 49_947.875     ) < 5E-1

     SCREAMING_SNAKE_CASE		    =      pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]     )     )     )
     assert len(snake_case__     ) == num_samples

    def UpperCamelCase      (      self			:   Union[str, Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='flax'				,     safety_checker=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.05_652_401)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_383_808.2)     ) < 5E-1

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.04_003_906)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_373_516.75)     ) < 5E-1

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa     )

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.04_003_906)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_373_516.75)     ) < 5E-1

    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      FlaxDDIMScheduler(
         beta_start=0.00_085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     set_alpha_to_one=snake_case__				,     steps_offset=1				,     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     scheduler=snake_case__				,     safety_checker=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      scheduler.create_state()

     SCREAMING_SNAKE_CASE		    =      scheduler_state

     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.random.PRNGKey(0     )
     SCREAMING_SNAKE_CASE		    =      5_0

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )

     # shard inputs and rng
     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      jax.random.split(snake_case__				,     snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images

     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     if jax.device_count() == 8:
      assert np.abs((np.abs(images[0, 0, :2, :2, -2:]				,     dtype=np.floataa     ).sum() - 0.045_043_945)     ) < 1E-3
      assert np.abs((np.abs(snake_case__				,     dtype=np.floataa     ).sum() - 2_347_693.5)     ) < 5E-1




    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      (
         'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
         ' field, close up, split lighting, cinematic'
     )

     SCREAMING_SNAKE_CASE		    =      jax.device_count()
     SCREAMING_SNAKE_CASE		    =      num_samples * [prompt]
     SCREAMING_SNAKE_CASE		    =      jax.random.split(jax.random.PRNGKey(0     )				,     snake_case__     )

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images
     assert images.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE		    =      images[2, 0, 2_5_6, 1_0:1_7, 1]

     # With memory efficient attention
     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      FlaxStableDiffusionPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'				,     revision='bf16'				,     dtype=jnp.bfloataa				,     safety_checker=snake_case__				,     use_memory_efficient_attention=snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      replicate(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline.prepare_inputs(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      shard(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      pipeline(snake_case__				,     snake_case__				,     snake_case__				,     jit=snake_case__     ).images
     assert images_eff.shape == (num_samples, 1, 5_1_2, 5_1_2, 3)
     SCREAMING_SNAKE_CASE		    =      images[2, 0, 2_5_6, 1_0:1_7, 1]

     # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
     # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
     assert abs(slice_eff - slice     ).max() < 1E-2

