




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()
__lowerCAmelCase		:				Union[str, Any]       			=		logging.get_logger(__name__)




def 			__magic_name__				( A		:					Any,  A		:					Optional[Any]=False   ):






			'''simple docstring'''







			a    =			[]
			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"
						a    =			[(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 			__magic_name__				( A		:					str,  A		:					Optional[Any],  A		:					int=False   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







			a    =			["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(A,  A   )




def 			__magic_name__				( A		:					Tuple,  A		:					int,  A		:					Optional[Any]   ):






			'''simple docstring'''







			a    =			dct.pop(A   )
			a    =			val




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			"http://images.cocodataset.org/val2017/000000039769.jpg"
			a    =			Image.open(requests.get(A,  stream=A   ).raw   )
			return im




@torch.no_grad()
def 			__magic_name__				( A		:					List[Any],  A		:					str,  A		:					Optional[int]=True   ):






			'''simple docstring'''







			a    =			ViTConfig()
			# patch_size
			if model_name[-1] == "8":
						a    =			8
			# set labels if required
			if not base_model:
						a    =			1000
						a    =			"huggingface/label-files"
						a    =			"imagenet-1k-id2label.json"
						a    =			json.load(open(hf_hub_download(A,  A,  repo_type="dataset"   ),  "r"   )   )
						a    =			{int(A   ): v for k, v in idalabel.items()}
						a    =			idalabel
						a    =			{v: k for k, v in idalabel.items()}
			# size of the architecture
			if model_name in ["dino_vits8", "dino_vits16"]:
						a    =			384
						a    =			1536
						a    =			12
						a    =			6

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

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

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

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

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

			Path(A   ).mkdir(exist_ok=A   )
			print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
			model.save_pretrained(A   )
			print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
			image_processor.save_pretrained(A   )


if __name__ == "__main__":
		__lowerCAmelCase		:				Union[str, Any]       			=		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)
		__lowerCAmelCase		:				List[str]       			=		parser.parse_args()
		convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase		:				List[str]       			=		{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Dict       			=		[
		    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ResNetForImageClassification',
		    'ResNetModel',
		    'ResNetPreTrainedModel',
		    'ResNetBackbone',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				int       			=		[
		    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFResNetForImageClassification',
		    'TFResNetModel',
		    'TFResNetPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase		:				Optional[Any]       			=		[
		    'FlaxResNetForImageClassification',
		    'FlaxResNetModel',
		    'FlaxResNetPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_resnet import (
				    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ResNetBackbone,
				    ResNetForImageClassification,
				    ResNetModel,
				    ResNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_resnet import (
				    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFResNetForImageClassification,
				    TFResNetModel,
				    TFResNetPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
		import sys

		__lowerCAmelCase		:				int       			=		_LazyModule(__name__, globals()['__file__'], _import_structure)






