

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__	   = logging.get_logger(__name__)



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]=False      )			->      Optional[Any]:




				'''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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]=False      )			->      Any:




				'''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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Optional[int]:




				'''simple docstring'''





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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      Tuple:




				'''simple docstring'''





				A__        =			dct.pop(SCREAMING_SNAKE_CASE_      )
				A__        =			val



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





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



@torch.no_grad()
def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Optional[int]=True      )			->      List[Any]:




				'''simple docstring'''





				A__        =			ViTConfig()
				# patch_size
				if model_name[-1] == "8":
								A__        =			8
				# set labels if required
				if not base_model:
								A__        =			1_0_0_0
								A__        =			"huggingface/label-files"
								A__        =			"imagenet-1k-id2label.json"
								A__        =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       repo_type="dataset"      )	,       "r"      )      )
								A__        =			{int(SCREAMING_SNAKE_CASE_      ): 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__        =			3_8_4
								A__        =			1_5_3_6
								A__        =			1_2
								A__        =			6

				# load original model from torch hub
				A__        =			torch.hub.load("facebookresearch/dino:main"	,       SCREAMING_SNAKE_CASE_      )
				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_(SCREAMING_SNAKE_CASE_      )
				A__        =			create_rename_keys(SCREAMING_SNAKE_CASE_	,       base_model=SCREAMING_SNAKE_CASE_      )
				for src, dest in rename_keys:
								rename_key(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				read_in_q_k_v(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

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

				# 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(SCREAMING_SNAKE_CASE_      )

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

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


if __name__ == "__main__":
	lowerCAmelCase__	   = 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__	   = parser.parse_args()
	convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)




import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							str=1_0_2_4      )			->      Any:




				'''simple docstring'''





				A__    ,				A__        =			[], []

				A__        =			list(zip(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )      )
				A__    ,				A__        =			sorted_examples[0]

				def is_too_big(SCREAMING_SNAKE_CASE_:							List[str]      ):
								return tok(SCREAMING_SNAKE_CASE_	,       return_tensors="pt"      ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]      ):
								A__        =			new_src + " " + src
								A__        =			new_tgt + " " + tgt
								if is_too_big(SCREAMING_SNAKE_CASE_      ) or is_too_big(SCREAMING_SNAKE_CASE_      ):  # cant fit, finalize example
												finished_src.append(SCREAMING_SNAKE_CASE_      )
												finished_tgt.append(SCREAMING_SNAKE_CASE_      )
												A__    ,				A__        =			src, tgt
								else:  # can fit, keep adding
												A__    ,				A__        =			cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(SCREAMING_SNAKE_CASE_      )
								finished_tgt.append(SCREAMING_SNAKE_CASE_      )
				return finished_src, finished_tgt



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Tuple      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			Path(SCREAMING_SNAKE_CASE_      )
				save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE_      )
				for split in ["train"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__        =			[x.rstrip() for x in Path(SCREAMING_SNAKE_CASE_      ).open().readlines()]
								A__    ,				A__        =			pack_examples(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
								print(F'packed {split} split from {len(SCREAMING_SNAKE_CASE_      )} examples -> {len(SCREAMING_SNAKE_CASE_      )}.'      )
								Path(save_path / F'{split}.source'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
								Path(save_path / F'{split}.target'      ).open("w"      ).write("\n".join(SCREAMING_SNAKE_CASE_      )      )
				for split in ["val", "test"]:
								A__    ,				A__        =			data_dir / F'{split}.source', data_dir / F'{split}.target'
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.source'      )
								shutil.copyfile(SCREAMING_SNAKE_CASE_	,       save_path / F'{split}.target'      )



def 					lowerCAmelCase__							(	)			->      Optional[int]:




				'''simple docstring'''





				A__        =			argparse.ArgumentParser()
				parser.add_argument("--tok_name"	,       type=SCREAMING_SNAKE_CASE_	,       help="like facebook/bart-large-cnn,t5-base, etc."      )
				parser.add_argument("--max_seq_len"	,       type=SCREAMING_SNAKE_CASE_	,       default=1_2_8      )
				parser.add_argument("--data_dir"	,       type=SCREAMING_SNAKE_CASE_      )
				parser.add_argument("--save_path"	,       type=SCREAMING_SNAKE_CASE_      )
				A__        =			parser.parse_args()
				A__        =			AutoTokenizer.from_pretrained(args.tok_name      )
				return pack_data_dir(SCREAMING_SNAKE_CASE_	,       Path(args.data_dir      )	,       args.max_seq_len	,       args.save_path      )


if __name__ == "__main__":
	packer_cli()

