

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_									=   logging.get_logger(__name__)
def      a      (			A__	:   List[str]     )    ->      int:





      """simple docstring"""



      _lowercase	       ='huggingface/label-files'
      _lowercase	       ='imagenet-1k-id2label.json'
      _lowercase	       =json.load(open(hf_hub_download(A__      ,   A__      ,   repo_type='dataset'     )      ,   'r'     )     )
      _lowercase	       ={int(A__     ): v for k, v in idalabel.items()}
      _lowercase	       ={v: k for k, v in idalabel.items()}

      _lowercase	       ='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"
      _lowercase	       =BitConfig(
          conv_layer=A__      ,   num_labels=1000      ,   idalabel=A__      ,   labelaid=A__      ,   )

      return config
def      a      (			A__	:   List[Any]     )    ->      Any:





      """simple docstring"""



      if "stem.conv" in name:
            _lowercase	       =name.replace('stem.conv'      ,   'bit.embedder.convolution'     )
      if "blocks" in name:
            _lowercase	       =name.replace('blocks'      ,   'layers'     )
      if "head.fc" in name:
            _lowercase	       =name.replace('head.fc'      ,   'classifier.1'     )
      if name.startswith('norm'     ):
            _lowercase	       ='bit.' + name
      if "bit" not in name and "classifier" not in name:
            _lowercase	       ='bit.encoder.' + name

      return name
def      a      (			)    ->      Optional[Any]:





      """simple docstring"""



      _lowercase	       ='http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase	       =Image.open(requests.get(A__      ,   stream=A__     ).raw     )
      return im
@torch.no_grad()
def      a      (			A__	:   Union[str, Any]      ,   A__	:   str      ,   A__	:   int=False     )    ->      str:





      """simple docstring"""



      _lowercase	       =get_config(A__     )

      # load original model from timm
      _lowercase	       =create_model(A__      ,   pretrained=A__     )
      timm_model.eval()

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

      # load HuggingFace model
      _lowercase	       =BitForImageClassification(A__     )
      model.eval()
      model.load_state_dict(A__     )

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

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

      _lowercase	       =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()      ,   )

      _lowercase	       =prepare_img()
      _lowercase	       =transform(A__     ).unsqueeze(0     )
      _lowercase	       =processor(A__      ,   return_tensors='pt'     ).pixel_values

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

      # verify logits
      with torch.no_grad():
            _lowercase	       =model(A__     )
            _lowercase	       =outputs.logits

      print('Logits:'      ,   logits[0, :3]     )
      print('Predicted class:'      ,   model.config.idalabel[logits.argmax(-1     ).item()]     )
      _lowercase	       =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__":
  lowercase_									=   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_									=   parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

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


