


'''simple docstring'''
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCAmelCase_ :     int								=      transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			)     ->   str:


 if isinstance(UpperCamelCase__,				torch.Tensor			):
  return image
 elif isinstance(UpperCamelCase__,				PIL.Image.Image			):
  __A  :		Union[str, Any]       =			[image]

 __A  :		Optional[int]       =			[trans(img.convert('RGB'			)			) for img in image]
 __A  :		int       =			torch.stack(UpperCamelCase__			)
 return image


class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  super().__init__()

  # make sure scheduler can always be converted to DDIM
  __A  :		List[Any]       =			DDIMScheduler.from_config(scheduler.config						)

  self.register_modules(unet=__lowercase      ,      scheduler=__lowercase						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if strength < 0 or strength > 1:
   raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			min(int(num_inference_steps * strength						)      ,      __lowercase						)

  __A  :		List[str]       =			max(num_inference_steps - init_timestep      ,      0						)
  __A  :		Optional[int]       =			self.scheduler.timesteps[t_start:]

  return timesteps, num_inference_steps - t_start


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=None						):

  """simple docstring"""







  if not isinstance(__lowercase      ,      (torch.Tensor, PIL.Image.Image, list)						):
   raise ValueError(
       F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__lowercase						)}"""						)

  __A  :		Dict       =			image.to(device=__lowercase      ,      dtype=__lowercase						)

  if isinstance(__lowercase      ,      __lowercase						) and len(__lowercase						) != batch_size:
   raise ValueError(
       F"""You have passed a list of generators of length {len(__lowercase						)}, but requested an effective batch"""
       F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""						)

  __A  :		Dict       =			init_latents.shape
  __A  :		Optional[int]       =			randn_tensor(__lowercase      ,      generator=__lowercase      ,      device=__lowercase      ,      dtype=__lowercase						)

  # get latents
  print('add noise to latents at timestep'      ,      __lowercase						)
  __A  :		List[str]       =			self.scheduler.add_noise(__lowercase      ,      __lowercase      ,      __lowercase						)
  __A  :		Tuple       =			init_latents

  return latents







 @torch.no_grad()
 def __call__(      self      ,      __lowercase = None      ,      __lowercase = 0.8      ,      __lowercase = 1      ,      __lowercase = None      ,      __lowercase = 0.0      ,      __lowercase = 50      ,      __lowercase = None      ,      __lowercase = "pil"      ,      __lowercase = True      ,      ):

  """simple docstring"""







  self.check_inputs(__lowercase						)

  # 2. Preprocess image
  __A  :		Dict       =			preprocess(__lowercase						)

  # 3. set timesteps
  self.scheduler.set_timesteps(__lowercase      ,      device=self.device						)
  __A     ,__A  :		Any       =			self.get_timesteps(__lowercase      ,      __lowercase      ,      self.device						)
  __A  :		Dict       =			timesteps[:1].repeat(__lowercase						)

  # 4. Prepare latent variables
  __A  :		int       =			self.prepare_latents(__lowercase      ,      __lowercase      ,      __lowercase      ,      self.unet.dtype      ,      self.device      ,      __lowercase						)
  __A  :		Tuple       =			latents

  # 5. Denoising loop
  for t in self.progress_bar(__lowercase						):
   # 1. predict noise model_output
   __A  :		List[str]       =			self.unet(__lowercase      ,      __lowercase						).sample

   # 2. predict previous mean of image x_t-1 and add variance depending on eta
   # eta corresponds to η in paper and should be between [0, 1]
   # do x_t -> x_t-1
   __A  :		Dict       =			self.scheduler.step(
       __lowercase      ,      __lowercase      ,      __lowercase      ,      eta=__lowercase      ,      use_clipped_model_output=__lowercase      ,      generator=__lowercase      ,      ).prev_sample

  __A  :		Optional[int]       =			(image / 2 + 0.5).clamp(0      ,      1						)
  __A  :		Optional[Any]       =			image.cpu().permute(0      ,      2      ,      3      ,      1						).numpy()
  if output_type == "pil":
   __A  :		Optional[int]       =			self.numpy_to_pil(__lowercase						)

  if not return_dict:
   return (image, latent_timestep.item())

  return ImagePipelineOutput(images=__lowercase						)










'''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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_ :     Tuple								=      logging.get_logger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					Tuple=False			):
 __A  :		List[str]       =			[]

 # fmt: off
 # stem:
 rename_keys.append(('cls_token', 'vit.embeddings.cls_token')			)
 rename_keys.append(('pos_embed', 'vit.embeddings.position_embeddings')			)

 rename_keys.append(('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight')			)
 rename_keys.append(('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias')			)

 # backbone
 rename_keys.append(('patch_embed.backbone.stem.conv.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.weight', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight')			)
 rename_keys.append(('patch_embed.backbone.stem.norm.bias', 'vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias')			)

 for stage_idx in range(len(config.backbone_config.depths			)			):
  for layer_idx in range(config.backbone_config.depths[stage_idx]			):
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")			)
   rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")			)

