
"""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 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_=False							):
		__SCREAMING_SNAKE_CASE				=					[]
		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"
				__SCREAMING_SNAKE_CASE				=					[(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_    ,							UpperCamelCase_    ,							UpperCamelCase_=False							):







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



def 		_lowerCAmelCase							(							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					["""head.weight""", """head.bias"""]
		for k in ignore_keys:
				state_dict.pop(UpperCamelCase_    ,							UpperCamelCase_							)



def 		_lowerCAmelCase							(							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					[
		    """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 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					dct.pop(UpperCamelCase_							)
		__SCREAMING_SNAKE_CASE				=					val



def 		_lowerCAmelCase							(							UpperCamelCase_    ,							UpperCamelCase_							):
		__SCREAMING_SNAKE_CASE				=					ViTMSNConfig()
		__SCREAMING_SNAKE_CASE				=					1000

		__SCREAMING_SNAKE_CASE				=					"""datasets/huggingface/label-files"""
		__SCREAMING_SNAKE_CASE				=					"""imagenet-1k-id2label.json"""
		__SCREAMING_SNAKE_CASE				=					json.load(open(hf_hub_download(UpperCamelCase_    ,							UpperCamelCase_							)    ,							"""r"""							)							)
		__SCREAMING_SNAKE_CASE				=					{int(UpperCamelCase_							): v for k, v in idalabel.items()}
		__SCREAMING_SNAKE_CASE				=					idalabel
		__SCREAMING_SNAKE_CASE				=					{v: k for k, v in idalabel.items()}

		if "s16" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					384
				__SCREAMING_SNAKE_CASE				=					1536
				__SCREAMING_SNAKE_CASE				=					6
		elif "l16" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					1024
				__SCREAMING_SNAKE_CASE				=					4096
				__SCREAMING_SNAKE_CASE				=					24
				__SCREAMING_SNAKE_CASE				=					16
				__SCREAMING_SNAKE_CASE				=					0.1
		elif "b4" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					4
		elif "l7" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					7
				__SCREAMING_SNAKE_CASE				=					1024
				__SCREAMING_SNAKE_CASE				=					4096
				__SCREAMING_SNAKE_CASE				=					24
				__SCREAMING_SNAKE_CASE				=					16
				__SCREAMING_SNAKE_CASE				=					0.1

		__SCREAMING_SNAKE_CASE				=					ViTMSNModel(UpperCamelCase_							)

		__SCREAMING_SNAKE_CASE				=					torch.hub.load_state_dict_from_url(UpperCamelCase_    ,							map_location="""cpu"""							)["""target_encoder"""]

		__SCREAMING_SNAKE_CASE				=					ViTImageProcessor(size=config.image_size							)

		remove_projection_head(UpperCamelCase_							)
		__SCREAMING_SNAKE_CASE				=					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()

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

		__SCREAMING_SNAKE_CASE				=					Image.open(requests.get(UpperCamelCase_    ,							stream=UpperCamelCase_							).raw							)
		__SCREAMING_SNAKE_CASE				=					ViTImageProcessor(
		    size=config.image_size    ,							image_mean=UpperCamelCase_    ,							image_std=UpperCamelCase_							)
		__SCREAMING_SNAKE_CASE				=					image_processor(images=UpperCamelCase_    ,							return_tensors="""pt"""							)

		# forward pass
		torch.manual_seed(2							)
		__SCREAMING_SNAKE_CASE				=					model(**UpperCamelCase_							)
		__SCREAMING_SNAKE_CASE				=					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:
				__SCREAMING_SNAKE_CASE				=					torch.tensor([[-1.0_915, -1.4_876, -1.1_809]]							)
		elif "b16" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					torch.tensor([[14.2_889, -18.9_045, 11.7_281]]							)
		elif "l16" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					torch.tensor([[41.5_028, -22.8_681, 45.6_475]]							)
		elif "b4" in checkpoint_url:
				__SCREAMING_SNAKE_CASE				=					torch.tensor([[-4.3_868, 5.2_932, -0.4_137]]							)
		else:
				__SCREAMING_SNAKE_CASE				=					torch.tensor([[-0.1_792, -0.6_465, 2.4_263]]							)

		# 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__":
							__magic_name__			=     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."
							)

							__magic_name__			=     parser.parse_args()
							convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE__     :     List[Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     *SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				warnings.warn(
				    'The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
				    ' Please use PerceiverImageProcessor instead.'      ,     SCREAMING_SNAKE_CASE      ,     )
				super().__init__(*SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )
