






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


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


def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[Any]=False   ) ->   Optional[int]:
						"""simple docstring"""







						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"),
												        ("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"
												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        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				Dict=False   ) ->   Tuple:
						"""simple docstring"""







						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        _a  (     UpperCamelCase_					:				Optional[int]   ) ->   List[Any]:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				int    ,   UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				List[Any]   ) ->   Any:
						"""simple docstring"""







						lowerCAmelCase__       =						dct.pop(UpperCamelCase_   )
						lowerCAmelCase__       =						val


def        _a  (     ) ->   Optional[int]:
						"""simple docstring"""







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


@torch.no_grad()
def        _a  (     UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				List[Any]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						ViTConfig()
						lowerCAmelCase__       =						False
						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						if vit_name[-5:] == "in21k":
												lowerCAmelCase__       =						True
												lowerCAmelCase__       =						int(vit_name[-12:-10]   )
												lowerCAmelCase__       =						int(vit_name[-9:-6]   )
						else:
												lowerCAmelCase__       =						1_000
												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()}
												lowerCAmelCase__       =						int(vit_name[-6:-4]   )
												lowerCAmelCase__       =						int(vit_name[-3:]   )
						# size of the architecture
						if "deit" in vit_name:
												if vit_name[9:].startswith("tiny"   ):
																		lowerCAmelCase__       =						192
																		lowerCAmelCase__       =						768
																		lowerCAmelCase__       =						12
																		lowerCAmelCase__       =						3
												elif vit_name[9:].startswith("small"   ):
																		lowerCAmelCase__       =						384
																		lowerCAmelCase__       =						1_536
																		lowerCAmelCase__       =						12
																		lowerCAmelCase__       =						6
												else:
																		pass
						else:
												if vit_name[4:].startswith("small"   ):
																		lowerCAmelCase__       =						768
																		lowerCAmelCase__       =						2_304
																		lowerCAmelCase__       =						8
																		lowerCAmelCase__       =						8
												elif vit_name[4:].startswith("base"   ):
																		pass
												elif vit_name[4:].startswith("large"   ):
																		lowerCAmelCase__       =						1_024
																		lowerCAmelCase__       =						4_096
																		lowerCAmelCase__       =						24
																		lowerCAmelCase__       =						16
												elif vit_name[4:].startswith("huge"   ):
																		lowerCAmelCase__       =						1_280
																		lowerCAmelCase__       =						5_120
																		lowerCAmelCase__       =						32
																		lowerCAmelCase__       =						16

    # load original model from timm
						lowerCAmelCase__       =						timm.create_model(UpperCamelCase_    ,   pretrained=UpperCamelCase_   )
						timm_model.eval()

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

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												lowerCAmelCase__       =						ViTModel(UpperCamelCase_   ).eval()
						else:
												lowerCAmelCase__       =						ViTForImageClassification(UpperCamelCase_   ).eval()
						model.load_state_dict(UpperCamelCase_   )

						# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
						if "deit" in vit_name:
												lowerCAmelCase__       =						DeiTImageProcessor(size=config.image_size   )
						else:
												lowerCAmelCase__       =						ViTImageProcessor(size=config.image_size   )
						lowerCAmelCase__       =						image_processor(images=prepare_img()    ,   return_tensors="pt"   )
						lowerCAmelCase__       =						encoding["pixel_values"]
						lowerCAmelCase__       =						model(UpperCamelCase_   )

						if base_model:
												lowerCAmelCase__       =						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:
												lowerCAmelCase__       =						timm_model(UpperCamelCase_   )
												assert timm_logits.shape == outputs.logits.shape
												assert torch.allclose(UpperCamelCase_    ,   outputs.logits    ,   atol=1e-3   )

						Path(UpperCamelCase_   ).mkdir(exist_ok=UpperCamelCase_   )
						print(F"Saving model {vit_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__":
	a_							=    argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--vit_name''',
	    default='''vit_base_patch16_224''',
	    type=str,
	    help='''Name of the 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.'''
	)

	a_							=    parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)














def        _a  (     UpperCamelCase_					:				Any    ,   UpperCamelCase_					:				List[str]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						0
						lowerCAmelCase__       =						len(UpperCamelCase_   ) - 1

						while left <= right:
												# avoid divided by 0 during interpolation
												if sorted_collection[left] == sorted_collection[right]:
																		if sorted_collection[left] == item:
																								return left
																		else:
																								return None

												lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
												    sorted_collection[right] - sorted_collection[left]
												)

												# out of range check
												if point < 0 or point >= len(UpperCamelCase_   ):
																		return None

												lowerCAmelCase__       =						sorted_collection[point]
												if current_item == item:
																		return point
												else:
																		if point < left:
																								lowerCAmelCase__       =						left
																								lowerCAmelCase__       =						point
																		elif point > right:
																								lowerCAmelCase__       =						right
																								lowerCAmelCase__       =						point
																		else:
																								if item < current_item:
																														lowerCAmelCase__       =						point - 1
																								else:
																														lowerCAmelCase__       =						point + 1
						return None


def        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[Any]    ,   UpperCamelCase_					:				int    ,   UpperCamelCase_					:				int   ) ->   List[Any]:
						"""simple docstring"""







						if sorted_collection[left] == sorted_collection[right]:
												if sorted_collection[left] == item:
																		return left
												else:
																		return None

						lowerCAmelCase__       =						left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(UpperCamelCase_   ):
												return None

						if sorted_collection[point] == item:
												return point
						elif point < left:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						elif point > right:
												return interpolation_search_by_recursion(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_   )
						else:
												if sorted_collection[point] > item:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   point - 1   )
												else:
																		return interpolation_search_by_recursion(
																		    UpperCamelCase_    ,   UpperCamelCase_    ,   point + 1    ,   UpperCamelCase_   )


def        _a  (     UpperCamelCase_					:				Optional[Any]   ) ->   Tuple:
						"""simple docstring"""







						if collection != sorted(UpperCamelCase_   ):
												raise ValueError("Collection must be ascending sorted"   )
						return True


if __name__ == "__main__":
	import sys
	a_							=    0
	if debug == 1:
		a_							=    [10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('''Sequence must be ascending sorted to apply interpolation search''')
		a_							=    67

	a_							=    interpolation_search(collection, target)
	if result is not None:
		print(F"{target} found at positions: {result}")
	else:
		print('''Not found''')