  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")			)
  rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")			)

 # transformer encoder
 for i in range(config.num_hidden_layers			):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")			)
  rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")			)
  rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")			)
  rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")			)
  rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")			)
  rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")			)

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
          ('pre_logits.fc.weight', 'pooler.dense.weight'),
          ('pre_logits.fc.bias', 'pooler.dense.bias'),
      ]			)

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  __A  :		List[str]       =			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'			) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('norm.weight', 'vit.layernorm.weight'),
          ('norm.bias', 'vit.layernorm.bias'),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]			)
 # fmt: on

 return rename_keys


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					int,				UpperCamelCase__       :					List[Any]=False			):
 for i in range(config.num_hidden_layers			):
  if base_model:
   __A  :		Optional[Any]       =			''
  else:
   __A  :		int       =			'vit.'
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __A  :		str       =			state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""			)
  __A  :		str       =			state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""			)
  # next, add query, keys and values (in that order) to the state dict
  __A  :		List[Any]       =			in_proj_weight[
      : config.hidden_size, :
  ]
  __A  :		List[Any]       =			in_proj_bias[: config.hidden_size]
  __A  :		Optional[int]       =			in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __A  :		int       =			in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __A  :		Dict       =			in_proj_weight[
      -config.hidden_size :, :
  ]
  __A  :		Optional[Any]       =			in_proj_bias[-config.hidden_size :]


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any]			):
 __A  :		Any       =			['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase__,				UpperCamelCase__			)


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Any,				UpperCamelCase__       :					Union[str, Any]			):
 __A  :		Dict       =			dct.pop(UpperCamelCase__			)
 __A  :		Optional[Any]       =			val


def 			_lowercase       (				):
 __A  :		Tuple       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A  :		Optional[Any]       =			Image.open(requests.get(UpperCamelCase__,				stream=UpperCamelCase__			).raw			)
 return im


@torch.no_grad()
def 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Any=False			):
 __A  :		Optional[Any]       =			BitConfig(
     global_padding='same',				layer_type='bottleneck',				depths=(3, 4, 9),				out_features=['stage3'],				embedding_dynamic_padding=UpperCamelCase__,				)
 __A  :		str       =			ViTHybridConfig(backbone_config=UpperCamelCase__,				image_size=384,				num_labels=1000			)
 __A  :		Union[str, Any]       =			False

 # load original model from timm
 __A  :		List[Any]       =			timm.create_model(UpperCamelCase__,				pretrained=UpperCamelCase__			)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __A  :		Optional[int]       =			timm_model.state_dict()
 if base_model:
  remove_classification_head_(UpperCamelCase__			)
 __A  :		List[str]       =			create_rename_keys(UpperCamelCase__,				UpperCamelCase__			)
 for src, dest in rename_keys:
  rename_key(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)
 read_in_q_k_v(UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__			)

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

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __A  :		List[Any]       =			ViTHybridModel(UpperCamelCase__			).eval()
 else:
  __A  :		int       =			ViTHybridForImageClassification(UpperCamelCase__			).eval()
 model.load_state_dict(UpperCamelCase__			)

 # create image processor
 __A  :		Tuple       =			create_transform(**resolve_data_config({},				model=UpperCamelCase__			)			)
 __A  :		Union[str, Any]       =			transform.transforms

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

 __A  :		List[str]       =			ViTHybridImageProcessor(
     do_resize=UpperCamelCase__,				size={'shortest_edge': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=UpperCamelCase__,				crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]},				do_normalize=UpperCamelCase__,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

 __A  :		Optional[Any]       =			prepare_img()
 __A  :		Any       =			transform(UpperCamelCase__			).unsqueeze(0			)
 __A  :		Dict       =			processor(UpperCamelCase__,				return_tensors='pt'			).pixel_values

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

 # verify logits
 with torch.no_grad():
  __A  :		Union[str, Any]       =			model(UpperCamelCase__			)
  __A  :		Dict       =			outputs.logits

 print('Predicted class:',				logits.argmax(-1			).item()			)
 if base_model:
  __A  :		Tuple       =			timm_model.forward_features(UpperCamelCase__			)
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(UpperCamelCase__,				outputs.pooler_output,				atol=1E-3			)
 else:
  __A  :		Any       =			timm_model(UpperCamelCase__			)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCamelCase__,				outputs.logits,				atol=1E-3			)
 print('Looks ok!'			)

 if pytorch_dump_folder_path is not None:
  Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)
  print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
  model.save_pretrained(UpperCamelCase__			)
  print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
  processor.save_pretrained(UpperCamelCase__			)

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


if __name__ == "__main__":
 UpperCAmelCase_ :     Optional[Any]								=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--vit_name',
     default='vit_base_r50_s16_384',
     type=str,
     help='Name of the hybrid ViT 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 upload the model to the HuggingFace hub.'
 )

 UpperCAmelCase_ :     Union[str, Any]								=      parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






