


import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					Union[str, Any]     ,			SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					Tuple     ,			SCREAMING_SNAKE_CASE						:					Any  ):







 """simple docstring"""



 UpperCamelCase__       :     Union[str, Any] 							= BertConfig.from_pretrained(
     '''bert-large-cased'''     ,			vocab_size=_a     ,			max_position_embeddings=512     ,			is_decoder=_a     ,			add_cross_attention=_a     ,			)
 UpperCamelCase__       :     Any 							= bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 UpperCamelCase__       :     str 							= BertGenerationConfig(**_a  )
 if is_encoder:
  UpperCamelCase__       :     List[str] 							= BertGenerationEncoder(_a  )
 else:
  UpperCamelCase__       :     Any 							= BertGenerationDecoder(_a  )
 print(F"Building PyTorch model from configuration: {config}"  )

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     _a     ,			_a     ,			model_class='''bert'''     ,			is_encoder_named_decoder=_a     ,			is_encoder=_a     ,			)

 # Save pytorch-model
 print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
 model.save_pretrained(_a  )


if __name__ == "__main__":
       __UpperCamelCase							:							str    							=					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--is_encoder_named_decoder",
           action="store_true",
           help="If decoder has to be renamed to encoder in PyTorch model.",
       )
       parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
       parser.add_argument("--vocab_size", default=5_0358, type=int, help="Vocab size of model")
       __UpperCamelCase							:							List[Any]    							=					parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_hub_path,
           args.pytorch_dump_path,
           args.is_encoder_named_decoder,
           args.vocab_size,
           is_encoder=args.is_encoder,
       )







from math import cos, sin







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_     :       str  		=					float(_a    )
    return [[scaling_factor * int(x == y    ) for x in range(2    )] for y in range(2    )]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					cos(_a    ), sin(_a    )
    return [[c, -s], [s, c]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Dict  		=					c * s
    return [[c * c, cs], [cs, s * s]]







def       lowerCamelCase_  (    _a       :						float    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_     :       List[Any]  		=					cos(_a    ), sin(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					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) = }")
