
'''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 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

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

   _a				:	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"
   _a				:	List[str]			=   BitConfig(
       conv_layer=lowerCAmelCase_ ,	num_labels=1_0_0_0 ,	idalabel=lowerCAmelCase_ ,	labelaid=lowerCAmelCase_ ,	)

   return config





def 					UpperCAmelCase_		(__a      :    int ):






   """simple docstring"""

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

   return name





def 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	Optional[int]			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
   _a				:	int			=   Image.open(requests.get(lowerCAmelCase_ ,	stream=lowerCAmelCase_ ).raw )
   return im





@torch.no_grad()
def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=False ):






   """simple docstring"""

   _a				:	Union[str, Any]			=   get_config(lowerCAmelCase_ )

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

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

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

   # create image processor
   _a				:	Optional[int]			=   create_transform(**resolve_data_config({} ,	model=lowerCAmelCase_ ) )
   _a				:	Tuple			=   transform.transforms

   _a				:	Tuple			=   {
       """bilinear""": PILImageResampling.BILINEAR,
       """bicubic""": PILImageResampling.BICUBIC,
       """nearest""": PILImageResampling.NEAREST,
   }

   _a				:	List[Any]			=   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() ,	)

   _a				:	List[Any]			=   prepare_img()
   _a				:	Any			=   transform(lowerCAmelCase_ ).unsqueeze(0 )
   _a				:	str			=   processor(lowerCAmelCase_ ,	return_tensors='pt' ).pixel_values

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

   # verify logits
   with torch.no_grad():
      _a				:	Optional[Any]			=   model(lowerCAmelCase_ )
      _a				:	List[Any]			=   outputs.logits

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








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A_							: Optional[Any]						 =	{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: List[str]						 =	["""LlamaTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Dict						 =	["""LlamaTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: Optional[int]						 =	[
       """LlamaForCausalLM""",
       """LlamaModel""",
       """LlamaPreTrainedModel""",
       """LlamaForSequenceClassification""",
   ]


if TYPE_CHECKING:
   from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama import LlamaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_llama_fast import LlamaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
   import sys

   A_							: Any						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



