code: 
  '''simple docstring'''
  import logging
  from pathlib import Path
  
  import numpy as np
  import pytorch_lightning as pl
  import torch
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
  from pytorch_lightning.utilities import rank_zero_only
  
  from utils import save_json
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       str:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					int     =	filter(lambda SCREAMING_SNAKE_CASE__ : p.requires_grad   ,					model.parameters() )
  		snake_case     :					Tuple     =	sum([np.prod(p.size() ) for p in model_parameters] )
  		return params
  
  
  lowercase__						=			logging.getLogger(__name__)
  
  
  
  
  
  
  class   snake_case__					(			pl.Callback				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Any			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	{f'lr_group_{i}': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups			)}
  				pl_module.logger.log_metrics(UpperCamelCase__			)
  
  
  
  		@rank_zero_only
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: pl.Trainer     ,  UpperCamelCase__							: pl.LightningModule     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: int=True			)	-> None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				logger.info(f'***** {type_path} results at step {trainer.global_step:05d} *****'			)
  				snake_case     :					Dict     =	trainer.callback_metrics
  				trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']}			)
  				# Log results
  				snake_case     :					Tuple     =	Path(pl_module.hparams.output_dir			)
  				if type_path == "test":
  						snake_case     :					Dict     =	od / '''test_results.txt'''
  						snake_case     :					Tuple     =	od / '''test_generations.txt'''
  				else:
  						# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
  						# If people want this it will be easy enough to add back.
  						snake_case     :					str     =	od / f'{type_path}_results/{trainer.global_step:05d}.txt'
  						snake_case     :					int     =	od / f'{type_path}_generations/{trainer.global_step:05d}.txt'
  						results_file.parent.mkdir(exist_ok=UpperCamelCase__			)
  						generations_file.parent.mkdir(exist_ok=UpperCamelCase__			)
  				with open(UpperCamelCase__     ,  '''a+'''			) as writer:
  						for key in sorted(UpperCamelCase__			):
  								if key in ["log", "progress_bar", "preds"]:
  										continue
  								snake_case     :					Union[str, Any]     =	metrics[key]
  								if isinstance(UpperCamelCase__     ,  torch.Tensor			):
  										snake_case     :					str     =	val.item()
  								snake_case     :					Any     =	f'{key}: {val:.6f}\n'
  								writer.write(UpperCamelCase__			)
  
  				if not save_generations:
  						return
  
  				if "preds" in metrics:
  						snake_case     :					List[Any]     =	'''\n'''.join(metrics['''preds''']			)
  						generations_file.open('''w+'''			).write(UpperCamelCase__			)
  
  
  
  		@rank_zero_only
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				try:
  						snake_case     :					Optional[Any]     =	pl_module.model.model.num_parameters()
  				except AttributeError:
  						snake_case     :					Dict     =	pl_module.model.num_parameters()
  
  				snake_case     :					Optional[int]     =	count_trainable_parameters(UpperCamelCase__			)
  				# mp stands for million parameters
  				trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6}			)
  
  
  
  		@rank_zero_only
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: pl.Trainer     ,  UpperCamelCase__							: pl.LightningModule			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				save_json(pl_module.metrics     ,  pl_module.metrics_save_path			)
  				return self._write_logs(UpperCamelCase__     ,  UpperCamelCase__     ,  '''test'''			)
  
  
  
  		@rank_zero_only
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: pl.Trainer     ,  UpperCamelCase__							: Any			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				save_json(pl_module.metrics     ,  pl_module.metrics_save_path			)
  				# Uncommenting this will save val generations
  				# return self._write_logs(trainer, pl_module, "valid")
  
  
  
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=1   ,					SCREAMING_SNAKE_CASE__=False )     ->       int:
  
  
  		'''simple docstring'''
  
  
  		if metric == "rouge2":
  				snake_case     :					List[str]     =	'''{val_avg_rouge2:.4f}-{step_count}'''
  		elif metric == "bleu":
  				snake_case     :					Union[str, Any]     =	'''{val_avg_bleu:.4f}-{step_count}'''
  		elif metric == "loss":
  				snake_case     :					List[str]     =	'''{val_avg_loss:.4f}-{step_count}'''
  		else:
  				raise NotImplementedError(
  				    F'seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'
  				    ''' this function.''' )
  
  		snake_case     :					Dict     =	ModelCheckpoint(
  		    dirpath=SCREAMING_SNAKE_CASE__   ,					filename=SCREAMING_SNAKE_CASE__   ,					monitor=F'val_{metric}'   ,					mode='''min''' if '''loss''' in metric else '''max'''   ,					save_top_k=SCREAMING_SNAKE_CASE__   ,					)
  		return checkpoint_callback
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		return EarlyStopping(
  		    monitor=F'val_{metric}'   ,					mode='''min''' if '''loss''' in metric else '''max'''   ,					patience=SCREAMING_SNAKE_CASE__   ,					verbose=SCREAMING_SNAKE_CASE__   ,					)
  
  
  
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)
  
  
  