



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OpenAIGPTConfig,
						    OpenAIGPTDoubleHeadsModel,
						    OpenAIGPTForSequenceClassification,
						    OpenAIGPTLMHeadModel,
						    OpenAIGPTModel,
						)


class 				_a						:





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=13 ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=True ,					__UpperCAmelCase=99 ,					__UpperCAmelCase=32 ,					__UpperCAmelCase=5 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=37 ,					__UpperCAmelCase="gelu" ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=0.1 ,					__UpperCAmelCase=512 ,					__UpperCAmelCase=16 ,					__UpperCAmelCase=2 ,					__UpperCAmelCase=0.0_2 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=4 ,					__UpperCAmelCase=None ,					):







											"""simple docstring"""



											a__	:							Tuple      						= parent
											a__	:							Union[str, Any]      						= batch_size
											a__	:							Tuple      						= seq_length
											a__	:							Any      						= is_training
											a__	:							Any      						= use_token_type_ids
											a__	:							str      						= use_labels
											a__	:							int      						= vocab_size
											a__	:							Any      						= hidden_size
											a__	:							Union[str, Any]      						= num_hidden_layers
											a__	:							Optional[int]      						= num_attention_heads
											a__	:							Optional[int]      						= intermediate_size
											a__	:							int      						= hidden_act
											a__	:							Union[str, Any]      						= hidden_dropout_prob
											a__	:							int      						= attention_probs_dropout_prob
											a__	:							Optional[int]      						= max_position_embeddings
											a__	:							Optional[int]      						= type_vocab_size
											a__	:							Union[str, Any]      						= type_sequence_label_size
											a__	:							Dict      						= initializer_range
											a__	:							Tuple      						= num_labels
											a__	:							str      						= num_choices
											a__	:							Union[str, Any]      						= scope
											a__	:							Any      						= self.vocab_size - 1



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Any      						= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size				)

											a__	:							List[str]      						= None
											if self.use_token_type_ids:
															a__	:							Optional[int]      						= ids_tensor([self.batch_size, self.seq_length] ,					self.type_vocab_size				)

											a__	:							List[Any]      						= None
											a__	:							Tuple      						= None
											a__	:							Union[str, Any]      						= None
											if self.use_labels:
															a__	:							Optional[int]      						= ids_tensor([self.batch_size] ,					self.type_sequence_label_size				)
															a__	:							Union[str, Any]      						= ids_tensor([self.batch_size, self.seq_length] ,					self.num_labels				)
															a__	:							Union[str, Any]      						= ids_tensor([self.batch_size] ,					self.num_choices				)

											a__	:							Optional[Any]      						= OpenAIGPTConfig(
											    vocab_size=self.vocab_size ,					n_embd=self.hidden_size ,					n_layer=self.num_hidden_layers ,					n_head=self.num_attention_heads ,					n_positions=self.max_position_embeddings ,					pad_token_id=self.pad_token_id ,					)

											a__	:							str      						= ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,					2				)

											return (
											    config,
											    input_ids,
											    head_mask,
											    token_type_ids,
											    sequence_labels,
											    token_labels,
											    choice_labels,
											)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					*__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= OpenAIGPTModel(config=__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()

											a__	:							List[Any]      						= model(__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					head_mask=__UpperCAmelCase				)
											a__	:							List[Any]      						= model(__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase				)
											a__	:							Optional[Any]      						= model(__UpperCAmelCase				)

											self.parent.assertEqual(result.last_hidden_state.shape ,					(self.batch_size, self.seq_length, self.hidden_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					*__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= OpenAIGPTLMHeadModel(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()

											a__	:							List[str]      						= model(__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.loss.shape ,					()				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					*__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= OpenAIGPTDoubleHeadsModel(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()

											a__	:							List[str]      						= model(__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.loss.shape ,					()				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.seq_length, self.vocab_size)				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					*__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= self.num_labels
											a__	:							Dict      						= OpenAIGPTForSequenceClassification(__UpperCAmelCase				)
											model.to(__UpperCAmelCase				)
											model.eval()

											a__	:							List[Any]      						= ids_tensor([self.batch_size] ,					self.type_sequence_label_size				)
											a__	:							Any      						= model(__UpperCAmelCase ,					token_type_ids=__UpperCAmelCase ,					labels=__UpperCAmelCase				)
											self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)				)




