






"""simple docstring"""




import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Any             =	SwinvaConfig()
  _a  :       Optional[int]             =	swinva_name.split("""_"""				)

  _a  :       Tuple             =	name_split[1]
  if "to" in name_split[3]:
    _a  :       Union[str, Any]             =	int(name_split[3][-3:]				)
  else:
    _a  :       int             =	int(name_split[3]				)
  if "to" in name_split[2]:
    _a  :       Dict             =	int(name_split[2][-2:]				)
  else:
    _a  :       Optional[int]             =	int(name_split[2][6:]				)

  if model_size == "tiny":
    _a  :       Any             =	9_6
    _a  :       Optional[int]             =	(2, 2, 6, 2)
    _a  :       Dict             =	(3, 6, 1_2, 2_4)
  elif model_size == "small":
    _a  :       Optional[int]             =	9_6
    _a  :       List[Any]             =	(2, 2, 1_8, 2)
    _a  :       int             =	(3, 6, 1_2, 2_4)
  elif model_size == "base":
    _a  :       str             =	1_2_8
    _a  :       int             =	(2, 2, 1_8, 2)
    _a  :       Any             =	(4, 8, 1_6, 3_2)
  else:
    _a  :       Tuple             =	1_9_2
    _a  :       List[Any]             =	(2, 2, 1_8, 2)
    _a  :       Union[str, Any]             =	(6, 1_2, 2_4, 4_8)

  if "to" in swinva_name:
    _a  :       Optional[int]             =	(1_2, 1_2, 1_2, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    _a  :       str             =	2_1_8_4_1
    _a  :       List[Any]             =	"""huggingface/label-files"""
    _a  :       List[str]             =	"""imagenet-22k-id2label.json"""
    _a  :       Union[str, Any]             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
    _a  :       str             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
    _a  :       int             =	idalabel
    _a  :       Optional[int]             =	{v: k for k, v in idalabel.items()}

  else:
    _a  :       str             =	1_0_0_0
    _a  :       int             =	"""huggingface/label-files"""
    _a  :       Optional[int]             =	"""imagenet-1k-id2label.json"""
    _a  :       str             =	json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
    _a  :       Tuple             =	{int(UpperCamelCase__				): v for k, v in idalabel.items()}
    _a  :       Any             =	idalabel
    _a  :       int             =	{v: k for k, v in idalabel.items()}

  _a  :       List[Any]             =	img_size
  _a  :       List[Any]             =	num_classes
  _a  :       List[str]             =	embed_dim
  _a  :       Dict             =	depths
  _a  :       List[str]             =	num_heads
  _a  :       Optional[Any]             =	window_size

  return config

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  if "patch_embed.proj" in name:
    _a  :       str             =	name.replace("""patch_embed.proj"""     ,   """embeddings.patch_embeddings.projection"""				)
  if "patch_embed.norm" in name:
    _a  :       Union[str, Any]             =	name.replace("""patch_embed.norm"""     ,   """embeddings.norm"""				)
  if "layers" in name:
    _a  :       List[Any]             =	"""encoder.""" + name
  if "attn.proj" in name:
    _a  :       Union[str, Any]             =	name.replace("""attn.proj"""     ,   """attention.output.dense"""				)
  if "attn" in name:
    _a  :       List[str]             =	name.replace("""attn"""     ,   """attention.self"""				)
  if "norm1" in name:
    _a  :       str             =	name.replace("""norm1"""     ,   """layernorm_before"""				)
  if "norm2" in name:
    _a  :       List[Any]             =	name.replace("""norm2"""     ,   """layernorm_after"""				)
  if "mlp.fc1" in name:
    _a  :       Any             =	name.replace("""mlp.fc1"""     ,   """intermediate.dense"""				)
  if "mlp.fc2" in name:
    _a  :       Dict             =	name.replace("""mlp.fc2"""     ,   """output.dense"""				)
  if "q_bias" in name:
    _a  :       Tuple             =	name.replace("""q_bias"""     ,   """query.bias"""				)
  if "k_bias" in name:
    _a  :       str             =	name.replace("""k_bias"""     ,   """key.bias"""				)
  if "v_bias" in name:
    _a  :       int             =	name.replace("""v_bias"""     ,   """value.bias"""				)
  if "cpb_mlp" in name:
    _a  :       Union[str, Any]             =	name.replace("""cpb_mlp"""     ,   """continuous_position_bias_mlp"""				)
  if name == "norm.weight":
    _a  :       int             =	"""layernorm.weight"""
  if name == "norm.bias":
    _a  :       List[Any]             =	"""layernorm.bias"""

