


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    snake_case_     (  snake_case     )  ->				int:
       lowercase__:   List[Any]  			=   'huggingface/label-files'
       lowercase__:   Optional[Any]  			=   'imagenet-1k-id2label.json'
       lowercase__:   int  			=   json.load(open(hf_hub_download(A__			,							A__			,							repo_type='dataset'     )			,							'r'     )     )
       lowercase__:   List[Any]  			=   {int(A__     ): v for k, v in idalabel.items()}
       lowercase__:   Dict  			=   {v: k for k, v in idalabel.items()}

       lowercase__:   Union[str, 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"
       lowercase__:   Optional[int]  			=   BitConfig(
           conv_layer=A__			,							num_labels=10_00			,							idalabel=A__			,							labelaid=A__			,							)

       return config




def    snake_case_     (  snake_case     )  ->				Any:



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

       return name




def    snake_case_     (  )  ->				Optional[Any]:
       lowercase__:   Optional[Any]  			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
       lowercase__:   List[Any]  			=   Image.open(requests.get(A__			,							stream=A__     ).raw     )
       return im




@torch.no_grad()
def    snake_case_     (  snake_case			,							snake_case			,							snake_case=False     )  ->				str:
       lowercase__:   Optional[int]  			=   get_config(A__     )

       # load original model from timm
       lowercase__:   List[str]  			=   create_model(A__			,							pretrained=A__     )
       timm_model.eval()

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

       # load HuggingFace model
       lowercase__:   str  			=   BitForImageClassification(A__     )
       model.eval()
       model.load_state_dict(A__     )

       # create image processor
       lowercase__:   str  			=   create_transform(**resolve_data_config({}			,							model=A__     )     )
       lowercase__:   Optional[Any]  			=   transform.transforms

       lowercase__:   Optional[Any]  			=   {
           'bilinear': PILImageResampling.BILINEAR,
           'bicubic': PILImageResampling.BICUBIC,
           'nearest': PILImageResampling.NEAREST,
       }

       lowercase__:   List[Any]  			=   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__:   Tuple  			=   prepare_img()
       lowercase__:   List[Any]  			=   transform(A__     ).unsqueeze(0     )
       lowercase__:   int  			=   processor(A__			,							return_tensors='pt'     ).pixel_values

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

       # verify logits
       with torch.no_grad():
              lowercase__:   Optional[Any]  			=   model(A__     )
              lowercase__:   str  			=   outputs.logits

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


