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 					UpperCamelCase__		(							A__     ,					A__=False     ,					A__=False     ,					A__="data2vec."       )  ->   int:
				snake_case__   :      Union[str, Any]   				= 'backbone.' if is_semantic else ''

				snake_case__   :      str   				= []
				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 					UpperCamelCase__		(							A__     ,					A__     ,					A__=False     ,					A__=False     ,					A__="data2vec_vision."       )  ->   List[Any]:
				for i in range(config.num_hidden_layers       ):
								snake_case__   :      str   				= 'backbone.' if is_semantic else ''
								# queries, keys and values
								snake_case__   :      Optional[int]   				= state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""       )
								snake_case__   :      Dict   				= state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""       )
								snake_case__   :      List[str]   				= state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""       )

								snake_case__   :      List[Any]   				= in_proj_weight[
								    : config.hidden_size, :
								]
								snake_case__   :      int   				= q_bias
								snake_case__   :      Tuple   				= in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								snake_case__   :      Any   				= in_proj_weight[
								    -config.hidden_size :, :
								]
								snake_case__   :      Any   				= v_bias

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

								snake_case__   :      Any   				= gamma_a
								snake_case__   :      Optional[Any]   				= gamma_a

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

												snake_case__   :      Tuple   				= table
												snake_case__   :      Optional[int]   				= index
def 					UpperCamelCase__		(							)  ->   Optional[Any]:
				snake_case__   :      int   				= argparse.ArgumentParser(
				    'Convert Data2VecVision to HF for image classification and pretraining'     ,					add_help=A__       )
				parser.add_argument('--hf_checkpoint_name'     ,					type=A__       )
				parser.add_argument('--input_size'     ,					default=224     ,					type=A__     ,					help='images input size'       )
				parser.add_argument('--beit_checkpoint'     ,					default=''     ,					help='beit checkpoint'       )

				return parser.parse_args()
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__       )  ->   Any:
				def load_state_dict(A__     ,					A__     ,					A__=""     ,					A__="relative_position_index"       ):
								snake_case__   :      Tuple   				= []
								snake_case__   :      List[str]   				= []
								snake_case__   :      Optional[Any]   				= []
								# copy state_dict so _load_from_state_dict can modify it
								snake_case__   :      Optional[int]   				= getattr(A__     ,					'_metadata'     ,					A__       )
								snake_case__   :      Dict   				= state_dict.copy()
								if metadata is not None:
												snake_case__   :      List[Any]   				= metadata

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

								load(A__     ,					prefix=A__       )

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

								snake_case__   :      List[str]   				= warn_missing_keys

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

				snake_case__   :      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': 1000,
								        'use_mean_pooling': True,
								        'init_scale': 0.0_0_1,
								        'use_rel_pos_bias': True,
								    }       )

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

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

				snake_case__   :      List[str]   				= list(checkpoint_model.keys()       )
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(A__       )

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

				load_state_dict(A__     ,					A__     ,					prefix=''       )

				return model
def 					UpperCamelCase__		(							)  ->   Optional[int]:
				snake_case__   :      int   				= get_args()

				snake_case__   :      Tuple   				= 'ft1k' in args.hf_checkpoint_name
				snake_case__   :      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
				snake_case__   :      Any   				= DataaVecVisionConfig()
				if is_finetuned:
								snake_case__   :      str   				= True
								snake_case__   :      List[Any]   				= False
								snake_case__   :      str   				= True
								snake_case__   :      Union[str, Any]   				= 1000

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

				if is_large:
								snake_case__   :      List[str]   				= 1024
								snake_case__   :      List[Any]   				= 4096
								snake_case__   :      Optional[Any]   				= 24
								snake_case__   :      Tuple   				= 16

				# 2. Load Beit model
				snake_case__   :      int   				= load_beit_model(A__     ,					A__     ,					A__       )
				orig_model.eval()

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

				snake_case__   :      str   				= (pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								snake_case__   :      List[Any]   				= orig_model(*A__       )

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

				snake_case__   :      Optional[int]   				= create_rename_keys(A__     ,					hf_prefix=A__     ,					has_lm_head=A__       )
				snake_case__   :      str   				= orig_model.state_dict()
				for src, dest in rename_keys:
								snake_case__   :      Optional[int]   				= state_dict.pop(A__       )
								snake_case__   :      int   				= val

				read_in_q_k_v(A__     ,					A__     ,					hf_prefix=A__     ,					has_lm_head=A__       )
				snake_case__				,    snake_case__   :      Optional[int]   				= hf_model.load_state_dict(A__     ,					strict=A__       )
				print('HF missing'     ,					A__       )
				print('HF unexpected_keys'     ,					A__       )

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								snake_case__   :      Any   				= hf_model(A__       )

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

				# 6. Compare
				snake_case__   :      Dict   				= torch.max(torch.abs(hf_output - orig_model_output       )       ).item()

				print(F"""max_absolute_diff = {max_absolute_diff}"""       )
				snake_case__   :      Optional[int]   				= torch.allclose(A__     ,					A__     ,					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"



def 					UpperCamelCase__		(							A__ = 400_0000       )  ->   int:

				if n <= 1:
								return 0
				snake_case__   :      Optional[Any]   				= 0
				snake_case__   :      Optional[int]   				= 2
				snake_case__   :      str   				= 0
				while 4 * b + a <= n:
								snake_case__				,    snake_case__   :      Optional[Any]   				= b, 4 * b + a
								count += a
				return count + b


if __name__ == "__main__":
				print(F'''{solution() = }''')


