




'''simple docstring'''
import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_=False						,						UpperCAmelCase_=False						,						UpperCAmelCase_="data2vec."							):
						_UpperCamelCase      :	Union[str, Any]				       =     'backbone.' if is_semantic else ''

						_UpperCamelCase      :	Optional[int]				       =     []
						for i in range(config.num_hidden_layers							):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append(
												    (f'{prefix}blocks.{i}.norm1.weight', f'{hf_prefix}encoder.layer.{i}.layernorm_before.weight')							)
												rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'{hf_prefix}encoder.layer.{i}.layernorm_before.bias')							)
												rename_keys.append(
												    (f'{prefix}blocks.{i}.attn.proj.weight', f'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight')							)
												rename_keys.append(
												    (f'{prefix}blocks.{i}.attn.proj.bias', f'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias')							)
												rename_keys.append(
												    (f'{prefix}blocks.{i}.norm2.weight', f'{hf_prefix}encoder.layer.{i}.layernorm_after.weight')							)
												rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'{hf_prefix}encoder.layer.{i}.layernorm_after.bias')							)
												rename_keys.append(
												    (f'{prefix}blocks.{i}.mlp.fc1.weight', f'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight')							)
												rename_keys.append(
												    (f'{prefix}blocks.{i}.mlp.fc1.bias', f'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias')							)
												rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'{hf_prefix}encoder.layer.{i}.output.dense.weight')							)
												rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'{hf_prefix}encoder.layer.{i}.output.dense.bias')							)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (f'{prefix}cls_token', f'{hf_prefix}embeddings.cls_token'),
						        (f'{prefix}patch_embed.proj.weight', f'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
						        (f'{prefix}patch_embed.proj.bias', f'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
						    ]							)

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('mask_token', f'{hf_prefix}embeddings.mask_token'),
												        (
												            'rel_pos_bias.relative_position_bias_table',
												            f'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
												        ),
												        (
												            'rel_pos_bias.relative_position_index',
												            f'{hf_prefix}encoder.relative_position_bias.relative_position_index',
												        ),
												        ('norm.weight', 'layernorm.weight'),
												        ('norm.bias', 'layernorm.bias'),
												    ]							)
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
												        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
												        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
												        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
												    ]							)
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('fc_norm.weight', f'{hf_prefix}pooler.layernorm.weight'),
												        ('fc_norm.bias', f'{hf_prefix}pooler.layernorm.bias'),
												        ('head.weight', 'classifier.weight'),
												        ('head.bias', 'classifier.bias'),
												    ]							)

						return rename_keys






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=False						,						UpperCAmelCase_=False						,						UpperCAmelCase_="data2vec_vision."							):
						for i in range(config.num_hidden_layers							):
												_UpperCamelCase      :	Optional[Any]				       =     'backbone.' if is_semantic else ''
												# queries, keys and values
												_UpperCamelCase      :	str				       =     state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'							)
												_UpperCamelCase      :	List[str]				       =     state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'							)
												_UpperCamelCase      :	List[str]				       =     state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'							)

												_UpperCamelCase      :	str				       =     in_proj_weight[
												    : config.hidden_size, :
												]
												_UpperCamelCase      :	List[Any]				       =     q_bias
												_UpperCamelCase      :	Optional[Any]				       =     in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCamelCase      :	Tuple				       =     in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCamelCase      :	List[str]				       =     v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												_UpperCamelCase      :	Union[str, Any]				       =     state_dict.pop(f'{prefix}blocks.{i}.gamma_1'							)
												_UpperCamelCase      :	Any				       =     state_dict.pop(f'{prefix}blocks.{i}.gamma_2'							)

												_UpperCamelCase      :	Any				       =     gamma_a
												_UpperCamelCase      :	List[Any]				       =     gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		_UpperCamelCase      :	Tuple				       =     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'							)
																		_UpperCamelCase      :	Any				       =     state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'							)

