


"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__				    =       logging.get_logger(__name__)
def  a__      (    SCREAMING_SNAKE_CASE   :   int    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]=False    ):


		'''simple docstring'''







		lowerCAmelCase					:					Dict								=							[]

		# fmt: off
		# stem:
		rename_keys.append(("cls_token", "vit.embeddings.cls_token")    )
		rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")    )

		rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")    )
		rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")    )

		# backbone
		rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")    )
		rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")    )
		rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")    )

		for stage_idx in range(len(config.backbone_config.depths    )    ):
				for layer_idx in range(config.backbone_config.depths[stage_idx]    ):
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")    )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")    )

				rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")    )
				rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")    )
				rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")    )

		# transformer encoder
		for i in range(config.num_hidden_layers    ):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")    )
				rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")    )
				rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")    )
				rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")    )
				rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")    )
				rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")    )
				rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")    )
				rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")    )
				rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")    )
				rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")    )

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ("norm.weight", "layernorm.weight"),
				        ("norm.bias", "layernorm.bias"),
				        ("pre_logits.fc.weight", "pooler.dense.weight"),
				        ("pre_logits.fc.bias", "pooler.dense.bias"),
				    ]    )

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				lowerCAmelCase					:					Any								=							[(pair[0], pair[1][4:]) if pair[1].startswith("vit"    ) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ("norm.weight", "vit.layernorm.weight"),
				        ("norm.bias", "vit.layernorm.bias"),
				        ("head.weight", "classifier.weight"),
				        ("head.bias", "classifier.bias"),
				    ]    )
		# fmt: on

		return rename_keys
def  a__      (    SCREAMING_SNAKE_CASE   :   Any    ,				SCREAMING_SNAKE_CASE   :   Optional[Any]    ,				SCREAMING_SNAKE_CASE   :   Union[str, Any]=False    ):


		'''simple docstring'''







		for i in range(config.num_hidden_layers    ):
				if base_model:
						lowerCAmelCase					:					str								=							""
				else:
						lowerCAmelCase					:					Any								=							"vit."
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				lowerCAmelCase					:					Optional[int]								=							state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""    )
				lowerCAmelCase					:					Dict								=							state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""    )
				# next, add query, keys and values (in that order) to the state dict
				lowerCAmelCase					:					Optional[Any]								=							in_proj_weight[
				    : config.hidden_size, :
				]
				lowerCAmelCase					:					Dict								=							in_proj_bias[: config.hidden_size]
				lowerCAmelCase					:					Union[str, Any]								=							in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				lowerCAmelCase					:					Optional[int]								=							in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				lowerCAmelCase					:					Union[str, Any]								=							in_proj_weight[
				    -config.hidden_size :, :
				]
				lowerCAmelCase					:					int								=							in_proj_bias[-config.hidden_size :]
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[int]								=							["head.weight", "head.bias"]
		for k in ignore_keys:
				state_dict.pop(_lowercase    ,				_lowercase    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ,				SCREAMING_SNAKE_CASE   :   List[str]    ,				SCREAMING_SNAKE_CASE   :   Dict    ):


		'''simple docstring'''







		lowerCAmelCase					:					List[str]								=							dct.pop(_lowercase    )
		lowerCAmelCase					:					List[Any]								=							val
def  a__      (    ):


		'''simple docstring'''







		lowerCAmelCase					:					Dict								=							"http://images.cocodataset.org/val2017/000000039769.jpg"
		lowerCAmelCase					:					int								=							Image.open(requests.get(_lowercase    ,				stream=_lowercase    ).raw    )
		return im
@torch.no_grad()
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ,				SCREAMING_SNAKE_CASE   :   Dict    ,				SCREAMING_SNAKE_CASE   :   Tuple=False    ):


		'''simple docstring'''







		lowerCAmelCase					:					Optional[Any]								=							BitConfig(
		    global_padding="same"    ,				layer_type="bottleneck"    ,				depths=(3, 4, 9)    ,				out_features=["stage3"]    ,				embedding_dynamic_padding=_lowercase    ,				)
		lowerCAmelCase					:					int								=							ViTHybridConfig(backbone_config=_lowercase    ,				image_size=3_8_4    ,				num_labels=1_0_0_0    )
		lowerCAmelCase					:					Union[str, Any]								=							False

