


import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()






def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:


     """simple docstring"""


     __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
     __UpperCamelCase   = GPTNeoConfig(
         hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
     print(f'''Building PyTorch model from configuration: {config}'''   )
     __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )

     # Save pytorch-model
     print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
     model.save_pretrained(_lowercase   )


if __name__ == "__main__":
    __snake_case             =     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __snake_case             =     parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






from math import cos, sin






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase   = float(_lowercase   )
     return [[scaling_factor * int(x == y   ) for x in range(2   )] for y in range(2   )]






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
     return [[c, -s], [s, c]]






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
     __UpperCamelCase   = c * s
     return [[c * c, cs], [cs, s * s]]






def 		_A		(		_lowercase   )				->   list[list[float]]:


     """simple docstring"""


     __UpperCamelCase,						__UpperCamelCase   = cos(_lowercase   ), sin(_lowercase   )
     __UpperCamelCase   = c * s
     return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(f"""    {scaling(5) = }""")
print(f"""  {rotation(4_5) = }""")
print(f"""{projection(4_5) = }""")


print(f"""{reflection(4_5) = }""")