																		_UpperCamelCase      :	Optional[Any]				       =     table
																		_UpperCamelCase      :	Dict				       =     index






def A__					(  ):
						_UpperCamelCase      :	str				       =     argparse.ArgumentParser(
						    'Convert Data2VecVision to HF for image classification and pretraining'						,						add_help=lowercase__							)
						parser.add_argument('--hf_checkpoint_name'						,						type=lowercase__							)
						parser.add_argument('--input_size'						,						default=2_2_4						,						type=lowercase__						,						help='images input size'							)
						parser.add_argument('--beit_checkpoint'						,						default=''						,						help='beit checkpoint'							)

						return parser.parse_args()






def A__					(  UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_							):
						def load_state_dict(UpperCAmelCase_						,						UpperCAmelCase_						,						UpperCAmelCase_=""						,						UpperCAmelCase_="relative_position_index"							):
												_UpperCamelCase      :	Dict				       =     []
												_UpperCamelCase      :	str				       =     []
												_UpperCamelCase      :	int				       =     []
												# copy state_dict so _load_from_state_dict can modify it
												_UpperCamelCase      :	Optional[int]				       =     getattr(lowercase__						,						'_metadata'						,						lowercase__							)
												_UpperCamelCase      :	List[Any]				       =     state_dict.copy()
												if metadata is not None:
																		_UpperCamelCase      :	str				       =     metadata

												def load(UpperCAmelCase_						,						UpperCAmelCase_=""							):
																		_UpperCamelCase      :	Dict				       =     {} if metadata is None else metadata.get(prefix[:-1]						,						{}							)
																		module._load_from_state_dict(
																		    lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						lowercase__							)
																		for name, child in module._modules.items():
																								if child is not None:
																														load(lowercase__						,						prefix + name + '.'							)

												load(lowercase__						,						prefix=lowercase__							)

												_UpperCamelCase      :	Dict				       =     []
												_UpperCamelCase      :	int				       =     []
												for key in missing_keys:
																		_UpperCamelCase      :	List[Any]				       =     True
																		for ignore_key in ignore_missing.split('|'							):
																								if ignore_key in key:
																														_UpperCamelCase      :	Union[str, Any]				       =     False
																														break
																		if keep_flag:
																								warn_missing_keys.append(lowercase__							)
																		else:
																								ignore_missing_keys.append(lowercase__							)

												_UpperCamelCase      :	Dict				       =     warn_missing_keys

												if len(lowercase__							) > 0:
																		print(
																		    'Weights of {} not initialized from pretrained model: {}'.format(
																		        model.__class__.__name__						,						lowercase__							)							)
												if len(lowercase__							) > 0:
																		print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__						,						lowercase__							)							)
												if len(lowercase__							) > 0:
																		print(
																		    'Ignored weights of {} not initialized from pretrained model: {}'.format(
																		        model.__class__.__name__						,						lowercase__							)							)
												if len(lowercase__							) > 0:
																		print('\n'.join(lowercase__							)							)

						_UpperCamelCase      :	Union[str, Any]				       =     {
						    'pretrained': False,
						    'use_shared_rel_pos_bias': True,
						    'use_abs_pos_emb': False,
						    'init_values': 0.1,
						}

						if is_finetuned:
												model_kwargs.update(
												    {
												        'num_classes': 1_0_0_0,
												        'use_mean_pooling': True,
												        'init_scale': 0.001,
												        'use_rel_pos_bias': True,
												    }							)

						_UpperCamelCase      :	List[Any]				       =     create_model(
						    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224'						,						**lowercase__						,						)
						_UpperCamelCase      :	Optional[int]				       =     model.patch_embed.patch_size
						_UpperCamelCase      :	str				       =     (args.input_size // patch_size[0], args.input_size // patch_size[1])
						_UpperCamelCase      :	Union[str, Any]				       =     torch.load(args.beit_checkpoint						,						map_location='cpu'							)

						print(f'Load ckpt from {args.beit_checkpoint}'							)
						_UpperCamelCase      :	int				       =     None
						for model_key in ("model", "module"):
												if model_key in checkpoint:
																		_UpperCamelCase      :	str				       =     checkpoint[model_key]
																		print(f'Load state_dict by model_key = {model_key}'							)
																		break

