

"""simple docstring"""




import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def 	UpperCAmelCase__   (				A__   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			SwinConfig()
   lowerCamelCase__						=			swin_name.split("_"   )

   lowerCamelCase__						=			name_split[1]
   lowerCamelCase__						=			int(name_split[4]   )
   lowerCamelCase__						=			int(name_split[3][-1]   )

   if model_size == "tiny":
      lowerCamelCase__						=			96
      lowerCamelCase__						=			(2, 2, 6, 2)
      lowerCamelCase__						=			(3, 6, 12, 24)
   elif model_size == "small":
      lowerCamelCase__						=			96
      lowerCamelCase__						=			(2, 2, 18, 2)
      lowerCamelCase__						=			(3, 6, 12, 24)
   elif model_size == "base":
      lowerCamelCase__						=			128
      lowerCamelCase__						=			(2, 2, 18, 2)
      lowerCamelCase__						=			(4, 8, 16, 32)
   else:
      lowerCamelCase__						=			192
      lowerCamelCase__						=			(2, 2, 18, 2)
      lowerCamelCase__						=			(6, 12, 24, 48)

   if "in22k" in swin_name:
      lowerCamelCase__						=			2_1841
   else:
      lowerCamelCase__						=			1000
      lowerCamelCase__						=			"huggingface/label-files"
      lowerCamelCase__						=			"imagenet-1k-id2label.json"
      lowerCamelCase__						=			json.load(open(hf_hub_download(A__	,    A__	,    repo_type="dataset"   )	,    "r"   )   )
      lowerCamelCase__						=			{int(A__   ): v for k, v in idalabel.items()}
      lowerCamelCase__						=			idalabel
      lowerCamelCase__						=			{v: k for k, v in idalabel.items()}

   lowerCamelCase__						=			img_size
   lowerCamelCase__						=			num_classes
   lowerCamelCase__						=			embed_dim
   lowerCamelCase__						=			depths
   lowerCamelCase__						=			num_heads
   lowerCamelCase__						=			window_size

   return config



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""


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

   if name == "norm.weight":
      lowerCamelCase__						=			"layernorm.weight"
   if name == "norm.bias":
      lowerCamelCase__						=			"layernorm.bias"

   if "head" in name:
      lowerCamelCase__						=			name.replace("head"	,    "classifier"   )
   else:
      lowerCamelCase__						=			"swin." + name

   return name



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[Any]:




   """simple docstring"""


   for key in orig_state_dict.copy().keys():
      lowerCamelCase__						=			orig_state_dict.pop(A__   )

      if "mask" in key:
         continue
      elif "qkv" in key:
         lowerCamelCase__						=			key.split("."   )
         lowerCamelCase__						=			int(key_split[1]   )
         lowerCamelCase__						=			int(key_split[3]   )
         lowerCamelCase__						=			model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

   return orig_state_dict



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			timm.create_model(A__	,    pretrained=A__   )
   timm_model.eval()

   lowerCamelCase__						=			get_swin_config(A__   )
   lowerCamelCase__						=			SwinForImageClassification(A__   )
   model.eval()

   lowerCamelCase__						=			convert_state_dict(timm_model.state_dict()	,    A__   )
   model.load_state_dict(A__   )

   lowerCamelCase__						=			"http://images.cocodataset.org/val2017/000000039769.jpg"

   lowerCamelCase__						=			AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"	,    "-"   )   )   )
   lowerCamelCase__						=			Image.open(requests.get(A__	,    stream=A__   ).raw   )
   lowerCamelCase__						=			image_processor(images=A__	,    return_tensors="pt"   )

   lowerCamelCase__						=			timm_model(inputs["pixel_values"]   )
   lowerCamelCase__						=			model(**A__   ).logits

   assert torch.allclose(A__	,    A__	,    atol=1E-3   )

   print(f'Saving model {swin_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 __name__ == "__main__":
    SCREAMING_SNAKE_CASE_     :			int	  =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--swin_name''',
        default='''swin_tiny_patch4_window7_224''',
        type=str,
        help='''Name of the Swin timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    SCREAMING_SNAKE_CASE_     :			Any	  =			parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class 		_A       (			__a				,       __a				,       unittest.TestCase					):
      __a								=	IFPipeline
      __a								=	TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      __a								=	TEXT_TO_IMAGE_BATCH_PARAMS
      __a								=	PipelineTesterMixin.required_optional_params - {'latents'}





