code: 
  
  
  """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_   (  lowercase__   :List[Any], lowercase__   :str=False, lowercase__   :Dict=False, lowercase__   :Union[str, Any]="data2vec."    ):
  			__lowerCamelCase        =			"""backbone.""" if is_semantic else """"""
  
  			__lowerCamelCase        =			[]
  			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_   (  lowercase__   :List[str], lowercase__   :Dict, lowercase__   :int=False, lowercase__   :Union[str, Any]=False, lowercase__   :str="data2vec_vision."    ):
  			for i in range(config.num_hidden_layers    ):
  						__lowerCamelCase        =			"""backbone.""" if is_semantic else """"""
  						# queries, keys and values
  						__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight'    )
  						__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias'    )
  						__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias'    )
  
  						__lowerCamelCase        =			in_proj_weight[
  						    : config.hidden_size, :
  						]
  						__lowerCamelCase        =			q_bias
  						__lowerCamelCase        =			in_proj_weight[
  						    config.hidden_size : config.hidden_size * 2, :
  						]
  						__lowerCamelCase        =			in_proj_weight[
  						    -config.hidden_size :, :
  						]
  						__lowerCamelCase        =			v_bias
  
  						# gamma_1 and gamma_2
  						# we call them lambda because otherwise they are renamed when using .from_pretrained
  						__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.gamma_1'    )
  						__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.gamma_2'    )
  
  						__lowerCamelCase        =			gamma_a
  						__lowerCamelCase        =			gamma_a
  
  						# relative_position bias table + index
  						if not has_lm_head:
  									# each layer has its own relative position bias
  									__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table'    )
  									__lowerCamelCase        =			state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index'    )
  
  									__lowerCamelCase        =			table
  									__lowerCamelCase        =			index
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			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=224, type=lowercase__, help="""images input size"""    )
  			parser.add_argument("""--beit_checkpoint""", default="""""", help="""beit checkpoint"""    )
  
  			return parser.parse_args()
  
  
  
  
  
  def 			a_   (  lowercase__   :str, lowercase__   :Optional[int], lowercase__   :Any    ):
  			def load_state_dict(lowercase__   :int, lowercase__   :str, lowercase__   :str="", lowercase__   :Optional[int]="relative_position_index"    ):
  						__lowerCamelCase        =			[]
  						__lowerCamelCase        =			[]
  						__lowerCamelCase        =			[]
  						# copy state_dict so _load_from_state_dict can modify it
  						__lowerCamelCase        =			getattr(lowercase__, """_metadata""", lowercase__    )
  						__lowerCamelCase        =			state_dict.copy()
  						if metadata is not None:
  									__lowerCamelCase        =			metadata
  
  						def load(lowercase__   :Optional[int], lowercase__   :Dict=""    ):
  									__lowerCamelCase        =			{} 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__    )
  
  						__lowerCamelCase        =			[]
  						__lowerCamelCase        =			[]
  						for key in missing_keys:
  									__lowerCamelCase        =			True
  									for ignore_key in ignore_missing.split("""|"""    ):
  												if ignore_key in key:
  															__lowerCamelCase        =			False
  															break
  									if keep_flag:
  												warn_missing_keys.append(lowercase__    )
  									else:
  												ignore_missing_keys.append(lowercase__    )
  
  						__lowerCamelCase        =			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__    )    )
  
  			__lowerCamelCase        =			{
  			    """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        =			create_model(
  			    """beit_large_patch16_224""" if is_large else """beit_base_patch16_224""", **lowercase__, )
  			__lowerCamelCase        =			model.patch_embed.patch_size
  			__lowerCamelCase        =			(args.input_size // patch_size[0], args.input_size // patch_size[1])
  			__lowerCamelCase        =			torch.load(args.beit_checkpoint, map_location="""cpu"""    )
  
  			print(f'Load ckpt from {args.beit_checkpoint}'    )
  			__lowerCamelCase        =			None
  			for model_key in ("model", "module"):
  						if model_key in checkpoint:
  									__lowerCamelCase        =			checkpoint[model_key]
  									print(f'Load state_dict by model_key = {model_key}'    )
  									break
  
  			__lowerCamelCase        =			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:
  									__lowerCamelCase        =			checkpoint_model[key]
  									__lowerCamelCase    ,__lowerCamelCase        =			rel_pos_bias.size()
  									__lowerCamelCase    ,__lowerCamelCase        =			model.state_dict()[key].size()
  									__lowerCamelCase        =			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_   (  ):
  			__lowerCamelCase        =			get_args()
  
