
'''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()
lowercase     :							Optional[Any]       						=     logging.get_logger()







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__ = True		):




    '''simple docstring'''



    print(F'Converting {name}...'		)

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

        from_model.eval()
        A			:  List[Any]   =		LevitForImageClassificationWithTeacher(snake_case__		).eval()
        A			:  Optional[Any]   =		OrderedDict()

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

        A			:  Any   =		torch.randn((2, 3, 224, 224)		)
        A			:  List[Any]   =		from_model(snake_case__		)
        A			:  Tuple   =		our_model(snake_case__		).logits

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

    A			:  int   =		name
    print(snake_case__		)

    if push_to_hub:
        our_model.save_pretrained(save_directory / checkpoint_name		)
        A			:  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		):




    '''simple docstring'''



    A			:  str   =		'''imagenet-1k-id2label.json'''
    A			:  Tuple   =		1000
    A			:  List[Any]   =		(1, num_labels)

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

    A			:  Optional[int]   =		idalabel
    A			:  Union[str, Any]   =		{v: k for k, v in idalabel.items()}

    A			:  int   =		partial(snake_case__						,							num_labels=snake_case__						,							idalabel=snake_case__						,							labelaid=snake_case__		)

    A			:  Tuple   =		{
        '''levit-128S''': 128,
        '''levit-128''': 128,
        '''levit-192''': 192,
        '''levit-256''': 256,
        '''levit-384''': 384,
    }

    A			:  str   =		{
        '''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__":
      lowercase     :							List[str]       						=     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',
      )

      lowercase     :							List[str]       						=     parser.parse_args()
      lowercase     :							Path       						=     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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase     :							Any       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''timesformer'''





     def __init__(				self		,     SCREAMING_SNAKE_CASE=224		,     SCREAMING_SNAKE_CASE=16		,     SCREAMING_SNAKE_CASE=3		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=768		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=12		,     SCREAMING_SNAKE_CASE=3072		,     SCREAMING_SNAKE_CASE="gelu"		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1e-6		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="divided_space_time"		,     SCREAMING_SNAKE_CASE=0		,     **SCREAMING_SNAKE_CASE		,     )					->							Tuple:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE      )

         A			:  List[Any]   =		image_size
         A			:  List[str]   =		patch_size
         A			:  List[str]   =		num_channels
         A			:  str   =		num_frames

         A			:  Any   =		hidden_size
         A			:  Dict   =		num_hidden_layers
         A			:  Any   =		num_attention_heads
         A			:  Optional[Any]   =		intermediate_size
         A			:  Union[str, Any]   =		hidden_act
         A			:  Tuple   =		hidden_dropout_prob
         A			:  Optional[int]   =		attention_probs_dropout_prob
         A			:  Tuple   =		initializer_range
         A			:  List[Any]   =		layer_norm_eps
         A			:  Union[str, Any]   =		qkv_bias

         A			:  List[str]   =		attention_type
         A			:  Union[str, Any]   =		drop_path_rate




