



'''simple docstring'''





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


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



    """simple docstring"""





    print(f"""Converting {name}..."""      )

    with torch.no_grad():
        if hidden_sizes == 128:
            if name[-1] == "S":
                lowerCAmelCase__			:  Optional[Any]											= timm.create_model("""levit_128s"""							,   pretrained=UpperCamelCase      )
            else:
                lowerCAmelCase__			:  Union[str, Any]											= timm.create_model("""levit_128"""							,   pretrained=UpperCamelCase      )
        if hidden_sizes == 192:
            lowerCAmelCase__			:  List[Any]											= timm.create_model("""levit_192"""							,   pretrained=UpperCamelCase      )
        if hidden_sizes == 256:
            lowerCAmelCase__			:  List[str]											= timm.create_model("""levit_256"""							,   pretrained=UpperCamelCase      )
        if hidden_sizes == 384:
            lowerCAmelCase__			:  Any											= timm.create_model("""levit_384"""							,   pretrained=UpperCamelCase      )

        from_model.eval()
        lowerCAmelCase__			:  Any											= LevitForImageClassificationWithTeacher(UpperCamelCase      ).eval()
        lowerCAmelCase__			:  List[Any]											= OrderedDict()

        lowerCAmelCase__			:  int											= from_model.state_dict()
        lowerCAmelCase__			:  str											= list(from_model.state_dict().keys()      )
        lowerCAmelCase__			:  Any											= list(our_model.state_dict().keys()      )
        print(len(UpperCamelCase      )							,   len(UpperCamelCase      )      )
        for i in range(len(UpperCamelCase      )      ):
            lowerCAmelCase__			:  Optional[int]											= weights[og_keys[i]]
        our_model.load_state_dict(UpperCamelCase      )

        lowerCAmelCase__			:  Optional[int]											= torch.randn((2, 3, 224, 224)      )
        lowerCAmelCase__			:  Dict											= from_model(UpperCamelCase      )
        lowerCAmelCase__			:  Optional[Any]											= our_model(UpperCamelCase      ).logits

    assert torch.allclose(UpperCamelCase							,   UpperCamelCase      ), "The model logits don't match the original one."

    lowerCAmelCase__			:  Optional[int]											= name
    print(UpperCamelCase      )

    if push_to_hub:
        our_model.save_pretrained(save_directory / checkpoint_name      )
        lowerCAmelCase__			:  Tuple											= LevitImageProcessor()
        image_processor.save_pretrained(save_directory / checkpoint_name      )

        print(f"""Pushed {checkpoint_name}"""      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase = None							,   UpperCamelCase = True      ):



    """simple docstring"""





    lowerCAmelCase__			:  int											= """imagenet-1k-id2label.json"""
    lowerCAmelCase__			:  List[Any]											= 1000
    lowerCAmelCase__			:  Union[str, Any]											= (1, num_labels)

    lowerCAmelCase__			:  str											= """huggingface/label-files"""
    lowerCAmelCase__			:  List[str]											= num_labels
    lowerCAmelCase__			:  str											= 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__			:  List[str]											= idalabel
    lowerCAmelCase__			:  Tuple											= {v: k for k, v in idalabel.items()}

    lowerCAmelCase__			:  str											= partial(UpperCamelCase							,   num_labels=UpperCamelCase							,   idalabel=UpperCamelCase							,   labelaid=UpperCamelCase      )

    lowerCAmelCase__			:  List[str]											= {
        """levit-128S""": 128,
        """levit-128""": 128,
        """levit-192""": 192,
        """levit-256""": 256,
        """levit-384""": 384,
    }

    lowerCAmelCase__			:  Optional[int]											= {
        """levit-128S""": ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]							,   num_attention_heads=[4, 6, 8]							,   depths=[2, 3, 4]							,   key_dim=[16, 16, 16]							,   drop_path_rate=0							,   ),
        """levit-128""": ImageNetPreTrainedConfig(
            hidden_sizes=[128, 256, 384]							,   num_attention_heads=[4, 8, 12]							,   depths=[4, 4, 4]							,   key_dim=[16, 16, 16]							,   drop_path_rate=0							,   ),
        """levit-192""": ImageNetPreTrainedConfig(
            hidden_sizes=[192, 288, 384]							,   num_attention_heads=[3, 5, 6]							,   depths=[4, 4, 4]							,   key_dim=[32, 32, 32]							,   drop_path_rate=0							,   ),
        """levit-256""": ImageNetPreTrainedConfig(
            hidden_sizes=[256, 384, 512]							,   num_attention_heads=[4, 6, 8]							,   depths=[4, 4, 4]							,   key_dim=[32, 32, 32]							,   drop_path_rate=0							,   ),
        """levit-384""": ImageNetPreTrainedConfig(
            hidden_sizes=[384, 512, 768]							,   num_attention_heads=[6, 9, 12]							,   depths=[4, 4, 4]							,   key_dim=[32, 32, 32]							,   drop_path_rate=0.1							,   ),
    }

    if model_name:
        convert_weight_and_push(
            names_to_hidden_sizes[model_name]							,   UpperCamelCase							,   names_to_config[model_name]							,   UpperCamelCase							,   UpperCamelCase      )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(names_to_hidden_sizes[model_name]							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
    return config, expected_shape


if __name__ == "__main__":
 _lowerCAmelCase										=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default=None,
     type=str,
     help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''levit-dump-folder/''',
     type=Path,
     required=False,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
 parser.add_argument(
     '''--no-push_to_hub''',
     dest='''push_to_hub''',
     action='''store_false''',
     help='''Do not push model and image processor to the hub''',
 )

 _lowerCAmelCase										=     parser.parse_args()
 _lowerCAmelCase										=     args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







'''simple docstring'''





import os
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= len(grid[0]      )
    lowerCAmelCase__			:  int											= len(UpperCamelCase      )

    lowerCAmelCase__			:  Optional[int]											= 0
    lowerCAmelCase__			:  Tuple											= 0
    lowerCAmelCase__			:  Optional[Any]											= 0

    # Check vertically, horizontally, diagonally at the same time (only works
    # for nxn grid)
    for i in range(UpperCamelCase      ):
        for j in range(n_rows - 3      ):
            lowerCAmelCase__			:  str											= grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
            lowerCAmelCase__			:  Optional[int]											= grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]

            # Left-to-right diagonal (\) product
            if i < n_columns - 3:
                lowerCAmelCase__			:  Optional[int]											= (
                    grid[i][j]
                    * grid[i + 1][j + 1]
                    * grid[i + 2][j + 2]
                    * grid[i + 3][j + 3]
                )

            # Right-to-left diagonal(/) product
            if i > 2:
                lowerCAmelCase__			:  Tuple											= (
                    grid[i][j]
                    * grid[i - 1][j + 1]
                    * grid[i - 2][j + 2]
                    * grid[i - 3][j + 3]
                )

            lowerCAmelCase__			:  Dict											= max(
                UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )
            if max_product > largest:
                lowerCAmelCase__			:  Any											= max_product

    return largest
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= []
    with open(os.path.dirname(UpperCamelCase      ) + """/grid.txt"""      ) as file:
        for line in file:
            grid.append(line.strip("""\n"""      ).split(""" """      )      )

    lowerCAmelCase__			:  Dict											= [[int(UpperCamelCase      ) for i in grid[j]] for j in range(len(UpperCamelCase      )      )]

    return largest_product(UpperCamelCase      )


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