						_UpperCamelCase      :	int				       =     list(checkpoint_model.keys()							)
						for key in all_keys:
												if "relative_position_index" in key:
																		checkpoint_model.pop(lowercase__							)

												if "relative_position_bias_table" in key:
																		_UpperCamelCase      :	Dict				       =     checkpoint_model[key]
																		_UpperCamelCase       ,    _UpperCamelCase      :	List[Any]				       =     rel_pos_bias.size()
																		_UpperCamelCase       ,    _UpperCamelCase      :	Optional[int]				       =     model.state_dict()[key].size()
																		_UpperCamelCase      :	Optional[int]				       =     model.patch_embed.patch_shape
																		if dst_patch_shape[0] != dst_patch_shape[1]:
																								raise NotImplementedError()

						load_state_dict(lowercase__						,						lowercase__						,						prefix=''							)

						return model






def A__					(  ):
						_UpperCamelCase      :	Optional[Any]				       =     get_args()

						_UpperCamelCase      :	Tuple				       =     'ft1k' in args.hf_checkpoint_name
						_UpperCamelCase      :	Union[str, Any]				       =     'large' in args.hf_checkpoint_name

						if is_finetuned:
												# To convert Beit's data2vec_vision to HF you need to copy
												# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
												# into this folder.
												import modeling_finetune  # noqa: F401
						else:
												# To convert Beit's data2vec_vision to HF you need to copy
												# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
												# into this folder
												# IMPORTANT: Note that for now we've only converted the down-stream
												# model and not the full pretrained model. This means for the integration
												# test you need to add a `return x` after the following line:
												# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
												# to make the integration test pass.
												import modeling_cyclical  # noqa: F401

						# 1. Create model config
						_UpperCamelCase      :	Optional[int]				       =     DataaVecVisionConfig()
						if is_finetuned:
												_UpperCamelCase      :	Any				       =     True
												_UpperCamelCase      :	Dict				       =     False
												_UpperCamelCase      :	Tuple				       =     True
												_UpperCamelCase      :	Optional[Any]				       =     1_0_0_0

												_UpperCamelCase      :	int				       =     'huggingface/label-files'
												_UpperCamelCase      :	Tuple				       =     'imagenet-1k-id2label.json'
												_UpperCamelCase      :	Optional[Any]				       =     json.load(open(hf_hub_download(lowercase__						,						lowercase__						,						repo_type='dataset'							)						,						'r'							)							)
												_UpperCamelCase      :	str				       =     {int(lowercase__							): v for k, v in idalabel.items()}
												_UpperCamelCase      :	Optional[Any]				       =     idalabel
												_UpperCamelCase      :	str				       =     {v: k for k, v in idalabel.items()}
						else:
												_UpperCamelCase      :	Dict				       =     False
												_UpperCamelCase      :	List[Any]				       =     True
												_UpperCamelCase      :	int				       =     False

						if is_large:
												_UpperCamelCase      :	Any				       =     1_0_2_4
												_UpperCamelCase      :	str				       =     4_0_9_6
												_UpperCamelCase      :	int				       =     2_4
												_UpperCamelCase      :	Tuple				       =     1_6

						# 2. Load Beit model
						_UpperCamelCase      :	Tuple				       =     load_beit_model(lowercase__						,						lowercase__						,						lowercase__							)
						orig_model.eval()

						# 3. Forward Beit model
						_UpperCamelCase      :	List[str]				       =     BeitImageProcessor(size=config.image_size						,						do_center_crop=lowercase__							)
						_UpperCamelCase      :	Dict				       =     Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'							)
						_UpperCamelCase      :	Tuple				       =     image_processor(images=lowercase__						,						return_tensors='pt'							)
						_UpperCamelCase      :	Optional[Any]				       =     encoding['pixel_values']

						_UpperCamelCase      :	int				       =     (pixel_values,) if is_finetuned else (pixel_values, None)
						with torch.no_grad():
												_UpperCamelCase      :	Union[str, Any]				       =     orig_model(*lowercase__							)

