

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()
_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE__					=					'''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"
     SCREAMING_SNAKE_CASE__					=					BitConfig(
         conv_layer=_A   ,	num_labels=10_00   ,	idalabel=_A   ,	labelaid=_A   ,	)

     return config
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     return name
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     SCREAMING_SNAKE_CASE__					=					Image.open(requests.get(_A   ,	stream=_A       ).raw       )
     return im
@torch.no_grad()
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A=False       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_config(_A       )

     # load original model from timm
     SCREAMING_SNAKE_CASE__					=					create_model(_A   ,	pretrained=_A       )
     timm_model.eval()

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

     # load HuggingFace model
     SCREAMING_SNAKE_CASE__					=					BitForImageClassification(_A       )
     model.eval()
     model.load_state_dict(_A       )

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

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

     SCREAMING_SNAKE_CASE__					=					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()   ,	)

     SCREAMING_SNAKE_CASE__					=					prepare_img()
     SCREAMING_SNAKE_CASE__					=					transform(_A       ).unsqueeze(0       )
     SCREAMING_SNAKE_CASE__					=					processor(_A   ,	return_tensors='''pt'''       ).pixel_values

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

     # verify logits
     with torch.no_grad():
          SCREAMING_SNAKE_CASE__					=					model(_A       )
          SCREAMING_SNAKE_CASE__					=					outputs.logits

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

   _SCREAMING_SNAKE_CASE  :						List[str]         =    parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


_SCREAMING_SNAKE_CASE  :						str         =    Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
_SCREAMING_SNAKE_CASE  :						str         =    sorted({word.strip().lower() for word in data.splitlines()})

_SCREAMING_SNAKE_CASE  :						List[Any]         =    collections.defaultdict(list)
for word in word_list:
       word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

       with open('''anagrams.txt''', '''w''') as file:
              file.write('''all_anagrams = \n ''')
              file.write(pprint.pformat(all_anagrams))


