
"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel



def     __magic_name__		(					UpperCamelCase     :		List[str]     )     ->						str:

    if "img_encoder.pos_embed" in name:
        a__		=			name.replace('img_encoder.pos_embed'							,		'vision_model.embeddings.position_embeddings'     )
    if "img_encoder.patch_embed.proj" in name:
        a__		=			name.replace('img_encoder.patch_embed.proj'							,		'vision_model.embeddings.patch_embeddings.projection'     )
    if "img_encoder.patch_embed.norm" in name:
        a__		=			name.replace('img_encoder.patch_embed.norm'							,		'vision_model.embeddings.layernorm'     )
    if "img_encoder.layers" in name:
        a__		=			name.replace('img_encoder.layers'							,		'vision_model.encoder.stages'     )
    if "blocks" in name and "res" not in name:
        a__		=			name.replace('blocks'							,		'layers'     )
    if "attn" in name and "pre_assign" not in name:
        a__		=			name.replace('attn'							,		'self_attn'     )
    if "proj" in name and "self_attn" in name and "text" not in name:
        a__		=			name.replace('proj'							,		'out_proj'     )
    if "pre_assign_attn.attn.proj" in name:
        a__		=			name.replace('pre_assign_attn.attn.proj'							,		'pre_assign_attn.attn.out_proj'     )
    if "norm1" in name:
        a__		=			name.replace('norm1'							,		'layer_norm1'     )
    if "norm2" in name and "pre_assign" not in name:
        a__		=			name.replace('norm2'							,		'layer_norm2'     )
    if "img_encoder.norm" in name:
        a__		=			name.replace('img_encoder.norm'							,		'vision_model.layernorm'     )
    # text encoder
    if "text_encoder.token_embedding" in name:
        a__		=			name.replace('text_encoder.token_embedding'							,		'text_model.embeddings.token_embedding'     )
    if "text_encoder.positional_embedding" in name:
        a__		=			name.replace('text_encoder.positional_embedding'							,		'text_model.embeddings.position_embedding.weight'     )
    if "text_encoder.transformer.resblocks." in name:
        a__		=			name.replace('text_encoder.transformer.resblocks.'							,		'text_model.encoder.layers.'     )
    if "ln_1" in name:
        a__		=			name.replace('ln_1'							,		'layer_norm1'     )
    if "ln_2" in name:
        a__		=			name.replace('ln_2'							,		'layer_norm2'     )
    if "c_fc" in name:
        a__		=			name.replace('c_fc'							,		'fc1'     )
    if "c_proj" in name:
        a__		=			name.replace('c_proj'							,		'fc2'     )
    if "text_encoder" in name:
        a__		=			name.replace('text_encoder'							,		'text_model'     )
    if "ln_final" in name:
        a__		=			name.replace('ln_final'							,		'final_layer_norm'     )
    # projection layers
    if "img_projector.linear_hidden." in name:
        a__		=			name.replace('img_projector.linear_hidden.'							,		'visual_projection.'     )
    if "img_projector.linear_out." in name:
        a__		=			name.replace('img_projector.linear_out.'							,		'visual_projection.3.'     )
    if "text_projector.linear_hidden" in name:
        a__		=			name.replace('text_projector.linear_hidden'							,		'text_projection'     )
    if "text_projector.linear_out" in name:
        a__		=			name.replace('text_projector.linear_out'							,		'text_projection.3'     )

    return name



def     __magic_name__		(					UpperCamelCase     :		Any							,		UpperCamelCase     :		List[Any]     )     ->						Dict:

    for key in orig_state_dict.copy().keys():
        a__		=			orig_state_dict.pop(__UpperCAmelCase     )

