




'''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()
_SCREAMING_SNAKE_CASE							:       Dict						=			logging.get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	List[str]=False					):

							'''simple docstring'''






							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'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"
														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 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	str							,    snake_case							:	Any=False					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]					):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	Union[str, Any]							,    snake_case							:	Dict							,    snake_case							:	Optional[Any]					):

							'''simple docstring'''






							snake_case_      =				dct.pop(snake_case					)
							snake_case_      =				val


def 					UpperCamelCase_(     ):

							'''simple docstring'''






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


@torch.no_grad()
def 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	Optional[int]							,    snake_case							:	Union[str, Any]=True					):

							'''simple docstring'''






							snake_case_      =				ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														snake_case_      =				8
							# set labels if required
							if not base_model:
														snake_case_      =				1_0_0_0
														snake_case_      =				"huggingface/label-files"
														snake_case_      =				"imagenet-1k-id2label.json"
														snake_case_      =				json.load(open(hf_hub_download(snake_case							,    snake_case							,    repo_type="dataset"					)							,    "r"					)					)
														snake_case_      =				{int(snake_case					): v for k, v in idalabel.items()}
														snake_case_      =				idalabel
														snake_case_      =				{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														snake_case_      =				3_8_4
														snake_case_      =				1_5_3_6
														snake_case_      =				1_2
														snake_case_      =				6

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

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

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

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

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

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


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










'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


_SCREAMING_SNAKE_CASE							:       Optional[Any]						=			"docs/source/en/_toctree.yml"


def 					UpperCamelCase_(     snake_case							:	Optional[Any]					):

							'''simple docstring'''






							snake_case_      =				defaultdict(snake_case					)
							for doc in model_doc:
														counts[doc["local"]] += 1
							snake_case_      =				[key for key, value in counts.items() if value > 1]

							snake_case_      =				[]
							for duplicate_key in duplicates:
														snake_case_      =				list({doc["title"] for doc in model_doc if doc["local"] == duplicate_key}					)
														if len(snake_case					) > 1:
																					raise ValueError(
																					    f'{duplicate_key} is present several times in the documentation table of content at '
																					    "`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
																					    "others."					)
														# Only add this once
														new_doc.append({"local": duplicate_key, "title": titles[0]}					)

							# Add none duplicate-keys
							new_doc.extend([doc for doc in model_doc if counts[doc["local"]] == 1]					)

							# Sort
							return sorted(snake_case							,    key=lambda snake_case					: s["title"].lower()					)


def 					UpperCamelCase_(     snake_case							:	Optional[int]=False					):

							'''simple docstring'''






							with open(snake_case							,    encoding="utf-8"					) as f:
														snake_case_      =				yaml.safe_load(f.read()					)

							# Get to the API doc
							snake_case_      =				0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							snake_case_      =				content[api_idx]["sections"]

							# Then to the model doc
							snake_case_      =				0
							while api_doc[model_idx]["title"] != "Models":
														model_idx += 1

							snake_case_      =				api_doc[model_idx]["sections"]

							snake_case_      =				[(idx, section) for idx, section in enumerate(snake_case					) if "sections" in section]
							snake_case_      =				False
							for idx, modality_doc in modalities_docs:
														snake_case_      =				modality_doc["sections"]
														snake_case_      =				clean_model_doc_toc(snake_case					)

														if old_modality_doc != new_modality_doc:
																					snake_case_      =				True
																					if overwrite:
																												snake_case_      =				new_modality_doc

							if diff:
														if overwrite:
																					snake_case_      =				model_doc
																					snake_case_      =				api_doc
																					with open(snake_case							,    "w"							,    encoding="utf-8"					) as f:
																												f.write(yaml.dump(snake_case							,    allow_unicode=snake_case					)					)
														else:
																					raise ValueError(
																					    "The model doc part of the table of content is not properly sorted, run `make style` to fix this."					)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       List[str]						=			argparse.ArgumentParser()
		parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
		_SCREAMING_SNAKE_CASE							:       Any						=			parser.parse_args()

		check_model_doc(args.fix_and_overwrite)




