






"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__					):
	return 1 if digit in (0, 1) else (digit * factorial(digit - 1					))






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Union[str, Any]	    =   0
	A_		:						str	    =   number
	while duplicate > 0:
		A_    ,     A_		:						Any	    =   divmod(snake_case__   ,   10					)
		fact_sum += factorial(snake_case__					)
	return fact_sum == number


if __name__ == "__main__":
							print("Program to check whether a number is a Krisnamurthy Number or not.")
							_lowerCAmelCase         =							int(input("Enter number: ").strip())
							print(
							    F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
							)









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






def 			__UpperCamelCase	(		snake_case__					):
	A_		:						Tuple	    =   ["""head.weight""", """head.bias"""]
	for k in ignore_keys:
		state_dict.pop(snake_case__   ,   snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Union[str, Any]	    =   dct.pop(snake_case__					)
	A_		:						List[Any]	    =   val






def 			__UpperCamelCase	(		):
	A_		:						Any	    =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:						str	    =   Image.open(requests.get(snake_case__   ,   stream=snake_case__					).raw					)
	return im






@torch.no_grad()
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=False					):
	A_		:						str	    =   BitConfig(
	    global_padding="""same"""   ,   layer_type="""bottleneck"""   ,   depths=(3, 4, 9)   ,   out_features=["""stage3"""]   ,   embedding_dynamic_padding=snake_case__   ,   )
	A_		:						int	    =   ViTHybridConfig(backbone_config=snake_case__   ,   image_size=384   ,   num_labels=1_000					)
	A_		:						Any	    =   False

	# load original model from timm
	A_		:						str	    =   timm.create_model(snake_case__   ,   pretrained=snake_case__					)
	timm_model.eval()

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

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

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		A_		:						int	    =   ViTHybridModel(snake_case__					).eval()
	else:
		A_		:						Union[str, Any]	    =   ViTHybridForImageClassification(snake_case__					).eval()
	model.load_state_dict(snake_case__					)

	# create image processor
	A_		:						Any	    =   create_transform(**resolve_data_config({}   ,   model=snake_case__					)					)
	A_		:						List[Any]	    =   transform.transforms

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

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

	A_		:						Tuple	    =   prepare_img()
	A_		:						List[str]	    =   transform(snake_case__					).unsqueeze(0					)
	A_		:						int	    =   processor(snake_case__   ,   return_tensors="""pt"""					).pixel_values

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

	# verify logits
	with torch.no_grad():
		A_		:						List[str]	    =   model(snake_case__					)
		A_		:						Any	    =   outputs.logits

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

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

	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)

