"""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()
UpperCAmelCase       =  logging.get_logger(__name__)






def      lowercase		(						a__	:		str     )						->   Optional[Any]:
					_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=1000 ,  idalabel=lowerCAmelCase__ ,  labelaid=lowerCAmelCase__ ,  )

					return config






def      lowercase		(						a__	:		Optional[Any]     )						->   List[Any]:
					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      lowercase		(						)						->   List[Any]:
					_UpperCamelCase      						=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase      						=	Image.open(requests.get(lowerCAmelCase__ ,  stream=lowerCAmelCase__     ).raw     )
					return im






@torch.no_grad()
def      lowercase		(						a__	:		Any ,  a__	:		int ,  a__	:		List[Any]=False     )						->   Dict:
					_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__":
		UpperCAmelCase       =  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.""",
		)

		UpperCAmelCase       =  parser.parse_args()
		convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[str]										=				'beit'

		def __init__(  self							, _a=8_192							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.0							, _a=0.0							, _a=0.02							, _a=1E-12							, _a=224							, _a=16							, _a=3							, _a=False							, _a=False							, _a=False							, _a=False							, _a=0.1							, _a=0.1							, _a=True							, _a=[3, 5, 7, 11]							, _a=[1, 2, 3, 6]							, _a=True							, _a=0.4							, _a=256							, _a=1							, _a=False							, _a=255							, **_a							, ):
				super().__init__(**_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			intermediate_size
				__a           =			hidden_act
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			initializer_range
				__a           =			layer_norm_eps

				__a           =			image_size
				__a           =			patch_size
				__a           =			num_channels
				__a           =			use_mask_token
				__a           =			use_absolute_position_embeddings
				__a           =			use_relative_position_bias
				__a           =			use_shared_relative_position_bias
				__a           =			layer_scale_init_value
				__a           =			drop_path_rate
				__a           =			use_mean_pooling
				# decode head attributes (semantic segmentation)
				__a           =			out_indices
				__a           =			pool_scales
				# auxiliary head attributes (semantic segmentation)
				__a           =			use_auxiliary_head
				__a           =			auxiliary_loss_weight
				__a           =			auxiliary_channels
				__a           =			auxiliary_num_convs
				__a           =			auxiliary_concat_input
				__a           =			semantic_loss_ignore_index







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			List[Any]										=				version.parse('1.11'       )

		@property
		def 			__UpperCAmelCase       (  self      ):
				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )





		@property
		def 			__UpperCAmelCase       (  self      ):
				return 1E-4