		# load original model from timm
		lowerCAmelCase					:					Any								=							timm.create_model(_lowercase    ,				pretrained=_lowercase    )
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		lowerCAmelCase					:					Union[str, Any]								=							timm_model.state_dict()
		if base_model:
				remove_classification_head_(_lowercase    )
		lowerCAmelCase					:					Any								=							create_rename_keys(_lowercase    ,				_lowercase    )
		for src, dest in rename_keys:
				rename_key(_lowercase    ,				_lowercase    ,				_lowercase    )
		read_in_q_k_v(_lowercase    ,				_lowercase    ,				_lowercase    )

		lowerCAmelCase					:					Dict								=							"huggingface/label-files"
		lowerCAmelCase					:					Optional[int]								=							"imagenet-1k-id2label.json"
		lowerCAmelCase					:					Tuple								=							json.load(open(hf_hub_download(_lowercase    ,				_lowercase    ,				repo_type="dataset"    )    ,				"r"    )    )
		lowerCAmelCase					:					int								=							{int(_lowercase    ): v for k, v in idalabel.items()}
		lowerCAmelCase					:					Dict								=							idalabel
		lowerCAmelCase					:					Optional[Any]								=							{v: k for k, v in idalabel.items()}

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				lowerCAmelCase					:					Tuple								=							ViTHybridModel(_lowercase    ).eval()
		else:
				lowerCAmelCase					:					Optional[int]								=							ViTHybridForImageClassification(_lowercase    ).eval()
		model.load_state_dict(_lowercase    )

		# create image processor
		lowerCAmelCase					:					Union[str, Any]								=							create_transform(**resolve_data_config({}    ,				model=_lowercase    )    )
		lowerCAmelCase					:					Union[str, Any]								=							transform.transforms

		lowerCAmelCase					:					Optional[Any]								=							{
		    "bilinear": PILImageResampling.BILINEAR,
		    "bicubic": PILImageResampling.BICUBIC,
		    "nearest": PILImageResampling.NEAREST,
		}

		lowerCAmelCase					:					List[Any]								=							ViTHybridImageProcessor(
		    do_resize=_lowercase    ,				size={"shortest_edge": timm_transforms[0].size}    ,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,				do_center_crop=_lowercase    ,				crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}    ,				do_normalize=_lowercase    ,				image_mean=timm_transforms[-1].mean.tolist()    ,				image_std=timm_transforms[-1].std.tolist()    ,				)

		lowerCAmelCase					:					Optional[int]								=							prepare_img()
		lowerCAmelCase					:					List[Any]								=							transform(_lowercase    ).unsqueeze(0    )
		lowerCAmelCase					:					List[Any]								=							processor(_lowercase    ,				return_tensors="pt"    ).pixel_values

		# verify pixel values
		assert torch.allclose(_lowercase    ,				_lowercase    )

		# verify logits
		with torch.no_grad():
				lowerCAmelCase					:					List[str]								=							model(_lowercase    )
				lowerCAmelCase					:					Union[str, Any]								=							outputs.logits

		print("Predicted class:"    ,				logits.argmax(-1    ).item()    )
		if base_model:
				lowerCAmelCase					:					str								=							timm_model.forward_features(_lowercase    )
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(_lowercase    ,				outputs.pooler_output    ,				atol=1E-3    )
		else:
				lowerCAmelCase					:					int								=							timm_model(_lowercase    )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(_lowercase    ,				outputs.logits    ,				atol=1E-3    )
		print("Looks ok!"    )

		if pytorch_dump_folder_path is not None:
				Path(_lowercase    ).mkdir(exist_ok=_lowercase    )
				print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""    )
				model.save_pretrained(_lowercase    )
				print(f"""Saving processor to {pytorch_dump_folder_path}"""    )
				processor.save_pretrained(_lowercase    )

		if push_to_hub:
				print(f"""Pushing model and processor to the hub {vit_name}"""    )
				model.push_to_hub(f"""ybelkada/{vit_name}"""    )
				processor.push_to_hub(f"""ybelkada/{vit_name}"""    )


if __name__ == "__main__":
					lowerCAmelCase__				    =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--vit_name''',
					    default='''vit_base_r50_s16_384''',
					    type=str,
					    help='''Name of the hybrid ViT timm model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
					)

					lowerCAmelCase__				    =       parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""







import baseaa
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  return baseaa.baaencode(string.encode("utf-8"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   bytes    ):


  '''simple docstring'''







  return baseaa.baadecode(SCREAMING_SNAKE_CASE    ).decode("utf-8"    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       '''Hello World!'''
     lowerCAmelCase__				    =       baseaa_encode(test)
     print(encoded)

     lowerCAmelCase__				    =       baseaa_decode(encoded)
     print(decoded)





