
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       :  Optional[int]	,__UpperCamelCase       :  List[str]=False	,__UpperCamelCase       :  Tuple=False	,__UpperCamelCase       :  str="data2vec."    ):
			lowerCAmelCase__ :    List[str]				      =			'''backbone.''' if is_semantic else ''''''

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

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

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

						lowerCAmelCase__ :    List[str]				      =			gamma_a
						lowerCAmelCase__ :    List[str]				      =			gamma_a

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

									lowerCAmelCase__ :    Dict				      =			table
									lowerCAmelCase__ :    List[Any]				      =			index



def      _a     (   ):
			lowerCAmelCase__ :    Any				      =			argparse.ArgumentParser(
			    '''Convert Data2VecVision to HF for image classification and pretraining'''	,add_help=__UpperCamelCase    )
			parser.add_argument('''--hf_checkpoint_name'''	,type=__UpperCamelCase    )
			parser.add_argument('''--input_size'''	,default=224	,type=__UpperCamelCase	,help='''images input size'''    )
			parser.add_argument('''--beit_checkpoint'''	,default=''''''	,help='''beit checkpoint'''    )

			return parser.parse_args()



def      _a     (   __UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  int	,__UpperCamelCase       :  Union[str, Any]    ):
			def load_state_dict(__UpperCamelCase       :  Any	,__UpperCamelCase       :  str	,__UpperCamelCase       :  str=""	,__UpperCamelCase       :  Tuple="relative_position_index"    ):
						lowerCAmelCase__ :    List[str]				      =			[]
						lowerCAmelCase__ :    Tuple				      =			[]
						lowerCAmelCase__ :    Dict				      =			[]
						# copy state_dict so _load_from_state_dict can modify it
						lowerCAmelCase__ :    Tuple				      =			getattr(__UpperCamelCase	,'''_metadata'''	,__UpperCamelCase    )
						lowerCAmelCase__ :    Optional[int]				      =			state_dict.copy()
						if metadata is not None:
									lowerCAmelCase__ :    Optional[int]				      =			metadata

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

						load(__UpperCamelCase	,prefix=__UpperCamelCase    )

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

						lowerCAmelCase__ :    Optional[Any]				      =			warn_missing_keys

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

			lowerCAmelCase__ :    int				      =			{
			    '''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''': 0.001,
						        '''use_rel_pos_bias''': True,
						    }    )

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

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

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

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

			load_state_dict(__UpperCamelCase	,__UpperCamelCase	,prefix=''''''    )

			return model



def      _a     (   ):
			lowerCAmelCase__ :    Optional[int]				      =			get_args()

			lowerCAmelCase__ :    List[str]				      =			'''ft1k''' in args.hf_checkpoint_name
			lowerCAmelCase__ :    Optional[int]				      =			'''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
			lowerCAmelCase__ :    Optional[Any]				      =			DataaVecVisionConfig()
			if is_finetuned:
						lowerCAmelCase__ :    List[str]				      =			True
						lowerCAmelCase__ :    List[Any]				      =			False
						lowerCAmelCase__ :    Dict				      =			True
						lowerCAmelCase__ :    Tuple				      =			1000

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

			if is_large:
						lowerCAmelCase__ :    Any				      =			1024
						lowerCAmelCase__ :    List[str]				      =			4096
						lowerCAmelCase__ :    str				      =			24
						lowerCAmelCase__ :    Union[str, Any]				      =			16

			# 2. Load Beit model
			lowerCAmelCase__ :    Any				      =			load_beit_model(__UpperCamelCase	,__UpperCamelCase	,__UpperCamelCase    )
			orig_model.eval()

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

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

			# 4. Load HF Data2VecVision model
			if is_finetuned:
						lowerCAmelCase__ :    List[str]				      =			DataaVecVisionForImageClassification(__UpperCamelCase    )
						hf_model.eval()
						lowerCAmelCase__ :    Any				      =			False
						lowerCAmelCase__ :    Tuple				      =			'''data2vec_vision.'''
			else:
						lowerCAmelCase__ :    str				      =			DataaVecVisionModel(__UpperCamelCase    )
						hf_model.eval()
						lowerCAmelCase__ :    List[Any]				      =			True
						lowerCAmelCase__ :    str				      =			''''''

			lowerCAmelCase__ :    str				      =			create_rename_keys(__UpperCamelCase	,hf_prefix=__UpperCamelCase	,has_lm_head=__UpperCamelCase    )
			lowerCAmelCase__ :    Dict				      =			orig_model.state_dict()
			for src, dest in rename_keys:
						lowerCAmelCase__ :    int				      =			state_dict.pop(__UpperCamelCase    )
						lowerCAmelCase__ :    str				      =			val

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

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

			lowerCAmelCase__ :    str				      =			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

			# 6. Compare
			lowerCAmelCase__ :    Optional[int]				      =			torch.max(torch.abs(hf_output - orig_model_output    )    ).item()

			print(f'''max_absolute_diff = {max_absolute_diff}'''    )
			lowerCAmelCase__ :    str				      =			torch.allclose(__UpperCamelCase	,__UpperCamelCase	,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"





import numpy as np
from numpy import floataa
from numpy.typing import NDArray



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    Any				      =			np.shape(__UpperCamelCase    )

			lowerCAmelCase__ :    NDArray[floataa]				      =			np.zeros((rows, 1)	,dtype=__UpperCamelCase    )
			for row in reversed(range(__UpperCamelCase    )    ):
						lowerCAmelCase__ :    List[str]				      =			0
						for col in range(row + 1	,__UpperCamelCase    ):
									total += coefficients[row, col] * x[col]

						lowerCAmelCase__ :    int				      =			(vector[row] - total) / coefficients[row, row]

			return x



def      _a     (   __UpperCamelCase       :  NDArray[floataa]	,__UpperCamelCase       :  NDArray[floataa]    ):
			lowerCAmelCase__      ,				lowerCAmelCase__ :    str				      =			np.shape(__UpperCamelCase    )
			if rows != columns:
						return np.array(()	,dtype=__UpperCamelCase    )

			# augmented matrix
			lowerCAmelCase__ :    NDArray[floataa]				      =			np.concatenate((coefficients, vector)	,axis=1    )
			lowerCAmelCase__ :    List[str]				      =			augmented_mat.astype('''float64'''    )

			# scale the matrix leaving it triangular
			for row in range(rows - 1    ):
						lowerCAmelCase__ :    str				      =			augmented_mat[row, row]
						for col in range(row + 1	,__UpperCamelCase    ):
									lowerCAmelCase__ :    Optional[int]				      =			augmented_mat[col, row] / pivot
									augmented_mat[col, :] -= factor * augmented_mat[row, :]

			lowerCAmelCase__ :    int				      =			retroactive_resolution(
			    augmented_mat[:, 0:columns]	,augmented_mat[:, columns : columns + 1]    )

			return x


if __name__ == "__main__":
							import doctest

							doctest.testmod()



