






"""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()
_A						=			logging.get_logger()
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_ = True      ):





		'''simple docstring'''


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

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

				from_model.eval()
				lowerCamelCase					:      List[Any]          =   LevitForImageClassificationWithTeacher(A__      ).eval()
				lowerCamelCase					:      int          =   OrderedDict()

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

				lowerCamelCase					:      str          =   torch.randn((2, 3, 224, 224)      )
				lowerCamelCase					:      Dict          =   from_model(A__      )
				lowerCamelCase					:      List[Any]          =   our_model(A__      ).logits

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

		lowerCamelCase					:      Optional[int]          =   name
		print(A__      )

		if push_to_hub:
				our_model.save_pretrained(save_directory / checkpoint_name      )
				lowerCamelCase					:      int          =   LevitImageProcessor()
				image_processor.save_pretrained(save_directory / checkpoint_name      )

				print(F"""Pushed {checkpoint_name}"""      )
def 		UpperCAmelCase (		a_,						a_ = None,						a_ = True      ):





		'''simple docstring'''


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

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

		lowerCamelCase					:      Any          =   idalabel
		lowerCamelCase					:      Tuple          =   {v: k for k, v in idalabel.items()}

		lowerCamelCase					:      int          =   partial(A__,						num_labels=A__,						idalabel=A__,						labelaid=A__      )

		lowerCamelCase					:      Dict          =   {
		    """levit-128S""": 128,
		    """levit-128""": 128,
		    """levit-192""": 192,
		    """levit-256""": 256,
		    """levit-384""": 384,
		}

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


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

					_A						=			parser.parse_args()
					_A						=			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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)

_A						=			{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'big_bird'




	def __init__(			self		,			UpperCAmelCase_=50358		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_="gelu_new"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			UpperCAmelCase_=2		,			UpperCAmelCase_=66		,			UpperCAmelCase_="block_sparse"		,			UpperCAmelCase_=True		,			UpperCAmelCase_=False		,			UpperCAmelCase_=64		,			UpperCAmelCase_=3		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			sep_token_id=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      Tuple          =   vocab_size
			lowerCamelCase					:      str          =   max_position_embeddings
			lowerCamelCase					:      str          =   hidden_size
			lowerCamelCase					:      Optional[int]          =   num_hidden_layers
			lowerCamelCase					:      List[str]          =   num_attention_heads
			lowerCamelCase					:      List[str]          =   intermediate_size
			lowerCamelCase					:      Optional[int]          =   hidden_act
			lowerCamelCase					:      Optional[int]          =   hidden_dropout_prob
			lowerCamelCase					:      Dict          =   attention_probs_dropout_prob
			lowerCamelCase					:      int          =   initializer_range
			lowerCamelCase					:      Union[str, Any]          =   type_vocab_size
			lowerCamelCase					:      Union[str, Any]          =   layer_norm_eps
			lowerCamelCase					:      int          =   use_cache

			lowerCamelCase					:      Tuple          =   rescale_embeddings
			lowerCamelCase					:      Dict          =   attention_type
			lowerCamelCase					:      Optional[int]          =   use_bias
			lowerCamelCase					:      Optional[int]          =   block_size
			lowerCamelCase					:      Tuple          =   num_random_blocks
			lowerCamelCase					:      List[Any]          =   classifier_dropout






class 	_lowercase       (    __UpperCAmelCase		):




	@property
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCamelCase					:      Tuple          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
			else:
					lowerCamelCase					:      int          =   {0: 'batch', 1: 'sequence'}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			    ] )






