
import argparse
import json
from pathlib import Path

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

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Tuple=False							)	->     Optional[Any]:
							lowerCamelCase_					=       []
							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''')							)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""cls_token""", """vit.embeddings.cls_token"""),
							        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]							)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""norm.weight""", """layernorm.weight"""),
														        ("""norm.bias""", """layernorm.bias"""),
														    ]							)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														lowerCamelCase_					=       [(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"""),
														    ]							)

							return rename_keys



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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							lowerCamelCase_					=       ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(UpperCAmelCase__      ,					UpperCAmelCase__							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Dict      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   int							)	->     Tuple:
							lowerCamelCase_					=       dct.pop(UpperCAmelCase__							)
							lowerCamelCase_					=       val



def   __lowerCAmelCase    (				)	->     Any:
							lowerCamelCase_					=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							lowerCamelCase_					=       Image.open(requests.get(UpperCAmelCase__      ,					stream=UpperCAmelCase__							).raw							)
							return im



@torch.no_grad()
def   __lowerCAmelCase    (				UpperCAmelCase__					:   str      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   Tuple=True							)	->     Dict:
							lowerCamelCase_					=       ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														lowerCamelCase_					=       8
							# set labels if required
							if not base_model:
														lowerCamelCase_					=       1_0_0_0
														lowerCamelCase_					=       """huggingface/label-files"""
														lowerCamelCase_					=       """imagenet-1k-id2label.json"""
														lowerCamelCase_					=       json.load(open(hf_hub_download(UpperCAmelCase__      ,					UpperCAmelCase__      ,					repo_type="""dataset"""							)      ,					"""r"""							)							)
														lowerCamelCase_					=       {int(UpperCAmelCase__							): v for k, v in idalabel.items()}
														lowerCamelCase_					=       idalabel
														lowerCamelCase_					=       {v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														lowerCamelCase_					=       3_8_4
														lowerCamelCase_					=       1_5_3_6
														lowerCamelCase_					=       1_2
														lowerCamelCase_					=       6

							# load original model from torch hub
							lowerCamelCase_					=       torch.hub.load("""facebookresearch/dino:main"""      ,					UpperCAmelCase__							)
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							lowerCamelCase_					=       original_model.state_dict()
							if base_model:
														remove_classification_head_(UpperCAmelCase__							)
							lowerCamelCase_					=       create_rename_keys(UpperCAmelCase__      ,					base_model=UpperCAmelCase__							)
							for src, dest in rename_keys:
														rename_key(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__							)
							read_in_q_k_v(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__							)

							# load HuggingFace model
							if base_model:
														lowerCamelCase_					=       ViTModel(UpperCAmelCase__      ,					add_pooling_layer=UpperCAmelCase__							).eval()
							else:
														lowerCamelCase_					=       ViTForImageClassification(UpperCAmelCase__							).eval()
							model.load_state_dict(UpperCAmelCase__							)

							# Check outputs on an image, prepared by ViTImageProcessor
							lowerCamelCase_					=       ViTImageProcessor()
							lowerCamelCase_					=       image_processor(images=prepare_img()      ,					return_tensors="""pt"""							)
							lowerCamelCase_					=       encoding["""pixel_values"""]
							lowerCamelCase_					=       model(UpperCAmelCase__							)

							if base_model:
														lowerCamelCase_					=       original_model(UpperCAmelCase__							)
														assert torch.allclose(UpperCAmelCase__      ,					outputs.last_hidden_state[:, 0, :]      ,					atol=1e-1							)
							else:
														lowerCamelCase_					=       original_model(UpperCAmelCase__							)
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(UpperCAmelCase__      ,					outputs.logits      ,					atol=1e-3							)

							Path(UpperCAmelCase__							).mkdir(exist_ok=UpperCAmelCase__							)
							print(F'''Saving model {model_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__							)


if __name__ == "__main__":
		lowercase   =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''dino_vitb16''',
		    type=str,
		    help='''Name of the model trained with DINO 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(
		    '''--base_model''',
		    action='''store_true''',
		    help='''Whether to only convert the base model (no projection head weights).''',
		)

		parser.set_defaults(base_model=True)
		lowercase   =	parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)







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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     int:
							warnings.warn(
							    """The preprocess method is deprecated and will be removed in a future version. Please"""
							    """ use VaeImageProcessor.preprocess instead"""      ,					UpperCAmelCase__      ,					)
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return image
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [image]

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

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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     Any:
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return mask
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       mask[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														lowerCamelCase_					=       [np.array(m.convert("""L"""							).resize((w, h)      ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       mask.astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       0
														lowerCamelCase_					=       1
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(mask[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return mask





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase ,       scheduler=__UpperCamelCase )



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

												lowerCamelCase_					=       _preprocess_image(__UpperCamelCase )
												lowerCamelCase_					=       original_image.to(device=self.device ,       dtype=self.unet.dtype )
												lowerCamelCase_					=       _preprocess_mask(__UpperCamelCase )
												lowerCamelCase_					=       mask_image.to(device=self.device ,       dtype=self.unet.dtype )

												lowerCamelCase_					=       original_image.shape[0]

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

												lowerCamelCase_					=       original_image.shape
												lowerCamelCase_					=       randn_tensor(__UpperCamelCase ,       generator=__UpperCamelCase ,       device=self.device ,       dtype=self.unet.dtype )

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       self.device )
												lowerCamelCase_					=       eta

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

																			else:
																										# compute the reverse: x_t-1 -> x_t
																										lowerCamelCase_					=       self.scheduler.undo_step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       t

												lowerCamelCase_					=       (image / 2 + 0.5).clamp(0 ,       1 )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 ).numpy()
												if output_type == "pil":
																			lowerCamelCase_					=       self.numpy_to_pil(__UpperCamelCase )

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase )





