"""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()
__lowerCamelCase											=  logging.get_logger(__name__)






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__=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   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





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






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							dct.pop(UpperCamelCase__				)
							A__	     =							val






def   UpperCAmelCase					(	):






							"""simple docstring"""





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






@torch.no_grad()
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__=True				):






							"""simple docstring"""





							A__	     =							ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														A__	     =							8
							# set labels if required
							if not base_model:
														A__	     =							1_000
														A__	     =							'huggingface/label-files'
														A__	     =							'imagenet-1k-id2label.json'
														A__	     =							json.load(open(hf_hub_download(UpperCamelCase__       ,    UpperCamelCase__       ,    repo_type='dataset'				)       ,    'r'				)				)
														A__	     =							{int(UpperCamelCase__				): 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__	     =							1_536
														A__	     =							12
														A__	     =							6

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

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

							# 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(UpperCamelCase__				)

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

							Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
							print(F'''Saving model {model_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__":
				__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)






"""simple docstring"""




from typing import Dict, Optional

import numpy as np

import datasets


__lowerCamelCase											=  "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n"

__lowerCamelCase											=  "\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric(\"mean_iou\")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n"

__lowerCamelCase											=  "\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}"






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							if label_map is not None:
														for old_id, new_id in label_map.items():
																					A__	     =							new_id

    # turn into Numpy arrays
							A__	     =							np.array(UpperCamelCase__				)
							A__	     =							np.array(UpperCamelCase__				)

							if reduce_labels:
														A__	     =							255
														A__	     =							label - 1
														A__	     =							255

							A__	     =							label != ignore_index
							A__	     =							np.not_equal(UpperCamelCase__       ,    UpperCamelCase__				)
							A__	     =							pred_label[mask]
							A__	     =							np.array(UpperCamelCase__				)[mask]

							A__	     =							pred_label[pred_label == label]

							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]
							A__	     =							np.histogram(UpperCamelCase__       ,    bins=UpperCamelCase__       ,    range=(0, num_labels - 1)				)[0]

							A__	     =							area_pred_label + area_label - area_intersect

							return area_intersect, area_union, area_pred_label, area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							A__	     =							np.zeros((num_labels,)       ,    dtype=np.floataa				)
							for result, gt_seg_map in zip(UpperCamelCase__       ,    UpperCamelCase__				):
														A__     ,  A__     ,  A__     ,  A__	     =							intersect_and_union(
														    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)
														total_area_intersect += area_intersect
														total_area_union += area_union
														total_area_pred_label += area_pred_label
														total_area_label += area_label
							return total_area_intersect, total_area_union, total_area_pred_label, total_area_label






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = None       ,    UpperCamelCase__ = False       ,    ):






							"""simple docstring"""





							A__     ,  A__     ,  A__     ,  A__	     =							total_intersect_and_union(
							    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				)

							# compute metrics
							A__	     =							{}

							A__	     =							total_area_intersect.sum() / total_area_label.sum()
							A__	     =							total_area_intersect / total_area_union
							A__	     =							total_area_intersect / total_area_label

							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							np.nanmean(UpperCamelCase__				)
							A__	     =							all_acc
							A__	     =							iou
							A__	     =							acc

							if nan_to_num is not None:
														A__	     =							{metric: np.nan_to_num(UpperCamelCase__       ,    nan=UpperCamelCase__				) for metric, metric_value in metrics.items()}

							return metrics


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,						_KWARGS_DESCRIPTION )
class     UpperCamelCase__(			datasets.Metric ):





						def 				snake_case__	(       self     )					->				str:
													return datasets.MetricInfo(
													    description=_DESCRIPTION		,citation=_CITATION		,inputs_description=_KWARGS_DESCRIPTION		,features=datasets.Features(
													        # 1st Seq - height dim, 2nd - width dim
													        {
													            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'     )     )     ),
													            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'     )     )     ),
													        }     )		,reference_urls=[
													        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
													    ]		,)





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,)					->				Tuple:
													A__	     =							mean_iou(
													    results=__UpperCAmelCase		,gt_seg_maps=__UpperCAmelCase		,num_labels=__UpperCAmelCase		,ignore_index=__UpperCAmelCase		,nan_to_num=__UpperCAmelCase		,label_map=__UpperCAmelCase		,reduce_labels=__UpperCAmelCase		,)
													return iou_result





