



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

   SCREAMING_SNAKE_CASE_: List[str]     			="""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"
   SCREAMING_SNAKE_CASE_: Optional[int]     			=BitConfig(
       conv_layer=lowercase							,			num_labels=1000							,			idalabel=lowercase							,			labelaid=lowercase							,			)

   return config





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

   return name





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			="""http://images.cocodataset.org/val2017/000000039769.jpg"""
   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open(requests.get(lowercase							,			stream=lowercase ).raw )
   return im





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=get_config(lowercase )

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

   # load state_dict of original model
   SCREAMING_SNAKE_CASE_: int     			=timm_model.state_dict()
   for key in state_dict.copy().keys():
      SCREAMING_SNAKE_CASE_: Tuple     			=state_dict.pop(lowercase )
      SCREAMING_SNAKE_CASE_: Any     			=val.squeeze() if """head""" in key else val

   # load HuggingFace model
   SCREAMING_SNAKE_CASE_: int     			=BitForImageClassification(lowercase )
   model.eval()
   model.load_state_dict(lowercase )

   # create image processor
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=create_transform(**resolve_data_config({}							,			model=lowercase ) )
   SCREAMING_SNAKE_CASE_: Tuple     			=transform.transforms

   SCREAMING_SNAKE_CASE_: List[str]     			={
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

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

   SCREAMING_SNAKE_CASE_: Optional[int]     			=prepare_img()
   SCREAMING_SNAKE_CASE_: List[str]     			=transform(lowercase ).unsqueeze(0 )
   SCREAMING_SNAKE_CASE_: List[Any]     			=processor(lowercase							,			return_tensors="""pt""" ).pixel_values

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

   # verify logits
   with torch.no_grad():
      SCREAMING_SNAKE_CASE_: str     			=model(lowercase )
      SCREAMING_SNAKE_CASE_: List[str]     			=outputs.logits

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

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

   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 ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_UpperCAmelCase     =			{
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					'albert'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=3_0000       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					List[str]=4096       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Dict=1_6384       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					str="gelu_new"       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=512       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					List[Any]=0.0_2       ,		lowerCAmelCase :					Union[str, Any]=1E-12       ,		lowerCAmelCase :					Tuple=0.1       ,		lowerCAmelCase :					List[Any]="absolute"       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					Optional[int]=3       ,		**lowerCAmelCase :					int       ,		)     ->					Tuple:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: List[str]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=embedding_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=inner_group_num
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_act
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Any     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=max_position_embeddings
     SCREAMING_SNAKE_CASE_: Any     			=type_vocab_size
     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Dict     			=classifier_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=position_embedding_type







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




     if self.task == "multiple-choice":
        SCREAMING_SNAKE_CASE_: str     			={0: """batch""", 1: """choice""", 2: """sequence"""}
     else:
        SCREAMING_SNAKE_CASE_: Dict     			={0: """batch""", 1: """sequence"""}
     return OrderedDict(
         [
             ("""input_ids""", dynamic_axis),
             ("""attention_mask""", dynamic_axis),
             ("""token_type_ids""", dynamic_axis),
         ]		)



