
'''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 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=False  ,					_SCREAMING_SNAKE_CASE=False  ,					_SCREAMING_SNAKE_CASE="data2vec."  ):
		lowerCAmelCase_   :       str    							='''backbone.''' if is_semantic else ''''''

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

				lowerCAmelCase_   :       Optional[int]    							=in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCAmelCase_   :       Optional[int]    							=q_bias
				lowerCAmelCase_   :       Optional[int]    							=in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase_   :       str    							=in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase_   :       Union[str, Any]    							=v_bias

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

				lowerCAmelCase_   :       Dict    							=gamma_a
				lowerCAmelCase_   :       Any    							=gamma_a

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

						lowerCAmelCase_   :       Optional[Any]    							=table
						lowerCAmelCase_   :       Any    							=index







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

		return parser.parse_args()







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

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

				load(_SCREAMING_SNAKE_CASE  ,					prefix=_SCREAMING_SNAKE_CASE  )

				lowerCAmelCase_   :       Any    							=[]
				lowerCAmelCase_   :       Dict    							=[]
				for key in missing_keys:
						lowerCAmelCase_   :       int    							=True
						for ignore_key in ignore_missing.split('''|'''  ):
								if ignore_key in key:
										lowerCAmelCase_   :       Optional[int]    							=False
										break
						if keep_flag:
								warn_missing_keys.append(_SCREAMING_SNAKE_CASE  )
						else:
								ignore_missing_keys.append(_SCREAMING_SNAKE_CASE  )

				lowerCAmelCase_   :       List[str]    							=warn_missing_keys

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

		lowerCAmelCase_   :       Optional[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_01,
				        '''use_rel_pos_bias''': True,
				    }  )

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

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

		lowerCAmelCase_   :       Union[str, Any]    							=list(checkpoint_model.keys()  )
		for key in all_keys:
				if "relative_position_index" in key:
						checkpoint_model.pop(_SCREAMING_SNAKE_CASE  )

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

		load_state_dict(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					prefix=''''''  )

		return model







def 		SCREAMING_SNAKE_CASE__					(							):
		lowerCAmelCase_   :       Any    							=get_args()

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

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

		if is_large:
				lowerCAmelCase_   :       List[Any]    							=1024
				lowerCAmelCase_   :       Optional[Any]    							=4096
				lowerCAmelCase_   :       int    							=24
				lowerCAmelCase_   :       Union[str, Any]    							=16

		# 2. Load Beit model
		lowerCAmelCase_   :       List[str]    							=load_beit_model(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  )
		orig_model.eval()

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

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

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

		lowerCAmelCase_   :       List[str]    							=create_rename_keys(_SCREAMING_SNAKE_CASE  ,					hf_prefix=_SCREAMING_SNAKE_CASE  ,					has_lm_head=_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Dict    							=orig_model.state_dict()
		for src, dest in rename_keys:
				lowerCAmelCase_   :       str    							=state_dict.pop(_SCREAMING_SNAKE_CASE  )
				lowerCAmelCase_   :       Optional[Any]    							=val

		read_in_q_k_v(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					hf_prefix=_SCREAMING_SNAKE_CASE  ,					has_lm_head=_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=hf_model.load_state_dict(_SCREAMING_SNAKE_CASE  ,					strict=_SCREAMING_SNAKE_CASE  )
		print('''HF missing'''  ,					_SCREAMING_SNAKE_CASE  )
		print('''HF unexpected_keys'''  ,					_SCREAMING_SNAKE_CASE  )

		# 5. Forward HF Data2VecVision model
		with torch.no_grad():
				lowerCAmelCase_   :       Tuple    							=hf_model(_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       Any    							=hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

		print(f'max_absolute_diff = {max_absolute_diff}'  )
		lowerCAmelCase_   :       Dict    							=torch.allclose(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					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







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))





