


'''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()
a_   :      Any				    =      logging.get_logger(__name__)






def 		_A			(lowerCAmelCase__					:Optional[Any]						)				->					Optional[Any]:




     '''simple docstring'''



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

     _a      =							'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      =							BitConfig(
         conv_layer=lowerCAmelCase__			,  num_labels=10_00			,  idalabel=lowerCAmelCase__			,  labelaid=lowerCAmelCase__			,  )

     return config






def 		_A			(lowerCAmelCase__					:str						)				->					Tuple:




     '''simple docstring'''



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

     return name






def 		_A			()				->					str:




     '''simple docstring'''



     _a      =							'http://images.cocodataset.org/val2017/000000039769.jpg'
     _a      =							Image.open(requests.get(lowerCAmelCase__			,  stream=lowerCAmelCase__						).raw						)
     return im






@torch.no_grad()
def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Union[str, Any]=False						)				->					Tuple:




     '''simple docstring'''



     _a      =							get_config(lowerCAmelCase__						)

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

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

     # load HuggingFace model
     _a      =							BitForImageClassification(lowerCAmelCase__						)
     model.eval()
     model.load_state_dict(lowerCAmelCase__						)

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

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

     _a      =							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      =							prepare_img()
     _a      =							transform(lowerCAmelCase__						).unsqueeze(0						)
     _a      =							processor(lowerCAmelCase__			,  return_tensors='pt'						).pixel_values

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

     # verify logits
     with torch.no_grad():
          _a      =							model(lowerCAmelCase__						)
          _a      =							outputs.logits

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





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a_   :      List[str]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  )   ->							Any:
      _a      =							feature_size
      _a      =							sampling_rate
      _a      =							padding_value

      _a      =							kwargs.pop('padding_side' ,			'right'  )
      _a      =							kwargs.pop('return_attention_mask' ,			__magic_name__  )

      super().__init__(**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							BatchFeature:
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(__magic_name__ ,			(list, tuple)  ) and isinstance(processed_features[0] ,			(dict, BatchFeature)  ):
           _a      =							{
               key: [example[key] for example in processed_features] for key in processed_features[0].keys()
           }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
           raise ValueError(
               'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
               f' to this method that includes {self.model_input_names[0]}, but you provided'
               f' {list(processed_features.keys()  )}'  )

      _a      =							processed_features[self.model_input_names[0]]
      _a      =							(
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(__magic_name__  ) == 0:
           if return_attention_mask:
                _a      =							[]
           return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      _a      =							required_input[0]
      if isinstance(__magic_name__ ,			(list, tuple)  ):
           # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
           _a      =							0
           while len(required_input[index]  ) == 0:
                index += 1
           if index < len(__magic_name__  ):
                _a      =							required_input[index][0]

      if return_tensors is None:
           if is_tf_tensor(__magic_name__  ):
                _a      =							'tf'
           elif is_torch_tensor(__magic_name__  ):
                _a      =							'pt'
           elif isinstance(__magic_name__ ,			(int, float, list, tuple, np.ndarray)  ):
                _a      =							'np'
           else:
                raise ValueError(
                    f'type of {first_element} unknown: {type(__magic_name__  )}. '
                    'Should be one of a python, numpy, pytorch or tensorflow object.'  )

      for key, value in processed_features.items():
           if isinstance(value[0] ,			(int, float)  ):
                _a      =							to_numpy(__magic_name__  )
           else:
                _a      =							[to_numpy(__magic_name__  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
      _a      =							self._get_padding_strategies(padding=__magic_name__ ,			max_length=__magic_name__  )

      _a      =							processed_features[self.model_input_names[0]]

      _a      =							len(__magic_name__  )
      if not all(len(__magic_name__  ) == batch_size for v in processed_features.values()  ):
           raise ValueError('Some items in the output dictionary have a different batch size than others.'  )

      _a      =							[]
      for i in range(__magic_name__  ):
           _a      =							{k: v[i] for k, v in processed_features.items()}
           # truncation
           _a      =							self._truncate(
               __magic_name__ ,			max_length=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			truncation=__magic_name__ ,			)
           truncated_inputs.append(__magic_name__  )

      if padding_strategy == PaddingStrategy.LONGEST:
           # make sure that `max_length` cannot be longer than the longest truncated length
           _a      =							max(len(input_slice[self.model_input_names[0]]  ) for input_slice in truncated_inputs  )
           _a      =							PaddingStrategy.MAX_LENGTH

      _a      =							{}
      for i in range(__magic_name__  ):
           # padding
           _a      =							self._pad(
               truncated_inputs[i] ,			max_length=__magic_name__ ,			padding_strategy=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=__magic_name__ ,			)

           for key, value in outputs.items():
                if key not in batch_outputs:
                     _a      =							[]
                if value.dtype is np.dtype(np.floataa  ):
                     _a      =							value.astype(np.floataa  )
                batch_outputs[key].append(__magic_name__  )

      return BatchFeature(__magic_name__ ,			tensor_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = PaddingStrategy.DO_NOT_PAD ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							dict:
      _a      =							processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
           _a      =							len(__magic_name__  )

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__  ) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
           _a      =							np.ones(len(__magic_name__  ) ,			dtype=np.intaa  )

      if needs_to_be_padded:
           _a      =							max_length - len(__magic_name__  )
           if self.padding_side == "right":
                if return_attention_mask:
                     _a      =							np.pad(
                         processed_features['attention_mask'] ,			(0, difference)  )
                _a      =							((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                _a      =							np.pad(
                    __magic_name__ ,			__magic_name__ ,			'constant' ,			constant_values=self.padding_value  )
           elif self.padding_side == "left":
                if return_attention_mask:
                     _a      =							np.pad(
                         processed_features['attention_mask'] ,			(difference, 0)  )
                _a      =							((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                _a      =							np.pad(
                    __magic_name__ ,			__magic_name__ ,			'constant' ,			constant_values=self.padding_value  )
           else:
                raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

      return processed_features
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							List[str]:
      if not truncation:
           return processed_features
      elif truncation and max_length is None:
           raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'  )

      _a      =							processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							len(__magic_name__  ) > max_length

      if needs_to_be_truncated:
           _a      =							processed_features[self.model_input_names[0]][:max_length]
           if "attention_mask" in processed_features:
                _a      =							processed_features['attention_mask'][:max_length]

      return processed_features




 def 							__UpperCAmelCase						(	self ,			__magic_name__=False ,			__magic_name__=None  )   ->							Dict:

      # Get padding strategy
      if padding is not False:
           if padding is True:
                _a      =							PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
           elif not isinstance(__magic_name__ ,			__magic_name__  ):
                _a      =							PaddingStrategy(__magic_name__  )
           elif isinstance(__magic_name__ ,			__magic_name__  ):
                _a      =							padding
      else:
           _a      =							PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
           if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    f'When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'  )

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
           raise ValueError(
               'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
               ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'  )

      return padding_strategy

