code: 
  '''simple docstring'''
  import torch
  
  from transformers import AutoTokenizer
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[str]			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[Any]     =	AutoTokenizer.from_pretrained(UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: str			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Any     =	None
  						for l in x:
  								snake_case     :					str     =	self.tokenizer(
  								    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  								snake_case     :					Tuple     =	torch.tensor([len(UpperCamelCase__			)]			)
  								if d is not None:
  										for k in d.keys():
  												snake_case     :					int     =	torch.cat((d[k], t[k])     ,  0			)
  								else:
  										snake_case     :					List[str]     =	t
  
  						snake_case     :					Union[str, Any]     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[E]'''			)			)
  						snake_case     :					int     =	torch.tensor(self.tokenizer.convert_tokens_to_ids('''[/E]'''			)			)
  
  				elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and all(isinstance(_x     ,  UpperCamelCase__			) for _x in x			):
  						snake_case     :					Optional[int]     =	self.tokenizer(
  						    UpperCamelCase__     ,  padding='''max_length'''     ,  max_length=384     ,  truncation=UpperCamelCase__     ,  return_tensors='''pt'''     ,  )
  
  				else:
  						raise Exception(
  						    '''Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'''
  						    ''' strings` for supports are supported.'''			)
  
  				return d
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Any=0.70			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	[]
  				for idx in range(len(W_query['''input_ids''']			)			):
  						snake_case     :					Optional[int]     =	range(p_start.shape[1]			)
  
  						snake_case     :					str     =	[]
  						for start_id in start_indexes:
  								for end_id in end_indexes:
  										if start_id < end_id:
  												output.append(
  												    (
  												        start_id,
  												        end_id,
  												        p_start[idx][start_id].item(),
  												        p_end[idx][end_id].item(),
  												    )			)
  
  						output.sort(key=lambda UpperCamelCase__			: (tup[2] * tup[3])     ,  reverse=UpperCamelCase__			)
  						snake_case     :					int     =	[]
  						for k in range(len(UpperCamelCase__			)			):
  								if output[k][2] * output[k][3] >= thresh:
  										snake_case ,snake_case     :					Tuple     =	output[k][0], output[k][1]
  										snake_case     :					str     =	self.tokenizer.decode(W_query['''input_ids'''][idx][c_start_pos:c_end_pos]			)
  										temp.append((decoded, output[k][2] * output[k][3])			)
  
  						final_outputs.append(UpperCamelCase__			)
  
  				return final_outputs
  
  
  
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)
  
  
  