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         return self._get_dummy_components()





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=0		)   ->					Union[str, Any]:
         if str(SCREAMING_SNAKE_CASE__		).startswith("mps"		):
            lowerCamelCase__						=			torch.manual_seed(SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						=			torch.Generator(device=SCREAMING_SNAKE_CASE__		).manual_seed(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			{
             "prompt": "A painting of a squirrel eating a burger",
             "generator": generator,
             "num_inference_steps": 2,
             "output_type": "numpy",
         }

         return inputs





      def 					_lowerCamelCase  (  self		)   ->					str:
         self._test_save_load_optional_components()





      @unittest.skipIf(torch_device != "cuda"    ,						reason="float16 requires CUDA"		)
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1e-1		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         self._test_save_load_local()





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2    ,						)





      @unittest.skipIf(
          torch_device != "cuda" or not is_xformers_available()    ,						reason="XFormers attention is only available with CUDA and `xformers` installed"    ,						)
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






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





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





      def 					_lowerCamelCase  (  self		)   ->					str:
         # if

         lowerCamelCase__						=			IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"    ,						variant="fp16"    ,						torch_dtype=torch.floataa		)

         lowerCamelCase__						=			IFSuperResolutionPipeline.from_pretrained(
             "DeepFloyd/IF-II-L-v1.0"    ,						variant="fp16"    ,						torch_dtype=torch.floataa    ,						text_encoder=SCREAMING_SNAKE_CASE__    ,						tokenizer=SCREAMING_SNAKE_CASE__		)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to("cuda"		)

         lowerCamelCase__					,       lowerCamelCase__						=			pipe_a.encode_prompt("anime turtle"    ,						device="cuda"		)

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         lowerCamelCase__						=			None
         lowerCamelCase__						=			None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

         self._test_if(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         lowerCamelCase__						=			IFImgaImgPipeline(**pipe_a.components		)
         lowerCamelCase__						=			IFImgaImgSuperResolutionPipeline(**pipe_a.components		)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

         self._test_if_imgaimg(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         lowerCamelCase__						=			IFInpaintingPipeline(**pipe_a.components		)
         lowerCamelCase__						=			IFInpaintingSuperResolutionPipeline(**pipe_a.components		)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

         self._test_if_inpainting(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					int:
         # pipeline 1

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)
         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						generator=SCREAMING_SNAKE_CASE__    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (64, 64, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # pipeline 2

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (256, 256, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         # pipeline 1

         _start_torch_memory_measurement()

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						generator=SCREAMING_SNAKE_CASE__    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (64, 64, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # pipeline 2

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						original_image=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (256, 256, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         # pipeline 1

         _start_torch_memory_measurement()

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(1		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)
         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						mask_image=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						generator=SCREAMING_SNAKE_CASE__    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (64, 64, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # pipeline 2

         _start_torch_memory_measurement()

         lowerCamelCase__						=			torch.Generator(device="cpu"		).manual_seed(0		)

         lowerCamelCase__						=			floats_tensor((1, 3, 64, 64)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(0		)		).to(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			floats_tensor((1, 3, 256, 256)    ,						rng=random.Random(1		)		).to(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			pipe_a(
             prompt_embeds=SCREAMING_SNAKE_CASE__    ,						negative_prompt_embeds=SCREAMING_SNAKE_CASE__    ,						image=SCREAMING_SNAKE_CASE__    ,						mask_image=SCREAMING_SNAKE_CASE__    ,						original_image=SCREAMING_SNAKE_CASE__    ,						generator=SCREAMING_SNAKE_CASE__    ,						num_inference_steps=2    ,						output_type="np"    ,						)

         lowerCamelCase__						=			output.images[0]

         assert image.shape == (256, 256, 3)

         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         lowerCamelCase__						=			load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"		)
         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)






def 	UpperCAmelCase__   (				)      ->      Union[str, Any]:




   """simple docstring"""
   torch.cuda.empty_cache()
   torch.cuda.reset_max_memory_allocated()
   torch.cuda.reset_peak_memory_stats()




