from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class 		_lowercase						(					snake_case_       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					SMALL_MODEL_IDENTIFIER
  UpperCamelCase_					:						List[str] =					'pt'
  UpperCamelCase_					:						Union[str, Any] =					'tf'

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							str			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					AutoModel.from_pretrained(self.test_model			)
  model_pt.save_pretrained(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							Any			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					TFAutoModel.from_pretrained(self.test_model   ,      from_pt=snake_case			)
  model_tf.save_pretrained(snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						int =					'mock_framework'

  # Framework provided - return whatever the user provides
  UpperCamelCase_					:						str =					FeaturesManager.determine_framework(self.test_model   ,      snake_case			)
  self.assertEqual(snake_case   ,      snake_case			)

  # Local checkpoint and framework provided - return provided framework
  # PyTorch checkpoint
  with TemporaryDirectory() as local_pt_ckpt:
   self._setup_pt_ckpt(snake_case			)
   UpperCamelCase_					:						Tuple =					FeaturesManager.determine_framework(snake_case   ,      snake_case			)
   self.assertEqual(snake_case   ,      snake_case			)

  # TensorFlow checkpoint
  with TemporaryDirectory() as local_tf_ckpt:
   self._setup_tf_ckpt(snake_case			)
   UpperCamelCase_					:						List[str] =					FeaturesManager.determine_framework(snake_case   ,      snake_case			)
   self.assertEqual(snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						List[str]:

  """simple docstring"""





  with TemporaryDirectory() as local_pt_ckpt:
   self._setup_pt_ckpt(snake_case			)
   UpperCamelCase_					:						Any =					FeaturesManager.determine_framework(snake_case			)
   self.assertEqual(snake_case   ,      self.framework_pt			)

  # TensorFlow checkpoint
  with TemporaryDirectory() as local_tf_ckpt:
   self._setup_tf_ckpt(snake_case			)
   UpperCamelCase_					:						Union[str, Any] =					FeaturesManager.determine_framework(snake_case			)
   self.assertEqual(snake_case   ,      self.framework_tf			)

  # Invalid local checkpoint
  with TemporaryDirectory() as local_invalid_ckpt:
   with self.assertRaises(snake_case			):
    UpperCamelCase_					:						Tuple =					FeaturesManager.determine_framework(snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						str =					MagicMock(return_value=snake_case			)
  with patch('transformers.onnx.features.is_tf_available'   ,      snake_case			):
   UpperCamelCase_					:						int =					FeaturesManager.determine_framework(self.test_model			)
   self.assertEqual(snake_case   ,      self.framework_pt			)

  # PyTorch not in environment -> use TensorFlow
  UpperCamelCase_					:						int =					MagicMock(return_value=snake_case			)
  with patch('transformers.onnx.features.is_torch_available'   ,      snake_case			):
   UpperCamelCase_					:						Any =					FeaturesManager.determine_framework(self.test_model			)
   self.assertEqual(snake_case   ,      self.framework_tf			)

  # Both in environment -> use PyTorch
  UpperCamelCase_					:						Tuple =					MagicMock(return_value=snake_case			)
  UpperCamelCase_					:						Any =					MagicMock(return_value=snake_case			)
  with patch('transformers.onnx.features.is_tf_available'   ,      snake_case			), patch(
      'transformers.onnx.features.is_torch_available'   ,      snake_case			):
   UpperCamelCase_					:						Optional[int] =					FeaturesManager.determine_framework(self.test_model			)
   self.assertEqual(snake_case   ,      self.framework_pt			)

  # Both not in environment -> raise error
  UpperCamelCase_					:						Dict =					MagicMock(return_value=snake_case			)
  UpperCamelCase_					:						int =					MagicMock(return_value=snake_case			)
  with patch('transformers.onnx.features.is_tf_available'   ,      snake_case			), patch(
      'transformers.onnx.features.is_torch_available'   ,      snake_case			):
   with self.assertRaises(snake_case			):
    UpperCamelCase_					:						int =					FeaturesManager.determine_framework(self.test_model			)




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()
a_    					=       logging.get_logger(__name__)





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

 UpperCamelCase_					:						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"
 UpperCamelCase_					:						int =					BitConfig(
     conv_layer=lowerCamelCase  ,  num_labels=1000  ,  idalabel=lowerCamelCase  ,  labelaid=lowerCamelCase  ,  )

 return config





def    __lowercase     (						lowerCamelCase					:     int	):
 if "stem.conv" in name:
  UpperCamelCase_					:						str =					name.replace('stem.conv'  ,  'bit.embedder.convolution'	)
 if "blocks" in name:
  UpperCamelCase_					:						str =					name.replace('blocks'  ,  'layers'	)
 if "head.fc" in name:
  UpperCamelCase_					:						Dict =					name.replace('head.fc'  ,  'classifier.1'	)
 if name.startswith('norm'	):
  UpperCamelCase_					:						List[str] =					'bit.' + name
 if "bit" not in name and "classifier" not in name:
  UpperCamelCase_					:						int =					'bit.encoder.' + name

 return name





def    __lowercase     (						):
 UpperCamelCase_					:						Tuple =					'http://images.cocodataset.org/val2017/000000039769.jpg'
 UpperCamelCase_					:						List[Any] =					Image.open(requests.get(lowerCamelCase  ,  stream=lowerCamelCase	).raw	)
 return im





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     List[str]=False	):
 UpperCamelCase_					:						Optional[Any] =					get_config(lowerCamelCase	)

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

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

 # load HuggingFace model
 UpperCamelCase_					:						Dict =					BitForImageClassification(lowerCamelCase	)
 model.eval()
 model.load_state_dict(lowerCamelCase	)

 # create image processor
 UpperCamelCase_					:						int =					create_transform(**resolve_data_config({}  ,  model=lowerCamelCase	)	)
 UpperCamelCase_					:						List[Any] =					transform.transforms

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

 UpperCamelCase_					:						Tuple =					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()  ,  )

 UpperCamelCase_					:						Dict =					prepare_img()
 UpperCamelCase_					:						Any =					transform(lowerCamelCase	).unsqueeze(0	)
 UpperCamelCase_					:						Union[str, Any] =					processor(lowerCamelCase  ,  return_tensors='pt'	).pixel_values

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

 # verify logits
 with torch.no_grad():
  UpperCamelCase_					:						Dict =					model(lowerCamelCase	)
  UpperCamelCase_					:						Tuple =					outputs.logits

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

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



