code: 
  import argparse
  import itertools
  import operator
  import sys
  from collections import OrderedDict
  
  from run_eval import datetime_now, run_generate
  
  from utils import ROUGE_KEYS
  
  
  # A table of supported tasks and the list of scores in the order of importance to be sorted by.
  # To add a new task, simply list the score names that `run_eval.run_generate()` returns
  lowercase__						=			{
      'translation': ['bleu'],
      'summarization': ROUGE_KEYS,
  }
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					List[str]     =	search.split()
  		snake_case     :					List[str]     =	dict((g.split('''=''' ) for g in groups) )
  		snake_case     :					Any     =	list(entries.keys() )
  		snake_case     :					List[Any]     =	[[F'--{k} {v}' for v in vs.split(''':''' )] for k, vs in entries.items()]
  		snake_case     :					Any     =	[list(SCREAMING_SNAKE_CASE__ ) for x in itertools.product(*SCREAMING_SNAKE_CASE__ )]
  		return matrix, entry_names
  
  
  
  
  def       _UpperCamelCase	(					)     ->       Union[str, Any]:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					int     =	sys.argv[0]
  
  		snake_case     :					str     =	argparse.ArgumentParser(
  		    usage=(
  		        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
  		        ''' refer to `run_eval.py -h` for the complete list.'''
  		    ) )
  		parser.add_argument(
  		    '''--search'''   ,					type=SCREAMING_SNAKE_CASE__   ,					required=SCREAMING_SNAKE_CASE__   ,					help='''param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\"'''   ,					)
  		parser.add_argument(
  		    '''--bs'''   ,					type=SCREAMING_SNAKE_CASE__   ,					default=8   ,					required=SCREAMING_SNAKE_CASE__   ,					help='''initial batch size (may get reduced if it\'s too big)''' )
  		parser.add_argument('''--task'''   ,					type=SCREAMING_SNAKE_CASE__   ,					help='''used for task_specific_params + metrics''' )
  		parser.add_argument(
  		    '''--info'''   ,					nargs='''?'''   ,					type=SCREAMING_SNAKE_CASE__   ,					const=datetime_now()   ,					help=(
  		        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
  		        ''' string will be used.'''
  		    )   ,					)
  		snake_case ,snake_case     :					List[Any]     =	parser.parse_known_args()
  		# we share some of the args
  		args_main.extend(['''--task''', args.task] )
  		snake_case     :					Any     =	[prog] + args_main
  
  		# to support variations like translation_en_to_de"
  		snake_case     :					Optional[Any]     =	'''translation''' if '''translation''' in args.task else '''summarization'''
  
  		snake_case ,snake_case     :					Optional[Any]     =	parse_search_arg(args.search )
  		snake_case     :					Any     =	task_score_names[task]  # score cols first
  		snake_case     :					Tuple     =	{col: len(str(SCREAMING_SNAKE_CASE__ ) ) for col in col_names}
  		snake_case     :					Tuple     =	[]
  		for r in matrix:
  				snake_case     :					str     =	dict((x.replace('''--'''   ,					'''''' ).split() for x in r) )
  				snake_case     :					Any     =	''' '''.join(SCREAMING_SNAKE_CASE__ ).split()
  				args_exp.extend(['''--bs''', str(args.bs )] )  # in case we need to reduce its size due to CUDA OOM
  				snake_case     :					Any     =	args_normal + args_exp
  
  				# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
  				snake_case     :					Dict     =	run_generate(verbose=SCREAMING_SNAKE_CASE__ )
  				# make sure scores are first in the table
  				snake_case     :					Dict     =	OrderedDict()
  				for score in task_score_names[task]:
  						snake_case     :					List[Any]     =	scores[score]
  				result.update(SCREAMING_SNAKE_CASE__ )
  				results.append(SCREAMING_SNAKE_CASE__ )
  
  				# find widest entries
  				for k, v in result.items():
  						snake_case     :					Union[str, Any]     =	len(str(SCREAMING_SNAKE_CASE__ ) )
  						if l > col_widths[k]:
  								snake_case     :					Optional[int]     =	l
  
  		snake_case     :					Union[str, Any]     =	sorted(SCREAMING_SNAKE_CASE__   ,					key=operator.itemgetter(*task_score_names[task] )   ,					reverse=SCREAMING_SNAKE_CASE__ )
  		print(''' | '''.join([F'{col:{col_widths[col]}}' for col in col_names] ) )
  		print(''' | '''.join([F'{"-"*col_widths[col]}' for col in col_names] ) )
  		for row in results_sorted:
  				print(''' | '''.join([F'{row[col]:{col_widths[col]}}' for col in col_names] ) )
  
  		snake_case     :					List[Any]     =	results_sorted[0]
  		for score in task_score_names[task]:
  				del best[score]
  		snake_case     :					List[str]     =	[F'--{k} {v}' for k, v in best.items()]
  		snake_case     :					Any     =	['''--bs''', str(args.bs )]
  		if args.info:
  				print(F'\nInfo: {args.info}' )
  		print('''\nBest score args:''' )
  		print(''' '''.join(args_main + best_args + dyn_args ) )
  
  		return results_sorted
  
  
  if __name__ == "__main__":
  						# Usage:
  						# [normal-run_eval_search.py cmd plus] \
  						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  						#
  						# Example:
  						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
  						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
  						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
  						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  						run_search()
  
  
  
style_context: 
  '''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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	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}' )
  		snake_case     :					Union[str, 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)
  
  
  