





from math import cos, sin





def    _lowercase    (  _UpperCAmelCase )  ->     list[list[float]]:
				lowerCamelCase		       =float(_UpperCAmelCase )
				return [[scaling_factor * int(x == y ) for x in range(2 )] for y in range(2 )]





def    _lowercase    (  _UpperCAmelCase )  ->     list[list[float]]:
				lowerCamelCase       ,     lowerCamelCase		       =cos(_UpperCAmelCase ), sin(_UpperCAmelCase )
				return [[c, -s], [s, c]]





def    _lowercase    (  _UpperCAmelCase )  ->     list[list[float]]:
				lowerCamelCase       ,     lowerCamelCase		       =cos(_UpperCAmelCase ), sin(_UpperCAmelCase )
				lowerCamelCase		       =c * s
				return [[c * c, cs], [cs, s * s]]





def    _lowercase    (  _UpperCAmelCase )  ->     list[list[float]]:
				lowerCamelCase       ,     lowerCamelCase		       =cos(_UpperCAmelCase ), sin(_UpperCAmelCase )
				lowerCamelCase		       =c * s
				return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F"    {scaling(5) = }")
print(F"  {rotation(45) = }")
print(F"{projection(45) = }")




print(F"{reflection(45) = }")











import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'			)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_			)

     # Save pytorch-model (weights and configuration)
     print(F'Save PyTorch model to {pytorch_dump_path}'			)
     model.save_pretrained(lowerCAmelCase_			)

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
