


"""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()
lowercase_			     =  logging.get_logger(__name__)

def        lowercase							(		lowerCAmelCase__ :     str			) ->      Optional[Any]:
		__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=1000    ,					idalabel=lowerCAmelCase__    ,					labelaid=lowerCAmelCase__    ,					)

		return config

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

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     Any    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     List[Any]=False			) ->      Dict:
		__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__":
				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)










import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCamelCase											=       logging.getLogger()




@unittest.skip('Temporarily disable the doc tests.'     )
@require_torch
@require_tf
@slow
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Path			,   lowercase_						:     Union[str, None] = None			,   lowercase_						:     Union[List[str], None] = None			,   lowercase_						:     Union[str, List[str], None] = None			,   lowercase_						:     bool = True			,   )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[file for file in os.listdir(lowercase_ ) if os.path.isfile(os.path.join(lowercase_			,   lowercase_ ) )]

             if identifier is not None:
                    _lowerCamelCase       :		Any		=[file for file in files if identifier in file]

             if n_identifier is not None:
                    if isinstance(lowercase_			,   lowercase_ ):
                           for n_ in n_identifier:
                                  _lowerCamelCase       :		Any		=[file for file in files if n_ not in file]
                    else:
                           _lowerCamelCase       :		List[Any]		=[file for file in files if n_identifier not in file]

             _lowerCamelCase       :		List[Any]		=ignore_files or []
             ignore_files.append('__init__.py' )
             _lowerCamelCase       :		List[Any]		=[file for file in files if file not in ignore_files]

             for file in files:
                    # Open all files
                    print('Testing'			,   lowercase_ )

                    if only_modules:
                           _lowerCamelCase       :		Optional[int]		=file.split('.' )[0]
                           try:
                                  _lowerCamelCase       :		Any		=getattr(lowercase_			,   lowercase_ )
                                  _lowerCamelCase       :		Optional[int]		=doctest.DocTestSuite(lowercase_ )
                                  _lowerCamelCase       :		List[Any]		=unittest.TextTestRunner().run(lowercase_ )
                                  self.assertIs(len(result.failures )			,   0 )
                           except AttributeError:
                                  logger.info(F'''{module_identifier} is not a module.''' )
                    else:
                           _lowerCamelCase       :		int		=doctest.testfile(str('..' / directory / file )			,   optionflags=doctest.ELLIPSIS )
                           self.assertIs(result.failed			,   0 )






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		int		=Path('src/transformers' )
             _lowerCamelCase       :		Optional[Any]		='modeling'
             _lowerCamelCase       :		List[str]		=[
                 'modeling_ctrl.py',
                 'modeling_tf_ctrl.py',
             ]
             self.analyze_directory(lowercase_			,   identifier=lowercase_			,   ignore_files=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=Path('src/transformers' )
             _lowerCamelCase       :		Union[str, Any]		='tokenization'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('src/transformers' )
             _lowerCamelCase       :		Any		='configuration'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=Path('src/transformers' )
             _lowerCamelCase       :		int		=['configuration', 'modeling', 'tokenization']
             self.analyze_directory(lowercase_			,   n_identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('docs/source' )
             _lowerCamelCase       :		Dict		=['favicon.ico']
             self.analyze_directory(lowercase_			,   ignore_files=lowercase_			,   only_modules=lowercase_ )


