



'''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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase      						=	logging.get_logger(__name__)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=False			):



    """simple docstring"""





    lowerCAmelCase__ :  List[str]							      =					[]
    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""")			)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ("""cls_token""", """vit.embeddings.cls_token"""),
            ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
            ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
            ("""pos_embed""", """vit.embeddings.position_embeddings"""),
        ]			)

    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__ :  Tuple							      =					[(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"""),
            ]			)

    return rename_keys
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase=False			):



    """simple docstring"""





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



    """simple docstring"""





    lowerCAmelCase__ :  Optional[int]							      =					["""head.weight""", """head.bias"""]
    for k in ignore_keys:
        state_dict.pop(UpperCamelCase							,    UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					dct.pop(UpperCamelCase			)
    lowerCAmelCase__ :  int							      =					val
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
    lowerCAmelCase__ :  List[Any]							      =					Image.open(requests.get(UpperCamelCase							,    stream=UpperCamelCase			).raw			)
    return im
@torch.no_grad()
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase			):



    """simple docstring"""





    lowerCAmelCase__ :  Union[str, Any]							      =					ViTConfig()
    lowerCAmelCase__ :  Any							      =					False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        lowerCAmelCase__ :  int							      =					True
        lowerCAmelCase__ :  Optional[int]							      =					int(vit_name[-12:-10]			)
        lowerCAmelCase__ :  Dict							      =					int(vit_name[-9:-6]			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					1000
        lowerCAmelCase__ :  Any							      =					"""huggingface/label-files"""
        lowerCAmelCase__ :  List[Any]							      =					"""imagenet-1k-id2label.json"""
        lowerCAmelCase__ :  Any							      =					json.load(open(hf_hub_download(UpperCamelCase							,    UpperCamelCase							,    repo_type="""dataset"""			)							,    """r"""			)			)
        lowerCAmelCase__ :  Optional[int]							      =					{int(UpperCamelCase			): v for k, v in idalabel.items()}
        lowerCAmelCase__ :  int							      =					idalabel
        lowerCAmelCase__ :  Optional[Any]							      =					{v: k for k, v in idalabel.items()}
        lowerCAmelCase__ :  Optional[Any]							      =					int(vit_name[-6:-4]			)
        lowerCAmelCase__ :  Union[str, Any]							      =					int(vit_name[-3:]			)
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("""tiny"""			):
            lowerCAmelCase__ :  List[Any]							      =					192
            lowerCAmelCase__ :  Tuple							      =					768
            lowerCAmelCase__ :  List[Any]							      =					12
            lowerCAmelCase__ :  str							      =					3
        elif vit_name[9:].startswith("""small"""			):
            lowerCAmelCase__ :  List[str]							      =					384
            lowerCAmelCase__ :  List[Any]							      =					1536
            lowerCAmelCase__ :  Any							      =					12
            lowerCAmelCase__ :  List[str]							      =					6
        else:
            pass
    else:
        if vit_name[4:].startswith("""small"""			):
            lowerCAmelCase__ :  Optional[Any]							      =					768
            lowerCAmelCase__ :  Any							      =					2304
            lowerCAmelCase__ :  Tuple							      =					8
            lowerCAmelCase__ :  str							      =					8
        elif vit_name[4:].startswith("""base"""			):
            pass
        elif vit_name[4:].startswith("""large"""			):
            lowerCAmelCase__ :  Any							      =					1024
            lowerCAmelCase__ :  List[str]							      =					4096
            lowerCAmelCase__ :  Optional[Any]							      =					24
            lowerCAmelCase__ :  Optional[int]							      =					16
        elif vit_name[4:].startswith("""huge"""			):
            lowerCAmelCase__ :  List[str]							      =					1280
            lowerCAmelCase__ :  int							      =					5120
            lowerCAmelCase__ :  Optional[int]							      =					32
            lowerCAmelCase__ :  List[Any]							      =					16

    # load original model from timm
    lowerCAmelCase__ :  Optional[Any]							      =					timm.create_model(UpperCamelCase							,    pretrained=UpperCamelCase			)
    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_(UpperCamelCase			)
    lowerCAmelCase__ :  str							      =					create_rename_keys(UpperCamelCase							,    UpperCamelCase			)
    for src, dest in rename_keys:
        rename_key(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)
    read_in_q_k_v(UpperCamelCase							,    UpperCamelCase							,    UpperCamelCase			)

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        lowerCAmelCase__ :  Union[str, Any]							      =					ViTModel(UpperCamelCase			).eval()
    else:
        lowerCAmelCase__ :  Any							      =					ViTForImageClassification(UpperCamelCase			).eval()
    model.load_state_dict(UpperCamelCase			)

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        lowerCAmelCase__ :  int							      =					DeiTImageProcessor(size=config.image_size			)
    else:
        lowerCAmelCase__ :  Union[str, Any]							      =					ViTImageProcessor(size=config.image_size			)
    lowerCAmelCase__ :  Optional[Any]							      =					image_processor(images=prepare_img()							,    return_tensors="""pt"""			)
    lowerCAmelCase__ :  Tuple							      =					encoding["""pixel_values"""]
    lowerCAmelCase__ :  str							      =					model(UpperCamelCase			)

    if base_model:
        lowerCAmelCase__ :  int							      =					timm_model.forward_features(UpperCamelCase			)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(UpperCamelCase							,    outputs.pooler_output							,    atol=1e-3			)
    else:
        lowerCAmelCase__ :  Optional[Any]							      =					timm_model(UpperCamelCase			)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(UpperCamelCase							,    outputs.logits							,    atol=1e-3			)

    Path(UpperCamelCase			).mkdir(exist_ok=UpperCamelCase			)
    print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""			)
    model.save_pretrained(UpperCamelCase			)
    print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
    image_processor.save_pretrained(UpperCamelCase			)


if __name__ == "__main__":
 _lowerCAmelCase      						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--vit_name''',
     default='''vit_base_patch16_224''',
     type=str,
     help='''Name of the 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.'''
 )

 _lowerCAmelCase      						=	parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)








"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					list    ):
  """simple docstring"""

  if len(snake_case__    ) <= 1:
    return [tuple(snake_case__    )]

  _snake_case							:		List[Any]								=  []

  def generate(snake_case__							:					int		,   snake_case__							:					list    ):
    if k == 1:
      res.append(tuple(arr[:]    )    )
      return

    generate(k - 1		,   snake_case__    )

    for i in range(k - 1    ):
      if k % 2 == 0:  # k is even
        _snake_case		,					_snake_case							:		Optional[Any]								=  arr[k - 1], arr[i]
      else:  # k is odd
        _snake_case		,					_snake_case							:		List[str]								=  arr[k - 1], arr[0]
      generate(k - 1		,   snake_case__    )

  generate(len(snake_case__    )		,   snake_case__    )
  return res


if __name__ == "__main__":
     A_						=				input('''Enter numbers separated by a comma:\n''').strip()
     A_						=				[int(item) for item in user_input.split(''',''')]
     print(heaps(arr))


