



'''simple docstring'''






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


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


def  snake_case_     (					_lowerCAmelCase  :						Optional[Any]  )      ->   str:
							UpperCAmelCase :       Tuple 							=		['''head.weight''', '''head.bias''']
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase   ,      _lowerCAmelCase  )


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Tuple   ,      _lowerCAmelCase  :						Dict  )      ->   Dict:
							UpperCAmelCase :       Union[str, Any] 							=		dct.pop(_lowerCAmelCase  )
							UpperCAmelCase :       Any 							=		val


def  snake_case_     (					)      ->   List[str]:
							UpperCAmelCase :       int 							=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase :       Dict 							=		Image.open(requests.get(_lowerCAmelCase   ,      stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Union[str, Any]=True  )      ->   Union[str, Any]:
							UpperCAmelCase :       str 							=		ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														UpperCAmelCase :       Optional[Any] 							=		8
							# set labels if required
							if not base_model:
														UpperCAmelCase :       Optional[Any] 							=		1000
														UpperCAmelCase :       str 							=		'''huggingface/label-files'''
														UpperCAmelCase :       Any 							=		'''imagenet-1k-id2label.json'''
														UpperCAmelCase :       Optional[Any] 							=		json.load(open(hf_hub_download(_lowerCAmelCase   ,      _lowerCAmelCase   ,      repo_type='''dataset'''  )   ,      '''r'''  )  )
														UpperCAmelCase :       Union[str, Any] 							=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase :       Any 							=		idalabel
														UpperCAmelCase :       Dict 							=		{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														UpperCAmelCase :       List[Any] 							=		384
														UpperCAmelCase :       Tuple 							=		1536
														UpperCAmelCase :       int 							=		12
														UpperCAmelCase :       Optional[Any] 							=		6

							# load original model from torch hub
							UpperCAmelCase :       List[Any] 							=		torch.hub.load('''facebookresearch/dino:main'''   ,      _lowerCAmelCase  )
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							UpperCAmelCase :       List[Any] 							=		original_model.state_dict()
							if base_model:
														remove_classification_head_(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		create_rename_keys(_lowerCAmelCase   ,      base_model=_lowerCAmelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
							read_in_q_k_v(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							# load HuggingFace model
							if base_model:
														UpperCAmelCase :       Union[str, Any] 							=		ViTModel(_lowerCAmelCase   ,      add_pooling_layer=_lowerCAmelCase  ).eval()
							else:
														UpperCAmelCase :       List[str] 							=		ViTForImageClassification(_lowerCAmelCase  ).eval()
							model.load_state_dict(_lowerCAmelCase  )

							# Check outputs on an image, prepared by ViTImageProcessor
							UpperCAmelCase :       str 							=		ViTImageProcessor()
							UpperCAmelCase :       int 							=		image_processor(images=prepare_img()   ,      return_tensors='''pt'''  )
							UpperCAmelCase :       Dict 							=		encoding['''pixel_values''']
							UpperCAmelCase :       List[Any] 							=		model(_lowerCAmelCase  )

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

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


if __name__ == "__main__":
						UpperCamelCase__:   Tuple								=			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)
						UpperCamelCase__:   Optional[int]								=			parser.parse_args()
						convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)










'''simple docstring'''






from __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str  )      ->   bool:
							UpperCAmelCase :       str 							=		get_failure_array(_lowerCAmelCase  )

							# 2) Step through text searching for pattern
							UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		0, 0  # index into text, pattern
							while i < len(_lowerCAmelCase  ):
														if pattern[j] == text[i]:
																					if j == (len(_lowerCAmelCase  ) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					UpperCAmelCase :       Optional[Any] 							=		failure[j - 1]
																					continue
														i += 1
							return False


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		[0]
							UpperCAmelCase :       str 							=		0
							UpperCAmelCase :       List[str] 							=		1
							while j < len(_lowerCAmelCase  ):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					UpperCAmelCase :       Union[str, Any] 							=		failure[i - 1]
																					continue
														j += 1
														failure.append(_lowerCAmelCase  )
							return failure


if __name__ == "__main__":
						# Test 1)
						UpperCamelCase__:   str								=			"abc1abc12"
						UpperCamelCase__:   str								=			"alskfjaldsabc1abc1abc12k23adsfabcabc"
						UpperCamelCase__:   Any								=			"alskfjaldsk23adsfabcabc"
						assert kmp(pattern, texta) and not kmp(pattern, texta)

						# Test 2)
						UpperCamelCase__:   Tuple								=			"ABABX"
						UpperCamelCase__:   Union[str, Any]								=			"ABABZABABYABABX"
						assert kmp(pattern, text)

						# Test 3)
						UpperCamelCase__:   Any								=			"AAAB"
						UpperCamelCase__:   str								=			"ABAAAAAB"
						assert kmp(pattern, text)

						# Test 4)
						UpperCamelCase__:   int								=			"abcdabcy"
						UpperCamelCase__:   Any								=			"abcxabcdabxabcdabcdabcy"
						assert kmp(pattern, text)

						# Test 5)
						UpperCamelCase__:   List[str]								=			"aabaabaaa"
						assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]





