




"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_						=				logging.get_logger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Any		,   snake_case__							:					List[str]		,   snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		Dict								=  original_name.split("""."""    )[0]
  _snake_case							:		Union[str, Any]								=  key.split("""."""    )
  _snake_case							:		Optional[Any]								=  int(key_list[key_list.index(snake_case__    ) - 2]    )
  _snake_case							:		str								=  int(key_list[key_list.index(snake_case__    ) - 1]    )
  _snake_case							:		Any								=  orig_block_num - offset

  _snake_case							:		List[str]								=  key.replace(F"{orig_block_num}.{layer_num}.{original_name}"		,   F"block.{new_block_num}.{layer_num}.{new_name}"    )
  return key


def 					UpperCAmelCase__       (snake_case__							:					Tuple    ):
  """simple docstring"""

  _snake_case							:		int								=  OrderedDict()
  _snake_case		,					_snake_case							:		Union[str, Any]								=  0, 0
  for key, value in state_dict.items():
    if key.startswith("""network"""    ):
      _snake_case							:		int								=  key.replace("""network"""		,   """poolformer.encoder"""    )
    if "proj" in key:
      # Works for the first embedding as well as the internal embedding layers
      if key.endswith("""bias"""    ) and "patch_embed" not in key:
        patch_emb_offset += 1
      _snake_case							:		Optional[int]								=  key[: key.find("""proj"""    )]
      _snake_case							:		List[Any]								=  key.replace(snake_case__		,   F"patch_embeddings.{total_embed_found}."    )
      _snake_case							:		Dict								=  key.replace("""proj"""		,   """projection"""    )
      if key.endswith("""bias"""    ):
        total_embed_found += 1
    if "patch_embeddings" in key:
      _snake_case							:		Tuple								=  """poolformer.encoder.""" + key
    if "mlp.fc1" in key:
      _snake_case							:		int								=  replace_key_with_offset(snake_case__		,   snake_case__		,   """mlp.fc1"""		,   """output.conv1"""    )
    if "mlp.fc2" in key:
      _snake_case							:		Dict								=  replace_key_with_offset(snake_case__		,   snake_case__		,   """mlp.fc2"""		,   """output.conv2"""    )
    if "norm1" in key:
      _snake_case							:		Tuple								=  replace_key_with_offset(snake_case__		,   snake_case__		,   """norm1"""		,   """before_norm"""    )
    if "norm2" in key:
      _snake_case							:		Dict								=  replace_key_with_offset(snake_case__		,   snake_case__		,   """norm2"""		,   """after_norm"""    )
    if "layer_scale_1" in key:
      _snake_case							:		Optional[int]								=  replace_key_with_offset(snake_case__		,   snake_case__		,   """layer_scale_1"""		,   """layer_scale_1"""    )
    if "layer_scale_2" in key:
      _snake_case							:		int								=  replace_key_with_offset(snake_case__		,   snake_case__		,   """layer_scale_2"""		,   """layer_scale_2"""    )
    if "head" in key:
      _snake_case							:		Optional[int]								=  key.replace("""head"""		,   """classifier"""    )
    _snake_case							:		List[Any]								=  value
  return new_state_dict


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		int								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _snake_case							:		List[str]								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )

  return image


@torch.no_grad()
def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					Optional[int]		,   snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  PoolFormerConfig()

  # set attributes based on model_name
  _snake_case							:		Tuple								=  """huggingface/label-files"""
  _snake_case							:		int								=  model_name[-3:]
  _snake_case							:		List[Any]								=  10_00
  _snake_case							:		List[str]								=  """imagenet-1k-id2label.json"""
  _snake_case							:		Any								=  (1, 10_00)

  # set config attributes
  _snake_case							:		Union[str, Any]								=  json.load(open(hf_hub_download(snake_case__		,   snake_case__		,   repo_type="""dataset"""    )		,   """r"""    )    )
  _snake_case							:		List[Any]								=  {int(snake_case__    ): v for k, v in idalabel.items()}
  _snake_case							:		List[str]								=  idalabel
  _snake_case							:		List[Any]								=  {v: k for k, v in idalabel.items()}
  if size == "s12":
    _snake_case							:		Dict								=  [2, 2, 6, 2]
    _snake_case							:		Dict								=  [64, 1_28, 3_20, 5_12]
    _snake_case							:		Dict								=  4.0
    _snake_case							:		Tuple								=  0.9
  elif size == "s24":
    _snake_case							:		Tuple								=  [4, 4, 12, 4]
    _snake_case							:		int								=  [64, 1_28, 3_20, 5_12]
    _snake_case							:		List[Any]								=  4.0
    _snake_case							:		int								=  0.9
  elif size == "s36":
    _snake_case							:		Tuple								=  [6, 6, 18, 6]
    _snake_case							:		Optional[int]								=  [64, 1_28, 3_20, 5_12]
    _snake_case							:		Optional[int]								=  4.0
    _snake_case							:		Union[str, Any]								=  1e-6
    _snake_case							:		List[str]								=  0.9
  elif size == "m36":
    _snake_case							:		Any								=  [6, 6, 18, 6]
    _snake_case							:		Union[str, Any]								=  [96, 1_92, 3_84, 7_68]
    _snake_case							:		Any								=  4.0
    _snake_case							:		Union[str, Any]								=  1e-6
    _snake_case							:		int								=  0.95
  elif size == "m48":
    _snake_case							:		Dict								=  [8, 8, 24, 8]
    _snake_case							:		Optional[int]								=  [96, 1_92, 3_84, 7_68]
    _snake_case							:		Optional[int]								=  4.0
    _snake_case							:		Dict								=  1e-6
    _snake_case							:		List[Any]								=  0.95
  else:
    raise ValueError(F"Size {size} not supported"    )

