






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





		'''simple docstring'''


		lowerCamelCase					:      Union[str, Any]          =   'huggingface/label-files'
		lowerCamelCase					:      List[Any]          =   'imagenet-1k-id2label.json'
		lowerCamelCase					:      Dict          =   json.load(open(hf_hub_download(a_,						a_,						repo_type='dataset'      ),						'r'      )      )
		lowerCamelCase					:      Optional[Any]          =   {int(a_      ): v for k, v in idalabel.items()}
		lowerCamelCase					:      List[str]          =   {v: k for k, v in idalabel.items()}

		lowerCamelCase					:      Dict          =   '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"
		lowerCamelCase					:      Tuple          =   BitConfig(
		    conv_layer=a_,						num_labels=1000,						idalabel=a_,						labelaid=a_,						)

		return config
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		if "stem.conv" in name:
				lowerCamelCase					:      str          =   name.replace('stem.conv',						'bit.embedder.convolution'      )
		if "blocks" in name:
				lowerCamelCase					:      Union[str, Any]          =   name.replace('blocks',						'layers'      )
		if "head.fc" in name:
				lowerCamelCase					:      str          =   name.replace('head.fc',						'classifier.1'      )
		if name.startswith('norm'      ):
				lowerCamelCase					:      int          =   'bit.' + name
		if "bit" not in name and "classifier" not in name:
				lowerCamelCase					:      str          =   'bit.encoder.' + name

		return name
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      str          =   Image.open(requests.get(a_,						stream=a_      ).raw      )
		return im
@torch.no_grad()
def 		UpperCAmelCase (		a_,						a_,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      List[str]          =   get_config(a_      )

		# load original model from timm
		lowerCamelCase					:      Union[str, Any]          =   create_model(a_,						pretrained=a_      )
		timm_model.eval()

		# load state_dict of original model
		lowerCamelCase					:      Optional[int]          =   timm_model.state_dict()
		for key in state_dict.copy().keys():
				lowerCamelCase					:      Union[str, Any]          =   state_dict.pop(a_      )
				lowerCamelCase					:      Any          =   val.squeeze() if 'head' in key else val

		# load HuggingFace model
		lowerCamelCase					:      List[Any]          =   BitForImageClassification(a_      )
		model.eval()
		model.load_state_dict(a_      )

		# create image processor
		lowerCamelCase					:      str          =   create_transform(**resolve_data_config({},						model=a_      )      )
		lowerCamelCase					:      Tuple          =   transform.transforms

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

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

		lowerCamelCase					:      int          =   prepare_img()
		lowerCamelCase					:      str          =   transform(a_      ).unsqueeze(0      )
		lowerCamelCase					:      Union[str, Any]          =   processor(a_,						return_tensors='pt'      ).pixel_values

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

		# verify logits
		with torch.no_grad():
				lowerCamelCase					:      Optional[int]          =   model(a_      )
				lowerCamelCase					:      Dict          =   outputs.logits

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

		if pytorch_dump_folder_path is not None:
				Path(a_      ).mkdir(exist_ok=a_      )
				print(F"""Saving model {model_name} and processor to {pytorch_dump_folder_path}"""      )
				model.save_pretrained(a_      )
				processor.save_pretrained(a_      )

		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__":
					_A						=			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.',
					)

					_A						=			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 ...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),
			    ] )






