



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()
lowercase_	       =			logging.get_logger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Any=False	)  ->						Union[str, Any]:
					__lowerCAmelCase	   =[]

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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Tuple	)  ->						Any:
					__lowerCAmelCase	   =["""head.weight""", """head.bias"""]
					for k in ignore_keys:
										state_dict.pop(__lowerCamelCase				,   __lowerCamelCase	)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[str]				,   __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Optional[int]	)  ->						Optional[Any]:
					__lowerCAmelCase	   =dct.pop(__lowerCamelCase	)
					__lowerCAmelCase	   =val






def 					__lowerCAmelCase		(  )  ->						Optional[int]:
					__lowerCAmelCase	   ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
					__lowerCAmelCase	   =Image.open(requests.get(__lowerCamelCase				,   stream=__lowerCamelCase	).raw	)
					return im






@torch.no_grad()
def 					__lowerCAmelCase		(  __lowerCamelCase	:    str				,   __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    int=False	)  ->						List[Any]:
					__lowerCAmelCase	   =BitConfig(
					    global_padding="""same"""				,   layer_type="""bottleneck"""				,   depths=(3, 4, 9)				,   out_features=["""stage3"""]				,   embedding_dynamic_padding=__lowerCamelCase				,   )
					__lowerCAmelCase	   =ViTHybridConfig(backbone_config=__lowerCamelCase				,   image_size=384				,   num_labels=1000	)
					__lowerCAmelCase	   =False

					# load original model from timm
					__lowerCAmelCase	   =timm.create_model(__lowerCamelCase				,   pretrained=__lowerCamelCase	)
					timm_model.eval()

					# load state_dict of original model, remove and rename some keys
					__lowerCAmelCase	   =timm_model.state_dict()
					if base_model:
										remove_classification_head_(__lowerCamelCase	)
					__lowerCAmelCase	   =create_rename_keys(__lowerCamelCase				,   __lowerCamelCase	)
					for src, dest in rename_keys:
										rename_key(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)
					read_in_q_k_v(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)

					__lowerCAmelCase	   ="""huggingface/label-files"""
					__lowerCAmelCase	   ="""imagenet-1k-id2label.json"""
					__lowerCAmelCase	   =json.load(open(hf_hub_download(__lowerCamelCase				,   __lowerCamelCase				,   repo_type="""dataset"""	)				,   """r"""	)	)
					__lowerCAmelCase	   ={int(__lowerCamelCase	): v for k, v in idalabel.items()}
					__lowerCAmelCase	   =idalabel
					__lowerCAmelCase	   ={v: k for k, v in idalabel.items()}

					# load HuggingFace model
					if vit_name[-5:] == "in21k":
										__lowerCAmelCase	   =ViTHybridModel(__lowerCamelCase	).eval()
					else:
										__lowerCAmelCase	   =ViTHybridForImageClassification(__lowerCamelCase	).eval()
					model.load_state_dict(__lowerCamelCase	)

					# create image processor
					__lowerCAmelCase	   =create_transform(**resolve_data_config({}				,   model=__lowerCamelCase	)	)
					__lowerCAmelCase	   =transform.transforms

					__lowerCAmelCase	   ={
					    """bilinear""": PILImageResampling.BILINEAR,
					    """bicubic""": PILImageResampling.BICUBIC,
					    """nearest""": PILImageResampling.NEAREST,
					}

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

					__lowerCAmelCase	   =prepare_img()
					__lowerCAmelCase	   =transform(__lowerCamelCase	).unsqueeze(0	)
					__lowerCAmelCase	   =processor(__lowerCamelCase				,   return_tensors="""pt"""	).pixel_values

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

					# verify logits
					with torch.no_grad():
										__lowerCAmelCase	   =model(__lowerCamelCase	)
										__lowerCAmelCase	   =outputs.logits

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

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

					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__":
				lowercase_	       =			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.'''
				)

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









def 					__lowerCAmelCase		(  )  ->						Tuple:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =1

					while len(__lowerCamelCase	) < 1E6:
										constant.append(str(__lowerCamelCase	)	)
										i += 1

					__lowerCAmelCase	   ="""""".join(__lowerCamelCase	)

					return (
					    int(constant[0]	)
					    * int(constant[9]	)
					    * int(constant[99]	)
					    * int(constant[999]	)
					    * int(constant[9999]	)
					    * int(constant[99999]	)
					    * int(constant[999999]	)
					)


if __name__ == "__main__":
				print(solution())




