"""simple docstring"""
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  )      ->int:
  # Initialise PyTorch model
  a__:	Any     				=      json.load(open(SCREAMING_SNAKE_CASE__						,	'r'  )  )
  a__:	int     				=      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}'  )
  a__:	Optional[Any]     				=      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__":
 lowercase__   			=      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.'
 )
 lowercase__   			=      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A									=		50_0000

_A			,	_A									=		os.path.split(__file__)
_A									=		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.select(range(0     ,	len(SCREAMING_SNAKE_CASE__							)     ,	2							)							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.sort('numbers'							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.shuffle()

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset							):
     __UpperCamelCase       				=dataset.train_test_split(0.1							)

@get_duration
def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   datasets.Dataset     ,	SCREAMING_SNAKE_CASE__     :   int=10							):
     for shard_id in range(SCREAMING_SNAKE_CASE__							):
          __UpperCamelCase       				=dataset.shard(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     ):
     __UpperCamelCase       				={'num examples': SPEED_TEST_N_EXAMPLES}
     __UpperCamelCase       				=(select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('generating dataset'							)
          __UpperCamelCase       				=datasets.Features({'text': datasets.Value('string'							), 'numbers': datasets.Value('float32'							)}							)
          __UpperCamelCase       				=generate_example_dataset(
              os.path.join(SCREAMING_SNAKE_CASE__     ,	'dataset.arrow'							)     ,	SCREAMING_SNAKE_CASE__     ,	num_examples=SCREAMING_SNAKE_CASE__							)
          print('Functions'							)
          for func in functions:
               print(func.__name__							)
               __UpperCamelCase       				=func(SCREAMING_SNAKE_CASE__							)

     with open(SCREAMING_SNAKE_CASE__     ,	'wb'							) as f:
          f.write(json.dumps(SCREAMING_SNAKE_CASE__							).encode('utf-8'							)							)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()