						# 4. Load HF Data2VecVision model
						if is_finetuned:
												_UpperCamelCase      :	Dict				       =     DataaVecVisionForImageClassification(lowercase__							)
												hf_model.eval()
												_UpperCamelCase      :	str				       =     False
												_UpperCamelCase      :	List[str]				       =     'data2vec_vision.'
						else:
												_UpperCamelCase      :	List[Any]				       =     DataaVecVisionModel(lowercase__							)
												hf_model.eval()
												_UpperCamelCase      :	Dict				       =     True
												_UpperCamelCase      :	Union[str, Any]				       =     ''

						_UpperCamelCase      :	Tuple				       =     create_rename_keys(lowercase__						,						hf_prefix=lowercase__						,						has_lm_head=lowercase__							)
						_UpperCamelCase      :	List[Any]				       =     orig_model.state_dict()
						for src, dest in rename_keys:
												_UpperCamelCase      :	str				       =     state_dict.pop(lowercase__							)
												_UpperCamelCase      :	Tuple				       =     val

						read_in_q_k_v(lowercase__						,						lowercase__						,						hf_prefix=lowercase__						,						has_lm_head=lowercase__							)
						_UpperCamelCase       ,    _UpperCamelCase      :	Tuple				       =     hf_model.load_state_dict(lowercase__						,						strict=lowercase__							)
						print('HF missing'						,						lowercase__							)
						print('HF unexpected_keys'						,						lowercase__							)

						# 5. Forward HF Data2VecVision model
						with torch.no_grad():
												_UpperCamelCase      :	Union[str, Any]				       =     hf_model(lowercase__							)

						_UpperCamelCase      :	Union[str, Any]				       =     hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

						# 6. Compare
						_UpperCamelCase      :	Union[str, Any]				       =     torch.max(torch.abs(hf_output - orig_model_output							)							).item()

						print(f'max_absolute_diff = {max_absolute_diff}'							)
						_UpperCamelCase      :	Dict				       =     torch.allclose(lowercase__						,						lowercase__						,						atol=1E-3							)
						print('Do both models output the same tensors?'						,						'🔥' if success else '💩'							)
						if not success:
												raise Exception('Something went wRoNg'							)

						# 7. Save
						print(f'Saving to {args.hf_checkpoint_name}'							)
						hf_model.save_pretrained(args.hf_checkpoint_name							)
						image_processor.save_pretrained(args.hf_checkpoint_name							)


if __name__ == "__main__":
					main()
					# Run the following to convert checkpoints
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"







'''simple docstring'''
from __future__ import annotations

from queue import Queue






class 							lowercase__			:





	def __init__(				self      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	dict[int, list[int]]				       =     {}





	def 			UpperCamelCase_						(				self      :       int				):







							'''simple docstring'''
							for i in self.vertices:
													print(lowerCamelCase__				,' : '				,' -> '.join([str(lowerCamelCase__				) for j in self.vertices[i]]				)				)





	def 			UpperCamelCase_						(				self      :       Optional[Any]				,lowerCamelCase__      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							if from_vertex in self.vertices:
													self.vertices[from_vertex].append(lowerCamelCase__				)
							else:
													_UpperCamelCase      :	Any				       =     [to_vertex]





	def 			UpperCamelCase_						(				self      :       int				,lowerCamelCase__      :       int				):







							'''simple docstring'''
							_UpperCamelCase      :	Dict				       =     set()

							# create a first in first out queue to store all the vertices for BFS
							_UpperCamelCase      :	Queue				       =     Queue()

							# mark the source node as visited and enqueue it
							visited.add(lowerCamelCase__				)
							queue.put(lowerCamelCase__				)

							while not queue.empty():
													_UpperCamelCase      :	Optional[Any]				       =     queue.get()

													# loop through all adjacent vertex and enqueue it if not yet visited
													for adjacent_vertex in self.vertices[vertex]:
																			if adjacent_vertex not in visited:
																									queue.put(lowerCamelCase__				)
																									visited.add(lowerCamelCase__				)
							return visited


if __name__ == "__main__":
					from doctest import testmod

					testmod(verbose=True)

					snake_case_				:			int						 =						Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					# 0  :  1 -> 2
					# 1  :  2
					# 2  :  0 -> 3
					# 3  :  3

					assert sorted(g.bfs(2)) == [0, 1, 2, 3]