  # load image processor
  _snake_case							:		str								=  PoolFormerImageProcessor(crop_pct=snake_case__    )

  # Prepare image
  _snake_case							:		Dict								=  prepare_img()
  _snake_case							:		Optional[int]								=  image_processor(images=snake_case__		,   return_tensors="""pt"""    ).pixel_values

  logger.info(F"Converting model {model_name}..."    )

  # load original state dict
  _snake_case							:		int								=  torch.load(snake_case__		,   map_location=torch.device("""cpu"""    )    )

  # rename keys
  _snake_case							:		Tuple								=  rename_keys(snake_case__    )

  # create HuggingFace model and load state dict
  _snake_case							:		str								=  PoolFormerForImageClassification(snake_case__    )
  model.load_state_dict(snake_case__    )
  model.eval()

  # Define image processor
  _snake_case							:		Union[str, Any]								=  PoolFormerImageProcessor(crop_pct=snake_case__    )
  _snake_case							:		Optional[Any]								=  image_processor(images=prepare_img()		,   return_tensors="""pt"""    ).pixel_values

  # forward pass
  _snake_case							:		List[Any]								=  model(snake_case__    )
  _snake_case							:		Tuple								=  outputs.logits

  # define expected logit slices for different models
  if size == "s12":
    _snake_case							:		List[str]								=  torch.tensor([-0.30_45, -0.67_58, -0.48_69]    )
  elif size == "s24":
    _snake_case							:		Any								=  torch.tensor([0.44_02, -0.13_74, -0.80_45]    )
  elif size == "s36":
    _snake_case							:		List[Any]								=  torch.tensor([-0.60_80, -0.51_33, -0.58_98]    )
  elif size == "m36":
    _snake_case							:		List[str]								=  torch.tensor([0.39_52, 0.22_63, -1.26_68]    )
  elif size == "m48":
    _snake_case							:		Union[str, Any]								=  torch.tensor([0.11_67, -0.06_56, -0.34_23]    )
  else:
    raise ValueError(F"Size {size} not supported"    )

  # verify logits
  assert logits.shape == expected_shape
  assert torch.allclose(logits[0, :3]		,   snake_case__		,   atol=1e-2    )

  # finally, save model and image processor
  logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."    )
  Path(snake_case__    ).mkdir(exist_ok=snake_case__    )
  model.save_pretrained(snake_case__    )
  print(F"Saving image processor to {pytorch_dump_folder_path}"    )
  image_processor.save_pretrained(snake_case__    )


if __name__ == "__main__":
     A_						=				argparse.ArgumentParser()

     parser.add_argument(
         '''--model_name''',
         default='''poolformer_s12''',
         type=str,
         help='''Name of the model you\'d like to convert.''',
     )
     parser.add_argument(
         '''--checkpoint_path''', default=None, 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 folder to output PyTorch model.'''
     )
     A_						=				parser.parse_args()
     convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort






@nightly
@require_onnxruntime
@require_torch_gpu
class 					lowercase(     unittest.TestCase ):







 '''simple docstring'''





 @property
 def  UpperCamelCase_   (				self:		Optional[Any]		):




   '''simple docstring'''



   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )


 @property
 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Any								=  ort.SessionOptions()
   _snake_case							:		Union[str, Any]								=  False
   return options


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo.png"""		)
   _snake_case							:		Union[str, Any]								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""		)
   _snake_case							:		Union[str, Any]								=  load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy"""		)

   # using the PNDM scheduler by default
   _snake_case							:		Optional[Any]								=  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
       """CompVis/stable-diffusion-v1-4""",      revision="""onnx""",      safety_checker=a_,      feature_extractor=a_,      provider=self.gpu_provider,      sess_options=self.gpu_options,      )
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		Optional[Any]								=  """A red cat sitting on a park bench"""

   _snake_case							:		Optional[int]								=  np.random.RandomState(0		)
   _snake_case							:		Any								=  pipe(
       prompt=a_,      image=a_,      mask_image=a_,      strength=0.75,      guidance_scale=7.5,      num_inference_steps=15,      generator=a_,      output_type="""np""",      )

   _snake_case							:		Dict								=  output.images[0]

   assert image.shape == (512, 512, 3)
   assert np.abs(expected_image - image		).max() < 1E-2


