






'''simple docstring'''







import argparse
import json

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

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> int:
							lowerCamelCase__      :			str 						= []
							for i in range(config.num_hidden_layers   ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f'''module.blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')   )
														rename_keys.append((f'''module.blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')   )
														rename_keys.append(
														    (f'''module.blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')   )
														rename_keys.append((f'''module.blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')   )
														rename_keys.append((f'''module.blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')   )
														rename_keys.append((f'''module.blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')   )
														rename_keys.append((f'''module.blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')   )
														rename_keys.append((f'''module.blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')   )
														rename_keys.append((f'''module.blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')   )
														rename_keys.append((f'''module.blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')   )

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

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

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														lowerCamelCase__      :			Any 						= [(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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=False   )   -> Optional[int]:
							for i in range(config.num_hidden_layers   ):
														if base_model:
																					lowerCamelCase__      :			Dict 						= """"""
														else:
																					lowerCamelCase__      :			List[str] 						= """vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														lowerCamelCase__      :			Any 						= state_dict.pop(f'''module.blocks.{i}.attn.qkv.weight'''   )
														lowerCamelCase__      :			str 						= state_dict.pop(f'''module.blocks.{i}.attn.qkv.bias'''   )
														# next, add query, keys and values (in that order) to the state dict
														lowerCamelCase__      :			str 						= in_proj_weight[
														    : config.hidden_size, :
														]
														lowerCamelCase__      :			Union[str, Any] 						= in_proj_bias[: config.hidden_size]
														lowerCamelCase__      :			List[Any] 						= in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														lowerCamelCase__      :			Any 						= in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														lowerCamelCase__      :			Optional[int] 						= in_proj_weight[
														    -config.hidden_size :, :
														]
														lowerCamelCase__      :			Tuple 						= in_proj_bias[-config.hidden_size :]





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							lowerCamelCase__      :			List[Any] 						= ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							# projection head is used in the self-supervised pre-training in MSN,
							# for downstream task it's not needed.
							lowerCamelCase__      :			Any 						= [
							    """module.fc.fc1.weight""",
							    """module.fc.fc1.bias""",
							    """module.fc.bn1.weight""",
							    """module.fc.bn1.bias""",
							    """module.fc.bn1.running_mean""",
							    """module.fc.bn1.running_var""",
							    """module.fc.bn1.num_batches_tracked""",
							    """module.fc.fc2.weight""",
							    """module.fc.fc2.bias""",
							    """module.fc.bn2.weight""",
							    """module.fc.bn2.bias""",
							    """module.fc.bn2.running_mean""",
							    """module.fc.bn2.running_var""",
							    """module.fc.bn2.num_batches_tracked""",
							    """module.fc.fc3.weight""",
							    """module.fc.fc3.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Optional[int]:
							lowerCamelCase__      :			List[str] 						= dct.pop(UpperCamelCase   )
							lowerCamelCase__      :			List[str] 						= val





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[int]:
							lowerCamelCase__      :			int 						= ViTMSNConfig()
							lowerCamelCase__      :			List[str] 						= 1000

							lowerCamelCase__      :			Dict 						= """datasets/huggingface/label-files"""
							lowerCamelCase__      :			Optional[Any] 						= """imagenet-1k-id2label.json"""
							lowerCamelCase__      :			List[str] 						= json.load(open(hf_hub_download(UpperCamelCase       ,    UpperCamelCase   )       ,    """r"""   )   )
							lowerCamelCase__      :			Tuple 						= {int(UpperCamelCase   ): v for k, v in idalabel.items()}
							lowerCamelCase__      :			Union[str, Any] 						= idalabel
							lowerCamelCase__      :			List[str] 						= {v: k for k, v in idalabel.items()}

							if "s16" in checkpoint_url:
														lowerCamelCase__      :			Optional[Any] 						= 384
														lowerCamelCase__      :			Tuple 						= 1536
														lowerCamelCase__      :			Tuple 						= 6
							elif "l16" in checkpoint_url:
														lowerCamelCase__      :			Tuple 						= 1024
														lowerCamelCase__      :			Tuple 						= 4096
														lowerCamelCase__      :			List[Any] 						= 24
														lowerCamelCase__      :			Optional[Any] 						= 16
														lowerCamelCase__      :			Dict 						= 0.1
							elif "b4" in checkpoint_url:
														lowerCamelCase__      :			List[str] 						= 4
							elif "l7" in checkpoint_url:
														lowerCamelCase__      :			Union[str, Any] 						= 7
														lowerCamelCase__      :			List[str] 						= 1024
														lowerCamelCase__      :			Any 						= 4096
														lowerCamelCase__      :			Tuple 						= 24
														lowerCamelCase__      :			Optional[int] 						= 16
														lowerCamelCase__      :			List[str] 						= 0.1

							lowerCamelCase__      :			int 						= ViTMSNModel(UpperCamelCase   )

							lowerCamelCase__      :			str 						= torch.hub.load_state_dict_from_url(UpperCamelCase       ,    map_location="""cpu"""   )["""target_encoder"""]

							lowerCamelCase__      :			Tuple 						= ViTImageProcessor(size=config.image_size   )

							remove_projection_head(UpperCamelCase   )
							lowerCamelCase__      :			Optional[int] 						= 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       ,    base_model=UpperCamelCase   )

							model.load_state_dict(UpperCamelCase   )
							model.eval()

							lowerCamelCase__      :			Tuple 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""

							lowerCamelCase__      :			str 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							lowerCamelCase__      :			Dict 						= ViTImageProcessor(
							    size=config.image_size       ,    image_mean=UpperCamelCase       ,    image_std=UpperCamelCase   )
							lowerCamelCase__      :			str 						= image_processor(images=UpperCamelCase       ,    return_tensors="""pt"""   )

							# forward pass
							torch.manual_seed(2   )
							lowerCamelCase__      :			Optional[Any] 						= model(**UpperCamelCase   )
							lowerCamelCase__      :			int 						= outputs.last_hidden_state

							# The following Colab Notebook was used to generate these outputs:
							# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
							if "s16" in checkpoint_url:
														lowerCamelCase__      :			Union[str, Any] 						= torch.tensor([[-1.0915, -1.4876, -1.1809]]   )
							elif "b16" in checkpoint_url:
														lowerCamelCase__      :			Optional[Any] 						= torch.tensor([[14.2889, -18.9045, 11.7281]]   )
							elif "l16" in checkpoint_url:
														lowerCamelCase__      :			Dict 						= torch.tensor([[41.5028, -22.8681, 45.6475]]   )
							elif "b4" in checkpoint_url:
														lowerCamelCase__      :			int 						= torch.tensor([[-4.3868, 5.2932, -0.4137]]   )
							else:
														lowerCamelCase__      :			Dict 						= torch.tensor([[-0.1792, -0.6465, 2.4263]]   )

							# verify logits
							assert torch.allclose(last_hidden_state[:, 0, :3]       ,    UpperCamelCase       ,    atol=1E-4   )

							print(f'''Saving model 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__":
			_A			:    List[Any]					  =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--checkpoint_url''',
			    default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
			    type=str,
			    help='''URL of the checkpoint you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_A			:    Any					  =parser.parse_args()
			convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







'''simple docstring'''







from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

