
"""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()
UpperCamelCase__          =	logging.get_logger()
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case = True ):
    print(F'''Converting {name}...''' )

    with torch.no_grad():
        if hidden_sizes == 128:
            if name[-1] == "S":
                UpperCAmelCase__	:      Dict     							=					timm.create_model('levit_128s'    ,pretrained=_snake_case )
            else:
                UpperCAmelCase__	:      Optional[Any]     							=					timm.create_model('levit_128'    ,pretrained=_snake_case )
        if hidden_sizes == 192:
            UpperCAmelCase__	:      int     							=					timm.create_model('levit_192'    ,pretrained=_snake_case )
        if hidden_sizes == 256:
            UpperCAmelCase__	:      Tuple     							=					timm.create_model('levit_256'    ,pretrained=_snake_case )
        if hidden_sizes == 384:
            UpperCAmelCase__	:      int     							=					timm.create_model('levit_384'    ,pretrained=_snake_case )

        from_model.eval()
        UpperCAmelCase__	:      Any     							=					LevitForImageClassificationWithTeacher(_snake_case ).eval()
        UpperCAmelCase__	:      List[Any]     							=					OrderedDict()

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

        UpperCAmelCase__	:      Any     							=					torch.randn((2, 3, 224, 224) )
        UpperCAmelCase__	:      Optional[Any]     							=					from_model(_snake_case )
        UpperCAmelCase__	:      Tuple     							=					our_model(_snake_case ).logits

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

    UpperCAmelCase__	:      List[Any]     							=					name
    print(_snake_case )

    if push_to_hub:
        our_model.save_pretrained(save_directory / checkpoint_name )
        UpperCAmelCase__	:      Union[str, Any]     							=					LevitImageProcessor()
        image_processor.save_pretrained(save_directory / checkpoint_name )

        print(F'''Pushed {checkpoint_name}''' )
def 					lowerCamelCase   (     _snake_case    ,_snake_case = None    ,_snake_case = True ):
    UpperCAmelCase__	:      int     							=					'imagenet-1k-id2label.json'
    UpperCAmelCase__	:      List[str]     							=					1000
    UpperCAmelCase__	:      List[str]     							=					(1, num_labels)

    UpperCAmelCase__	:      Optional[int]     							=					'huggingface/label-files'
    UpperCAmelCase__	:      int     							=					num_labels
    UpperCAmelCase__	:      str     							=					json.load(open(hf_hub_download(_snake_case    ,_snake_case    ,repo_type='dataset' )    ,'r' ) )
    UpperCAmelCase__	:      List[Any]     							=					{int(_snake_case ): v for k, v in idalabel.items()}

    UpperCAmelCase__	:      Dict     							=					idalabel
    UpperCAmelCase__	:      int     							=					{v: k for k, v in idalabel.items()}

    UpperCAmelCase__	:      List[str]     							=					partial(_snake_case    ,num_labels=_snake_case    ,idalabel=_snake_case    ,labelaid=_snake_case )

    UpperCAmelCase__	:      Optional[Any]     							=					{
        'levit-128S': 128,
        'levit-128': 128,
        'levit-192': 192,
        'levit-256': 256,
        'levit-384': 384,
    }

    UpperCAmelCase__	:      Union[str, Any]     							=					{
        '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]    ,_snake_case    ,names_to_config[model_name]    ,_snake_case    ,_snake_case )
    else:
        for model_name, config in names_to_config.items():
            convert_weight_and_push(names_to_hidden_sizes[model_name]    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case )
    return config, expected_shape


if __name__ == "__main__":
    UpperCamelCase__          =	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',
    )

    UpperCamelCase__          =	parser.parse_args()
    UpperCamelCase__          =	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'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE									=				{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _SCREAMING_SNAKE_CASE									=				[
           "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MCTCTForCTC",
           "MCTCTModel",
           "MCTCTPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       _SCREAMING_SNAKE_CASE									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