  if "head" in name:
    _a  :       Dict             =	name.replace("""head"""     ,   """classifier"""				)
  else:
    _a  :       Union[str, Any]             =	"""swinv2.""" + name

  return name

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for key in orig_state_dict.copy().keys():
    _a  :       Optional[Any]             =	orig_state_dict.pop(UpperCamelCase__				)

    if "mask" in key:
      continue
    elif "qkv" in key:
      _a  :       str             =	key.split("""."""				)
      _a  :       Union[str, Any]             =	int(key_split[1]				)
      _a  :       Tuple             =	int(key_split[3]				)
      _a  :       List[str]             =	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        _a  :       Union[str, Any]             =	val[:dim, :]
        _a  :       str             =	val[dim : dim * 2, :]
        _a  :       Union[str, Any]             =	val[-dim:, :]
      else:
        _a  :       Union[str, Any]             =	val[:dim]
        _a  :       Union[str, Any]             =	val[
            dim : dim * 2
        ]
        _a  :       int             =	val[-dim:]
    else:
      _a  :       Union[str, Any]             =	val

  return orig_state_dict

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       int             =	timm.create_model(UpperCamelCase__     ,   pretrained=UpperCamelCase__				)
  timm_model.eval()

  _a  :       Union[str, Any]             =	get_swinva_config(UpperCamelCase__				)
  _a  :       Tuple             =	SwinvaForImageClassification(UpperCamelCase__				)
  model.eval()

  _a  :       int             =	convert_state_dict(timm_model.state_dict()     ,   UpperCamelCase__				)
  model.load_state_dict(UpperCamelCase__				)

  _a  :       List[str]             =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

  _a  :       Optional[Any]             =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""     ,   """-"""				)				)				)
  _a  :       str             =	Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__				).raw				)
  _a  :       Any             =	image_processor(images=UpperCamelCase__     ,   return_tensors="""pt"""				)

  _a  :       Optional[Any]             =	timm_model(inputs["""pixel_values"""]				)
  _a  :       Dict             =	model(**UpperCamelCase__				).logits

  assert torch.allclose(UpperCamelCase__     ,   UpperCamelCase__     ,   atol=1e-3				)

  print(F"""Saving model {swinva_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__				)

  model.push_to_hub(
      repo_path_or_name=Path(UpperCamelCase__     ,   UpperCamelCase__				)     ,   organization="""nandwalritik"""     ,   commit_message="""Add model"""     ,   )


if __name__ == "__main__":
 _snake_case          =   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--swinv2_name',
     default='swinv2_tiny_patch4_window8_256',
     type=str,
     help='Name of the Swinv2 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.'
 )

 _snake_case          =   parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)









"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_snake_case          =   False






class        UpperCamelCase			(	unittest.TestCase      ):
 pass







@nightly
@require_torch_gpu
class        UpperCamelCase			(	unittest.TestCase      ):


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


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Tuple             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[str]             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(UpperCAmelCase__							)
     _a  :       Dict             =	VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	generator.manual_seed(0							)
   _a  :       str             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







 def _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       Optional[int]             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       int             =	"""cyberpunk 2077"""
   _a  :       Tuple             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Any             =	pipe.dual_guided(
       prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							,						).images

   _a  :       List[str]             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[int]             =	np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

   _a  :       int             =	"""A painting of a squirrel eating a burger """
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.text_to_image(
       prompt=UpperCAmelCase__							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							).images

   _a  :       int             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       int             =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

   _a  :       str             =	pipe.image_variation(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       str             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]							)

   assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-1

