from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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






class 		a__						(					snake_case_					):

					def __init__(    self      :       Union[str, Any],_A      :       CLIPSegForImageSegmentation,_A      :       CLIPSegProcessor,_A      :       AutoencoderKL,_A      :       CLIPTextModel,_A      :       CLIPTokenizer,_A      :       UNetaDConditionModel,_A      :       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],_A      :       StableDiffusionSafetyChecker,_A      :       CLIPImageProcessor,):







							"""simple docstring"""






							super().__init__()

							if hasattr(scheduler.config,"steps_offset"    ) and scheduler.config.steps_offset != 1:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
									    F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
									    F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
									    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
									    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
									    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
									    ' file'
									)
									deprecate("steps_offset!=1","1.0.0",_A,standard_warn=_A    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    dict(scheduler.config    )
									SCREAMING_SNAKE_CASE_  :		int				      =    1
									SCREAMING_SNAKE_CASE_  :		int				      =    FrozenDict(_A    )

							if hasattr(scheduler.config,"skip_prk_steps"    ) and scheduler.config.skip_prk_steps is False:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    (
									    F'The configuration file of this scheduler: {scheduler} has not set the configuration'
									    ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
									    ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
									    ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
									    ' Hub, it would be very nice if you could open a Pull request for the'
									    ' `scheduler/scheduler_config.json` file'
									)
									deprecate("skip_prk_steps not set","1.0.0",_A,standard_warn=_A    )
									SCREAMING_SNAKE_CASE_  :		Dict				      =    dict(scheduler.config    )
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    True
									SCREAMING_SNAKE_CASE_  :		List[Any]				      =    FrozenDict(_A    )

							if safety_checker is None:
									logger.warning(
									    F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
									    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
									    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
									    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
									    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
									    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."    )

							self.register_modules(
							    segmentation_model=_A,segmentation_processor=_A,vae=_A,text_encoder=_A,tokenizer=_A,unet=_A,scheduler=_A,safety_checker=_A,feature_extractor=_A,)

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Optional[Union[str, int]] = "auto"    ):







							"""simple docstring"""






							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_A    )

					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							self.enable_attention_slicing(_A    )

					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError("Please install accelerate via `pip install accelerate`"    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.device("cuda"    )

							for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
									if cpu_offloaded_model is not None:
											cpu_offload(_A,_A    )

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							if self.device != torch.device("meta"    ) or not hasattr(self.unet,"_hf_hook"    ):
									return self.device
							for module in self.unet.modules():
									if (
									    hasattr(_A,"_hf_hook"    )
									    and hasattr(module._hf_hook,"execution_device"    )
									    and module._hf_hook.execution_device is not None
									):
											return torch.device(module._hf_hook.execution_device    )
							return self.device






					@torch.no_grad()
					def __call__(    self      :       Tuple,_A      :       Union[str, List[str]],_A      :       Union[torch.FloatTensor, PIL.Image.Image],_A      :       str,_A      :       int = 512,_A      :       int = 512,_A      :       int = 50,_A      :       float = 7.5,_A      :       Optional[Union[str, List[str]]] = None,_A      :       Optional[int] = 1,_A      :       float = 0.0,_A      :       Optional[torch.Generator] = None,_A      :       Optional[torch.FloatTensor] = None,_A      :       Optional[str] = "pil",_A      :       bool = True,_A      :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None,_A      :       int = 1,**_A      :       Any,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.segmentation_processor(
							    text=[text],images=[image],padding="max_length",return_tensors="pt"    ).to(self.device    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.segmentation_model(**_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    self.numpy_to_pil(_A    )[0].resize(image.size    )

							# Run inpainting pipeline with the generated mask
							SCREAMING_SNAKE_CASE_  :		int				      =    StableDiffusionInpaintPipeline(
							    vae=self.vae,text_encoder=self.text_encoder,tokenizer=self.tokenizer,unet=self.unet,scheduler=self.scheduler,safety_checker=self.safety_checker,feature_extractor=self.feature_extractor,)
							return inpainting_pipeline(
							    prompt=_A,image=_A,mask_image=_A,height=_A,width=_A,num_inference_steps=_A,guidance_scale=_A,negative_prompt=_A,num_images_per_prompt=_A,eta=_A,generator=_A,latents=_A,output_type=_A,return_dict=_A,callback=_A,callback_steps=_A,)