        if "qkv" in key:
            # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            a__		=			key.split('.'     )
            a__	,     a__		=			int(key_split[2]     ), int(key_split[4]     )
            a__		=			config.vision_config.hidden_size
            if "weight" in key:
                a__		=			val[:dim, :]
                a__		=			val[dim : dim * 2, :]
                a__		=			val[-dim:, :]
            else:
                a__		=			val[:dim]
                a__		=			val[dim : dim * 2]
                a__		=			val[-dim:]
        elif "in_proj" in key:
            # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
            # we need to split them up into separate matrices/vectors
            a__		=			key.split('.'     )
            a__		=			int(key_split[3]     )
            a__		=			config.text_config.hidden_size
            if "weight" in key:
                a__		=			val[:dim, :]
                a__		=			val[
                    dim : dim * 2, :
                ]
                a__		=			val[-dim:, :]
            else:
                a__		=			val[:dim]
                a__		=			val[dim : dim * 2]
                a__		=			val[-dim:]
        else:
            a__		=			rename_key(__UpperCAmelCase     )
            # squeeze if necessary
            if (
                "text_projection.0" in new_name
                or "text_projection.3" in new_name
                or "visual_projection.0" in new_name
                or "visual_projection.3" in new_name
            ):
                a__		=			val.squeeze_()
            else:
                a__		=			val

    return orig_state_dict



def     __magic_name__		(					)     ->						Union[str, Any]:
    a__		=			'http://images.cocodataset.org/val2017/000000039769.jpg'
    a__		=			Image.open(requests.get(__UpperCAmelCase							,		stream=__UpperCAmelCase     ).raw     )
    return im



@torch.no_grad()
def     __magic_name__		(					UpperCamelCase     :		Optional[int]							,		UpperCamelCase     :		str							,		UpperCamelCase     :		Tuple="groupvit-gcc-yfcc"							,		UpperCamelCase     :		List[str]=False     )     ->						List[str]:
    a__		=			GroupViTConfig()
    a__		=			GroupViTModel(__UpperCAmelCase     ).eval()

    a__		=			torch.load(__UpperCAmelCase							,		map_location='cpu'     )['model']
    a__		=			convert_state_dict(__UpperCAmelCase							,		__UpperCAmelCase     )
    a__	,     a__		=			model.load_state_dict(__UpperCAmelCase							,		strict=__UpperCAmelCase     )
    assert missing_keys == ["text_model.embeddings.position_ids"]
    assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCAmelCase     ) == 0)

    # verify result
    a__		=			CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'     )
    a__		=			prepare_img()
    a__		=			processor(text=['a photo of a cat', 'a photo of a dog']							,		images=__UpperCAmelCase							,		padding=__UpperCAmelCase							,		return_tensors='pt'     )

    with torch.no_grad():
        a__		=			model(**__UpperCAmelCase     )

    if model_name == "groupvit-gcc-yfcc":
        a__		=			torch.tensor([[13.3523, 6.3629]]     )
    elif model_name == "groupvit-gcc-redcaps":
        a__		=			torch.tensor([[16.1873, 8.6230]]     )
    else:
        raise ValueError(f'Model name {model_name} not supported.'     )
    assert torch.allclose(outputs.logits_per_image							,		__UpperCAmelCase							,		atol=1E-3     )

    processor.save_pretrained(__UpperCAmelCase     )
    model.save_pretrained(__UpperCAmelCase     )
    print('Successfully saved processor and model to'							,		__UpperCAmelCase     )

    if push_to_hub:
        print('Pushing to the hub...'     )
        processor.push_to_hub(__UpperCAmelCase							,		organization='nielsr'     )
        model.push_to_hub(__UpperCAmelCase							,		organization='nielsr'     )


if __name__ == "__main__":
    a		:       Optional[int]     						=       argparse.ArgumentParser()
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
    )
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
    parser.add_argument(
        '--model_name',
        default='groupvit-gccy-fcc',
        type=str,
        help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
    )
    parser.add_argument(
        '--push_to_hub',
        action='store_true',
        help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
    )
    a		:       str     						=       parser.parse_args()

    convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''

from collections.abc import Generator


def 						__magic_name__       (      )						->   Generator[int, None, None]:

					'''simple docstring'''

					snake_case_     ,snake_case_		     =							0, 1
					while True:
										snake_case_     ,snake_case_		     =							b, a + b
										yield b


def 						__magic_name__       (      __UpperCAmelCase = 1000      )						->   int:

					'''simple docstring'''

					snake_case_		     =							1
					snake_case_		     =							fibonacci_generator()
					while len(str(next(__UpperCAmelCase      )      )      ) < n:
										answer += 1
					return answer + 1


if __name__ == "__main__":
							print(solution(int(str(input()).strip())))

