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()
lowercase__			:				Union[str, Any]		=	logging.get_logger(__name__)

def    SCREAMING_SNAKE_CASE_     (						snake_case__							)     ->	Optional[Any]:
							lowerCAmelCase			=		'huggingface/label-files'
							lowerCAmelCase			=		'imagenet-1k-id2label.json'
							lowerCAmelCase			=		json.load(open(hf_hub_download(lowerCAmelCase_   ,  lowerCAmelCase_   ,  repo_type='''dataset'''							)   ,  '''r'''							)							)
							lowerCAmelCase			=		{int(lowerCAmelCase_							): v for k, v in idalabel.items()}
							lowerCAmelCase			=		{v: k for k, v in idalabel.items()}

							lowerCAmelCase			=		'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			=		BitConfig(
							    conv_layer=lowerCAmelCase_   ,  num_labels=1_0_0_0   ,  idalabel=lowerCAmelCase_   ,  labelaid=lowerCAmelCase_   ,  )

							return config

def    SCREAMING_SNAKE_CASE_     (						snake_case__							)     ->	Optional[Any]:
							if "stem.conv" in name:
														lowerCAmelCase			=		name.replace('''stem.conv'''   ,  '''bit.embedder.convolution'''							)
							if "blocks" in name:
														lowerCAmelCase			=		name.replace('''blocks'''   ,  '''layers'''							)
							if "head.fc" in name:
														lowerCAmelCase			=		name.replace('''head.fc'''   ,  '''classifier.1'''							)
							if name.startswith('''norm'''							):
														lowerCAmelCase			=		'bit.' + name
							if "bit" not in name and "classifier" not in name:
														lowerCAmelCase			=		'bit.encoder.' + name

							return name

def    SCREAMING_SNAKE_CASE_     (						)     ->	str:
							lowerCAmelCase			=		'http://images.cocodataset.org/val2017/000000039769.jpg'
							lowerCAmelCase			=		Image.open(requests.get(lowerCAmelCase_   ,  stream=lowerCAmelCase_							).raw							)
							return im

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_     (						snake_case__   ,  snake_case__   ,  snake_case__=False							)     ->	Optional[int]:
							lowerCAmelCase			=		get_config(lowerCAmelCase_							)

							# load original model from timm
							lowerCAmelCase			=		create_model(lowerCAmelCase_   ,  pretrained=lowerCAmelCase_							)
							timm_model.eval()

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

							# load HuggingFace model
							lowerCAmelCase			=		BitForImageClassification(lowerCAmelCase_							)
							model.eval()
							model.load_state_dict(lowerCAmelCase_							)

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

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

							lowerCAmelCase			=		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()   ,  )

							lowerCAmelCase			=		prepare_img()
							lowerCAmelCase			=		transform(lowerCAmelCase_							).unsqueeze(0							)
							lowerCAmelCase			=		processor(lowerCAmelCase_   ,  return_tensors='''pt'''							).pixel_values

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

							# verify logits
							with torch.no_grad():
														lowerCAmelCase			=		model(lowerCAmelCase_							)
														lowerCAmelCase			=		outputs.logits

							print('''Logits:'''   ,  logits[0, :3]							)
							print('''Predicted class:'''   ,  model.config.idalabel[logits.argmax(-1							).item()]							)
							lowerCAmelCase			=		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__":
	lowercase__			:				List[str]		=	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.''',
	)

	lowercase__			:				List[str]		=	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									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''SwinForImageClassification''',
							    '''SwinForMaskedImageModeling''',
							    '''SwinModel''',
							    '''SwinPreTrainedModel''',
							    '''SwinBackbone''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFSwinForImageClassification''',
							    '''TFSwinForMaskedImageModeling''',
							    '''TFSwinModel''',
							    '''TFSwinPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_swin import (
														    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SwinBackbone,
														    SwinForImageClassification,
														    SwinForMaskedImageModeling,
														    SwinModel,
														    SwinPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_swin import (
														    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSwinForImageClassification,
														    TFSwinForMaskedImageModeling,
														    TFSwinModel,
														    TFSwinPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




