

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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Tuple:






    '''simple docstring'''
    # Initialise PyTorch model
    __UpperCAmelCase			   =     json.load(open(SCREAMING_SNAKE_CASE    ,				'''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(SCREAMING_SNAKE_CASE )

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

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


if __name__ == "__main__":
       A_						:     Any			  =				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.'
       )
       A_						:     List[str]			  =				parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







def  __a    (				SCREAMING_SNAKE_CASE ) ->       float:






    '''simple docstring'''
    return -num if num < 0 else num


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_min() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs_val(SCREAMING_SNAKE_CASE ) < abs_val(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max() arg is an empty sequence''' )
    __UpperCAmelCase			   =     x[0]
    for i in x:
        if abs(SCREAMING_SNAKE_CASE ) > abs(SCREAMING_SNAKE_CASE ):
            __UpperCAmelCase			   =     i
    return j


def  __a    (				SCREAMING_SNAKE_CASE ) ->       int:






    '''simple docstring'''
    if len(SCREAMING_SNAKE_CASE ) == 0:
        raise ValueError('''abs_max_sort() arg is an empty sequence''' )
    return sorted(SCREAMING_SNAKE_CASE    ,				key=SCREAMING_SNAKE_CASE )[-1]


def  __a    (				) ->       Union[str, Any]:






    '''simple docstring'''
    assert abs_val(0 ) == 0
    assert abs_val(3_4 ) == 3_4
    assert abs_val(-1_0_0_0_0_0_0_0_0_0_0_0 ) == 1_0_0_0_0_0_0_0_0_0_0_0

    __UpperCAmelCase			   =     [-3, -1, 2, -1_1]
    assert abs_max(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_max_sort(SCREAMING_SNAKE_CASE ) == -1_1
    assert abs_min(SCREAMING_SNAKE_CASE ) == -1


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       test_abs_val()
       print(abs_val(-34))  # --> 34