  			__lowerCamelCase        =			"""ft1k""" in args.hf_checkpoint_name
  			__lowerCamelCase        =			"""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        =			DataaVecVisionConfig()
  			if is_finetuned:
  						__lowerCamelCase        =			True
  						__lowerCamelCase        =			False
  						__lowerCamelCase        =			True
  						__lowerCamelCase        =			1000
  
  						__lowerCamelCase        =			"""huggingface/label-files"""
  						__lowerCamelCase        =			"""imagenet-1k-id2label.json"""
  						__lowerCamelCase        =			json.load(open(hf_hub_download(lowercase__, lowercase__, repo_type="""dataset"""    ), """r"""    )    )
  						__lowerCamelCase        =			{int(lowercase__    ): v for k, v in idalabel.items()}
  						__lowerCamelCase        =			idalabel
  						__lowerCamelCase        =			{v: k for k, v in idalabel.items()}
  			else:
  						__lowerCamelCase        =			False
  						__lowerCamelCase        =			True
  						__lowerCamelCase        =			False
  
  			if is_large:
  						__lowerCamelCase        =			1024
  						__lowerCamelCase        =			4096
  						__lowerCamelCase        =			24
  						__lowerCamelCase        =			16
  
  			# 2. Load Beit model
  			__lowerCamelCase        =			load_beit_model(lowercase__, lowercase__, lowercase__    )
  			orig_model.eval()
  
  			# 3. Forward Beit model
  			__lowerCamelCase        =			BeitImageProcessor(size=config.image_size, do_center_crop=lowercase__    )
  			__lowerCamelCase        =			Image.open("""../../../../tests/fixtures/tests_samples/COCO/000000039769.png"""    )
  			__lowerCamelCase        =			image_processor(images=lowercase__, return_tensors="""pt"""    )
  			__lowerCamelCase        =			encoding["""pixel_values"""]
  
  			__lowerCamelCase        =			(pixel_values,) if is_finetuned else (pixel_values, None)
  			with torch.no_grad():
  						__lowerCamelCase        =			orig_model(*lowercase__    )
  
  			# 4. Load HF Data2VecVision model
  			if is_finetuned:
  						__lowerCamelCase        =			DataaVecVisionForImageClassification(lowercase__    )
  						hf_model.eval()
  						__lowerCamelCase        =			False
  						__lowerCamelCase        =			"""data2vec_vision."""
  			else:
  						__lowerCamelCase        =			DataaVecVisionModel(lowercase__    )
  						hf_model.eval()
  						__lowerCamelCase        =			True
  						__lowerCamelCase        =			""""""
  
  			__lowerCamelCase        =			create_rename_keys(lowercase__, hf_prefix=lowercase__, has_lm_head=lowercase__    )
  			__lowerCamelCase        =			orig_model.state_dict()
  			for src, dest in rename_keys:
  						__lowerCamelCase        =			state_dict.pop(lowercase__    )
  						__lowerCamelCase        =			val
  
  			read_in_q_k_v(lowercase__, lowercase__, hf_prefix=lowercase__, has_lm_head=lowercase__    )
  			__lowerCamelCase    ,__lowerCamelCase        =			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():
  						__lowerCamelCase        =			hf_model(lowercase__    )
  
  			__lowerCamelCase        =			hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state
  
  			# 6. Compare
  			__lowerCamelCase        =			torch.max(torch.abs(hf_output - orig_model_output    )    ).item()
  
  			print(f'max_absolute_diff = {max_absolute_diff}'    )
  			__lowerCamelCase        =			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"
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  def 			a_   (  lowercase__   :str, lowercase__   :int = 13    ):
  			__lowerCamelCase        =			""""""
  			for c in s:
  						if "A" <= c <= "Z":
  									out += chr(ord("""A"""    ) + (ord(lowercase__    ) - ord("""A"""    ) + n) % 26    )
  						elif "a" <= c <= "z":
  									out += chr(ord("""a"""    ) + (ord(lowercase__    ) - ord("""a"""    ) + n) % 26    )
  						else:
  									out += c
  			return out
  
  
  
  
  
  def 			a_   (  ):
  			__lowerCamelCase        =			input("""Enter message: """    )
  
  			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
  			print("""Encryption:""", lowercase__    )
  
  			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
  			print("""Decryption: """, lowercase__    )
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()
  					main()
  
  
  
  
  