
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()
lowercase						:      Optional[Any]									= logging.get_logger(__name__)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    str)      ->					List[Any]:






       '''simple docstring'''





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

       __UpperCamelCase			:     Dict         =      key.replace(F'{orig_block_num}.{layer_num}.{original_name}'		,						F'block.{new_block_num}.{layer_num}.{new_name}')
       return key




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int)      ->					str:






       '''simple docstring'''





       __UpperCamelCase			:     Any         =      OrderedDict()
       __UpperCamelCase     ,		__UpperCamelCase			:     int         =      0, 0
       for key, value in state_dict.items():
              if key.startswith("network"):
                     __UpperCamelCase			:     Union[str, Any]         =      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
                     __UpperCamelCase			:     Tuple         =      key[: key.find("proj")]
                     __UpperCamelCase			:     int         =      key.replace(_UpperCamelCase		,						F'patch_embeddings.{total_embed_found}.')
                     __UpperCamelCase			:     Optional[Any]         =      key.replace("proj"		,						"projection")
                     if key.endswith("bias"):
                            total_embed_found += 1
              if "patch_embeddings" in key:
                     __UpperCamelCase			:     Optional[Any]         =      "poolformer.encoder." + key
              if "mlp.fc1" in key:
                     __UpperCamelCase			:     Optional[Any]         =      replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"mlp.fc1"		,						"output.conv1")
              if "mlp.fc2" in key:
                     __UpperCamelCase			:     Tuple         =      replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"mlp.fc2"		,						"output.conv2")
              if "norm1" in key:
                     __UpperCamelCase			:     Dict         =      replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"norm1"		,						"before_norm")
              if "norm2" in key:
                     __UpperCamelCase			:     Optional[int]         =      replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"norm2"		,						"after_norm")
              if "layer_scale_1" in key:
                     __UpperCamelCase			:     List[str]         =      replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"layer_scale_1"		,						"layer_scale_1")
              if "layer_scale_2" in key:
                     __UpperCamelCase			:     List[str]         =      replace_key_with_offset(_UpperCamelCase		,						_UpperCamelCase		,						"layer_scale_2"		,						"layer_scale_2")
              if "head" in key:
                     __UpperCamelCase			:     Any         =      key.replace("head"		,						"classifier")
              __UpperCamelCase			:     Optional[Any]         =      value
       return new_state_dict




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[Any]:






       '''simple docstring'''





       __UpperCamelCase			:     Dict         =      "http://images.cocodataset.org/val2017/000000039769.jpg"
       __UpperCamelCase			:     Optional[Any]         =      Image.open(requests.get(_UpperCamelCase		,						stream=_UpperCamelCase).raw)

       return image




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    int)      ->					int:






       '''simple docstring'''





       __UpperCamelCase			:     Optional[int]         =      PoolFormerConfig()

       # set attributes based on model_name
       __UpperCamelCase			:     Dict         =      "huggingface/label-files"
       __UpperCamelCase			:     List[Any]         =      model_name[-3:]
       __UpperCamelCase			:     Optional[int]         =      1_000
       __UpperCamelCase			:     Optional[int]         =      "imagenet-1k-id2label.json"
       __UpperCamelCase			:     Optional[int]         =      (1, 1_000)

       # set config attributes
       __UpperCamelCase			:     Any         =      json.load(open(hf_hub_download(_UpperCamelCase		,						_UpperCamelCase		,						repo_type="dataset")		,						"r"))
       __UpperCamelCase			:     str         =      {int(_UpperCamelCase): v for k, v in idalabel.items()}
       __UpperCamelCase			:     int         =      idalabel
       __UpperCamelCase			:     Optional[Any]         =      {v: k for k, v in idalabel.items()}
       if size == "s12":
              __UpperCamelCase			:     Optional[Any]         =      [2, 2, 6, 2]
              __UpperCamelCase			:     List[Any]         =      [64, 128, 320, 512]
              __UpperCamelCase			:     List[str]         =      4.0
              __UpperCamelCase			:     Optional[Any]         =      0.9
       elif size == "s24":
              __UpperCamelCase			:     Optional[int]         =      [4, 4, 12, 4]
              __UpperCamelCase			:     Optional[int]         =      [64, 128, 320, 512]
              __UpperCamelCase			:     Any         =      4.0
              __UpperCamelCase			:     Optional[Any]         =      0.9
       elif size == "s36":
              __UpperCamelCase			:     Tuple         =      [6, 6, 18, 6]
              __UpperCamelCase			:     Any         =      [64, 128, 320, 512]
              __UpperCamelCase			:     Any         =      4.0
              __UpperCamelCase			:     Dict         =      1e-6
              __UpperCamelCase			:     Any         =      0.9
       elif size == "m36":
              __UpperCamelCase			:     Optional[Any]         =      [6, 6, 18, 6]
              __UpperCamelCase			:     List[Any]         =      [96, 192, 384, 768]
              __UpperCamelCase			:     Any         =      4.0
              __UpperCamelCase			:     Dict         =      1e-6
              __UpperCamelCase			:     List[str]         =      0.9_5
       elif size == "m48":
              __UpperCamelCase			:     List[str]         =      [8, 8, 24, 8]
              __UpperCamelCase			:     Any         =      [96, 192, 384, 768]
              __UpperCamelCase			:     List[str]         =      4.0
              __UpperCamelCase			:     Union[str, Any]         =      1e-6
              __UpperCamelCase			:     int         =      0.9_5
       else:
              raise ValueError(F'Size {size} not supported')

       # load image processor
       __UpperCamelCase			:     Optional[Any]         =      PoolFormerImageProcessor(crop_pct=_UpperCamelCase)

