

'''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      					=     logging.get_logger(__name__)


def  a__       (    _SCREAMING_SNAKE_CASE   :       Dict				)     ->					Optional[int]:


	"""simple docstring"""





	UpperCAmelCase_     :      Optional[int]						=  "huggingface/label-files"
	UpperCAmelCase_     :      List[Any]						=  "imagenet-1k-id2label.json"
	UpperCAmelCase_     :      Optional[int]						=  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,				_SCREAMING_SNAKE_CASE	,				repo_type="dataset"				)	,				"r"				)				)
	UpperCAmelCase_     :      Any						=  {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
	UpperCAmelCase_     :      List[Any]						=  {v: k for k, v in idalabel.items()}

	UpperCAmelCase_     :      Optional[Any]						=  "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_     :      int						=  BitConfig(
	    conv_layer=_SCREAMING_SNAKE_CASE	,				num_labels=10_00	,				idalabel=_SCREAMING_SNAKE_CASE	,				labelaid=_SCREAMING_SNAKE_CASE	,				)

	return config


def  a__       (    _SCREAMING_SNAKE_CASE   :       Any				)     ->					int:


	"""simple docstring"""





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

	return name


def  a__       (    )     ->					List[Any]:


	"""simple docstring"""





	UpperCAmelCase_     :      Tuple						=  "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase_     :      Dict						=  Image.open(requests.get(_SCREAMING_SNAKE_CASE	,				stream=_SCREAMING_SNAKE_CASE				).raw				)
	return im


@torch.no_grad()
def  a__       (    _SCREAMING_SNAKE_CASE   :       Any	,				_SCREAMING_SNAKE_CASE   :       Optional[Any]	,				_SCREAMING_SNAKE_CASE   :       Optional[int]=False				)     ->					Tuple:


	"""simple docstring"""





	UpperCAmelCase_     :      List[str]						=  get_config(_SCREAMING_SNAKE_CASE				)

	# load original model from timm
	UpperCAmelCase_     :      str						=  create_model(_SCREAMING_SNAKE_CASE	,				pretrained=_SCREAMING_SNAKE_CASE				)
	timm_model.eval()

	# load state_dict of original model
	UpperCAmelCase_     :      Dict						=  timm_model.state_dict()
	for key in state_dict.copy().keys():
		UpperCAmelCase_     :      Dict						=  state_dict.pop(_SCREAMING_SNAKE_CASE				)
		UpperCAmelCase_     :      List[str]						=  val.squeeze() if "head" in key else val

	# load HuggingFace model
	UpperCAmelCase_     :      List[str]						=  BitForImageClassification(_SCREAMING_SNAKE_CASE				)
	model.eval()
	model.load_state_dict(_SCREAMING_SNAKE_CASE				)

	# create image processor
	UpperCAmelCase_     :      str						=  create_transform(**resolve_data_config({}	,				model=_SCREAMING_SNAKE_CASE				)				)
	UpperCAmelCase_     :      int						=  transform.transforms

	UpperCAmelCase_     :      Any						=  {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

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

	UpperCAmelCase_     :      List[Any]						=  prepare_img()
	UpperCAmelCase_     :      Tuple						=  transform(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)
	UpperCAmelCase_     :      List[str]						=  processor(_SCREAMING_SNAKE_CASE	,				return_tensors="pt"				).pixel_values

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

	# verify logits
	with torch.no_grad():
		UpperCAmelCase_     :      Dict						=  model(_SCREAMING_SNAKE_CASE				)
		UpperCAmelCase_     :      int						=  outputs.logits

	print("Logits:"	,				logits[0, :3]				)
	print("Predicted class:"	,				model.config.idalabel[logits.argmax(-1				).item()]				)
	UpperCAmelCase_     :      List[Any]						=  timm_model(_SCREAMING_SNAKE_CASE				)
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(_SCREAMING_SNAKE_CASE	,				outputs.logits	,				atol=1E-3				)
	print("Looks ok!"				)

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

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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		str					='''mra'''





       def __init__(				self	:Any,      snake_case	:List[str]=5_0265,      snake_case	:List[Any]=768,      snake_case	:Optional[Any]=12,      snake_case	:Optional[Any]=12,      snake_case	:str=3072,      snake_case	:Tuple="gelu",      snake_case	:Optional[int]=0.1,      snake_case	:int=0.1,      snake_case	:Any=512,      snake_case	:Union[str, Any]=1,      snake_case	:Union[str, Any]=0.0_2,      snake_case	:List[Any]=1e-5,      snake_case	:Optional[int]="absolute",      snake_case	:Optional[int]=4,      snake_case	:str="full",      snake_case	:Optional[int]=0,      snake_case	:List[Any]=0,      snake_case	:int=1,      snake_case	:List[Any]=0,      snake_case	:Dict=2,      **snake_case	:Dict,      ):



          """simple docstring"""

          super().__init__(pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      **snake_case)

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =hidden_size
          _lowercase              =num_hidden_layers
          _lowercase              =num_attention_heads
          _lowercase              =intermediate_size
          _lowercase              =hidden_act
          _lowercase              =hidden_dropout_prob
          _lowercase              =attention_probs_dropout_prob
          _lowercase              =initializer_range
          _lowercase              =type_vocab_size
          _lowercase              =layer_norm_eps
          _lowercase              =position_embedding_type
          _lowercase              =block_per_row
          _lowercase              =approx_mode
          _lowercase              =initial_prior_first_n_blocks
          _lowercase              =initial_prior_diagonal_n_blocks