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def    __lowercase     (						lowerCamelCase					:     Optional[Any]	):
 UpperCamelCase_					:						List[Any] =					'huggingface/label-files'
 UpperCamelCase_					:						int =					'imagenet-1k-id2label.json'
 UpperCamelCase_					:						int =					json.load(open(hf_hub_download(lowerCamelCase  ,  lowerCamelCase  ,  repo_type='dataset'	)  ,  'r'	)	)
 UpperCamelCase_					:						str =					{int(lowerCamelCase	): v for k, v in idalabel.items()}
 UpperCamelCase_					:						Optional[int] =					{v: k for k, v in idalabel.items()}

 UpperCamelCase_					:						str =					'std_conv' if 'bit' in model_name else False

 # note that when using BiT as backbone for ViT-hybrid checkpoints,
 # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
 # config.conv_layer = "std_conv_same"
 UpperCamelCase_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     int	):
 if "stem.conv" in name:
  UpperCamelCase_					:						str =					name.replace('stem.conv'  ,  'bit.embedder.convolution'	)
 if "blocks" in name:
  UpperCamelCase_					:						str =					name.replace('blocks'  ,  'layers'	)
 if "head.fc" in name:
  UpperCamelCase_					:						Dict =					name.replace('head.fc'  ,  'classifier.1'	)
 if name.startswith('norm'	):
  UpperCamelCase_					:						List[str] =					'bit.' + name
 if "bit" not in name and "classifier" not in name:
  UpperCamelCase_					:						int =					'bit.encoder.' + name

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					get_config(lowerCamelCase	)

 # load original model from timm
 UpperCamelCase_					:						Dict =					create_model(lowerCamelCase  ,  pretrained=lowerCamelCase	)
 timm_model.eval()

 # load state_dict of original model
 UpperCamelCase_					:						str =					timm_model.state_dict()
 for key in state_dict.copy().keys():
  UpperCamelCase_					:						Tuple =					state_dict.pop(lowerCamelCase	)
  UpperCamelCase_					:						str =					val.squeeze() if 'head' in key else val

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

 UpperCamelCase_					:						str =					{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

 UpperCamelCase_					:						Tuple =					BitImageProcessor(
     do_resize=lowerCamelCase  ,  size={'shortest_edge': timm_transforms[0].size}  ,  resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,  do_center_crop=lowerCamelCase  ,  crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}  ,  do_normalize=lowerCamelCase  ,  image_mean=timm_transforms[-1].mean.tolist()  ,  image_std=timm_transforms[-1].std.tolist()  ,  )

 UpperCamelCase_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					processor(lowerCamelCase  ,  return_tensors='pt'	).pixel_values

 # verify pixel values
 assert torch.allclose(lowerCamelCase  ,  lowerCamelCase	)

 # verify logits
 with torch.no_grad():
  UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					outputs.logits

 print('Logits:'  ,  logits[0, :3]	)
 print('Predicted class:'  ,  model.config.idalabel[logits.argmax(-1	).item()]	)
 UpperCamelCase_					:						List[Any] =					timm_model(lowerCamelCase	)
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(lowerCamelCase  ,  outputs.logits  ,  atol=1e-3	)
 print('Looks ok!'	)

 if pytorch_dump_folder_path is not None:
  Path(lowerCamelCase	).mkdir(exist_ok=lowerCamelCase	)
  print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"	)
  model.save_pretrained(lowerCamelCase	)
  processor.save_pretrained(lowerCamelCase	)

 if push_to_hub:
  print(F"Pushing model {model_name} and processor to the hub"	)
  model.push_to_hub(F"ybelkada/{model_name}"	)
  processor.push_to_hub(F"ybelkada/{model_name}"	)


if __name__ == "__main__":
   a_    					=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model_name',
       default='resnetv2_50x1_bitm',
       type=str,
       help='Name of the BiT 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.'
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
       help='Whether to push the model to the hub.',
   )

   a_    					=       parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