       # Prepare image
       __UpperCamelCase			:     Union[str, Any]         =      prepare_img()
       __UpperCamelCase			:     Any         =      image_processor(images=_UpperCamelCase		,						return_tensors="pt").pixel_values

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

       # load original state dict
       __UpperCamelCase			:     Tuple         =      torch.load(_UpperCamelCase		,						map_location=torch.device("cpu"))

       # rename keys
       __UpperCamelCase			:     str         =      rename_keys(_UpperCamelCase)

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

       # Define image processor
       __UpperCamelCase			:     int         =      PoolFormerImageProcessor(crop_pct=_UpperCamelCase)
       __UpperCamelCase			:     Tuple         =      image_processor(images=prepare_img()		,						return_tensors="pt").pixel_values

       # forward pass
       __UpperCamelCase			:     Optional[int]         =      model(_UpperCamelCase)
       __UpperCamelCase			:     Tuple         =      outputs.logits

       # define expected logit slices for different models
       if size == "s12":
              __UpperCamelCase			:     Optional[Any]         =      torch.tensor([-0.3_0_4_5, -0.6_7_5_8, -0.4_8_6_9])
       elif size == "s24":
              __UpperCamelCase			:     str         =      torch.tensor([0.4_4_0_2, -0.1_3_7_4, -0.8_0_4_5])
       elif size == "s36":
              __UpperCamelCase			:     Dict         =      torch.tensor([-0.6_0_8_0, -0.5_1_3_3, -0.5_8_9_8])
       elif size == "m36":
              __UpperCamelCase			:     str         =      torch.tensor([0.3_9_5_2, 0.2_2_6_3, -1.2_6_6_8])
       elif size == "m48":
              __UpperCamelCase			:     List[Any]         =      torch.tensor([0.1_1_6_7, -0.0_6_5_6, -0.3_4_2_3])
       else:
              raise ValueError(F'Size {size} not supported')

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

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


if __name__ == "__main__":
  lowercase						:      Any									= 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.'
  )
  lowercase						:      List[str]									= parser.parse_args()
  convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase						:      Any									= logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							warnings.warn(
							    "The preprocess method is deprecated and will be removed in a future version. Please"
							    " use VaeImageProcessor.preprocess instead"		,						_lowerCamelCase		,						)
							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return image
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     int         =      [image]

							if isinstance(image[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      image[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														__UpperCamelCase			:     Any         =      [np.array(i.resize((w, h)		,						resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      np.array(_lowerCamelCase).astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     Union[str, Any]         =      image.transpose(0		,						3		,						1		,						2)
														__UpperCamelCase			:     Optional[int]         =      2.0 * image - 1.0
														__UpperCamelCase			:     List[str]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(image[0]		,						torch.Tensor):
														__UpperCamelCase			:     Dict         =      torch.cat(_lowerCamelCase		,						dim=0)
							return image




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return mask
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     Any         =      [mask]

							if isinstance(mask[0]		,						PIL.Image.Image):
														__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      mask[0].size
														__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														__UpperCamelCase			:     List[str]         =      [np.array(m.convert("L").resize((w, h)		,						resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask]
														__UpperCamelCase			:     Dict         =      np.concatenate(_lowerCamelCase		,						axis=0)
														__UpperCamelCase			:     Any         =      mask.astype(np.floataa) / 2_5_5.0
														__UpperCamelCase			:     List[str]         =      0
														__UpperCamelCase			:     Dict         =      1
														__UpperCamelCase			:     List[Any]         =      torch.from_numpy(_lowerCamelCase)
							elif isinstance(mask[0]		,						torch.Tensor):
														__UpperCamelCase			:     Optional[Any]         =      torch.cat(_lowerCamelCase		,						dim=0)
							return mask




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







	@torch.no_grad()
	def __call__(      self	:Union[str, Any]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:Union[torch.Tensor, PIL.Image.Image]  ,     a	:int = 2_5_0  ,     a	:float = 0.0  ,     a	:int = 1_0  ,     a	:int = 1_0  ,     a	:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     a	:Optional[str] = "pil"  ,     a	:bool = True  ,     )	->				Union[ImagePipelineOutput, Tuple]:
								__UpperCamelCase			:     Tuple         =      image

								__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
								__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
								__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
								__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

								__UpperCamelCase			:     str         =      original_image.shape[0]

								# sample gaussian noise to begin the loop
								if isinstance(a  ,     a		) and len(a		) != batch_size:
															raise ValueError(
															    f'You have passed a list of generators of length {len(a		)}, but requested an effective batch'
															    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
								__UpperCamelCase			:     Tuple         =      eta

								__UpperCamelCase			:     List[str]         =      self.scheduler.timesteps[0] + 1
								__UpperCamelCase			:     str         =      generator[0] if isinstance(a  ,     a		) else generator
								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
															if t < t_last:
																						# predict the noise residual
																						__UpperCamelCase			:     Union[str, Any]         =      self.unet(a  ,     a		).sample
																						# compute previous image: x_t -> x_t-1
																						__UpperCamelCase			:     Tuple         =      self.scheduler.step(a  ,     a  ,     a  ,     a  ,     a  ,     a		).prev_sample

															else:
																						# compute the reverse: x_t-1 -> x_t
																						__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
															__UpperCamelCase			:     Dict         =      t

								__UpperCamelCase			:     Union[str, Any]         =      (image / 2 + 0.5).clamp(0  ,     1		)
								__UpperCamelCase			:     Tuple         =      image.cpu().permute(0  ,     2  ,     3  ,     1		).numpy()
								if output_type == "pil":
															__UpperCamelCase			:     str         =      self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)