"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__						:						int		  =			logging.get_logger(__name__)



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							'huggingface/label-files'
				UpperCAmelCase__    =							'imagenet-1k-id2label.json'
				UpperCAmelCase__    =							json.load(open(hf_hub_download(lowerCamelCase      ,    lowerCamelCase      ,    repo_type='dataset' )      ,    'r' ) )
				UpperCAmelCase__    =							{int(lowerCamelCase ): v for k, v in idalabel.items()}
				UpperCAmelCase__    =							{v: k for k, v in idalabel.items()}

				UpperCAmelCase__    =							'std_conv' if 'bit' in model_name else False

				# note that when using BiT as backbone for ViT-hybrid checkpoints,
				# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
				# config.conv_layer = "std_conv_same"
				UpperCAmelCase__    =							BitConfig(
				    conv_layer=lowerCamelCase      ,    num_labels=1_0_0_0      ,    idalabel=lowerCamelCase      ,    labelaid=lowerCamelCase      ,    )

				return config



def 						a_		(      lowerCamelCase ):
				if "stem.conv" in name:
								UpperCAmelCase__    =							name.replace('stem.conv'      ,    'bit.embedder.convolution' )
				if "blocks" in name:
								UpperCAmelCase__    =							name.replace('blocks'      ,    'layers' )
				if "head.fc" in name:
								UpperCAmelCase__    =							name.replace('head.fc'      ,    'classifier.1' )
				if name.startswith('norm' ):
								UpperCAmelCase__    =							'bit.' + name
				if "bit" not in name and "classifier" not in name:
								UpperCAmelCase__    =							'bit.encoder.' + name

				return name



def 						a_		(      ):
				UpperCAmelCase__    =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__    =							Image.open(requests.get(lowerCamelCase      ,    stream=lowerCamelCase ).raw )
				return im



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=False ):
				UpperCAmelCase__    =							get_config(lowerCamelCase )

				# load original model from timm
				UpperCAmelCase__    =							create_model(lowerCamelCase      ,    pretrained=lowerCamelCase )
				timm_model.eval()

				# load state_dict of original model
				UpperCAmelCase__    =							timm_model.state_dict()
				for key in state_dict.copy().keys():
								UpperCAmelCase__    =							state_dict.pop(lowerCamelCase )
								UpperCAmelCase__    =							val.squeeze() if 'head' in key else val

				# load HuggingFace model
				UpperCAmelCase__    =							BitForImageClassification(lowerCamelCase )
				model.eval()
				model.load_state_dict(lowerCamelCase )

				# create image processor
				UpperCAmelCase__    =							create_transform(**resolve_data_config({}      ,    model=lowerCamelCase ) )
				UpperCAmelCase__    =							transform.transforms

				UpperCAmelCase__    =							{
				    'bilinear': PILImageResampling.BILINEAR,
				    'bicubic': PILImageResampling.BICUBIC,
				    'nearest': PILImageResampling.NEAREST,
				}

				UpperCAmelCase__    =							BitImageProcessor(
				    do_resize=lowerCamelCase      ,    size={'shortest_edge': timm_transforms[0].size}      ,    resample=pillow_resamplings[timm_transforms[0].interpolation.value]      ,    do_center_crop=lowerCamelCase      ,    crop_size={'height': timm_transforms[1].size[0], 'width': timm_transforms[1].size[1]}      ,    do_normalize=lowerCamelCase      ,    image_mean=timm_transforms[-1].mean.tolist()      ,    image_std=timm_transforms[-1].std.tolist()      ,    )

				UpperCAmelCase__    =							prepare_img()
				UpperCAmelCase__    =							transform(lowerCamelCase ).unsqueeze(0 )
				UpperCAmelCase__    =							processor(lowerCamelCase      ,    return_tensors='pt' ).pixel_values

				# verify pixel values
				assert torch.allclose(lowerCamelCase      ,    lowerCamelCase )

				# verify logits
				with torch.no_grad():
								UpperCAmelCase__    =							model(lowerCamelCase )
								UpperCAmelCase__    =							outputs.logits

				print('Logits:'      ,    logits[0, :3] )
				print('Predicted class:'      ,    model.config.idalabel[logits.argmax(-1 ).item()] )
				UpperCAmelCase__    =							timm_model(lowerCamelCase )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(lowerCamelCase      ,    outputs.logits      ,    atol=1e-3 )
				print('Looks ok!' )

				if pytorch_dump_folder_path is not None:
								Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
								print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
								model.save_pretrained(lowerCamelCase )
								processor.save_pretrained(lowerCamelCase )

				if push_to_hub:
								print(f'''Pushing model {model_name} and processor to the hub''' )
								model.push_to_hub(f'''ybelkada/{model_name}''' )
								processor.push_to_hub(f'''ybelkada/{model_name}''' )


if __name__ == "__main__":
						lowerCAmelCase__						:						Optional[int]		  =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='resnetv2_50x1_bitm',
						    type=str,
						    help='Name of the BiT 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.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model to the hub.',
						)

						lowerCAmelCase__						:						Optional[Any]		  =			parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__						:						int		  =			{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Union[str, Any]		  =			[
						    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TapasForMaskedLM',
						    'TapasForQuestionAnswering',
						    'TapasForSequenceClassification',
						    'TapasModel',
						    'TapasPreTrainedModel',
						    'load_tf_weights_in_tapas',
						]
try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__						:						Optional[Any]		  =			[
						    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFTapasForMaskedLM',
						    'TFTapasForQuestionAnswering',
						    'TFTapasForSequenceClassification',
						    'TFTapasModel',
						    'TFTapasPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
						from .tokenization_tapas import TapasTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tapas import (
												    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TapasForMaskedLM,
												    TapasForQuestionAnswering,
												    TapasForSequenceClassification,
												    TapasModel,
												    TapasPreTrainedModel,
												    load_tf_weights_in_tapas,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_tapas import (
												    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFTapasForMaskedLM,
												    TFTapasForQuestionAnswering,
												    TFTapasForSequenceClassification,
												    TFTapasModel,
												    TFTapasPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase__						:						Dict		  =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