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.prepare_config_and_inputs()
											(
											    (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   (
											    a__
											)						,   
											)	:							Any      						= config_and_inputs
											a__	:							Tuple      						= {
											    "input_ids": input_ids,
											    "token_type_ids": token_type_ids,
											    "head_mask": head_mask,
											}

											return config, inputs_dict





@require_torch
class 				_a						(       SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  SCREAMING_SNAKE_CASE       ,  unittest.TestCase			):





							'''simple docstring'''

							A				:Any =       (
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							A				:List[str] =       (
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							A				:List[str] =       (
							    {
							        "feature-extraction": OpenAIGPTModel,
							        "text-classification": OpenAIGPTForSequenceClassification,
							        "text-generation": OpenAIGPTLMHeadModel,
							        "zero-shot": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
															# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
															# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
															# tiny config could not be created.
															return True

											return False



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase=False				):







											"""simple docstring"""



											a__	:							Optional[int]      						= super()._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase ,					return_labels=__UpperCAmelCase				)

											if return_labels:
															if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																			a__	:							int      						= torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,					dtype=torch.long ,					device=__UpperCAmelCase ,					)
																			a__	:							Dict      						= inputs_dict["labels"]
																			a__	:							Optional[Any]      						= inputs_dict["labels"]
																			a__	:							Any      						= torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices) ,					dtype=torch.long ,					device=__UpperCAmelCase ,					)
																			a__	:							Any      						= torch.zeros(
																			    self.model_tester.batch_size ,					dtype=torch.long ,					device=__UpperCAmelCase				)
											return inputs_dict



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= OpenAIGPTModelTester(self				)
											a__	:							List[str]      						= ConfigTester(self ,					config_class=__UpperCAmelCase ,					n_embd=37				)



							def 		_A	(							self				):







											"""simple docstring"""



											self.config_tester.run_common_tests()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[str]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_openai_gpt_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_lm_head_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_double_lm_head_model(*__UpperCAmelCase				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__UpperCAmelCase				)




							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															a__	:							int      						= OpenAIGPTModel.from_pretrained(__UpperCAmelCase				)
															self.assertIsNotNone(__UpperCAmelCase				)





@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							@slow
							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= OpenAIGPTLMHeadModel.from_pretrained("openai-gpt"				)
											model.to(__UpperCAmelCase				)
											a__	:							List[Any]      						= torch.tensor([[481, 4735, 544]] ,					dtype=torch.long ,					device=__UpperCAmelCase				)  # the president is
											a__	:							int      						= [
											    481,
											    4735,
											    544,
											    246,
											    963,
											    870,
											    762,
											    239,
											    244,
											    4_0477,
											    244,
											    249,
											    719,
											    881,
											    487,
											    544,
											    240,
											    244,
											    603,
											    481,
											]  # the president is a very good man. " \n " i\'m sure he is, " said the

											a__	:							List[Any]      						= model.generate(__UpperCAmelCase ,					do_sample=__UpperCAmelCase				)
											self.assertListEqual(output_ids[0].tolist() ,					__UpperCAmelCase				)







import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCamelCase		       =      getLogger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase = 8	,				__UpperCamelCase = 10_24	,				__UpperCamelCase="val"	,				__UpperCamelCase=None	,				__UpperCamelCase=False	,				__UpperCamelCase="summarization"	,				__UpperCamelCase=None	,				__UpperCamelCase=1	,				__UpperCamelCase = None	,				__UpperCamelCase=""	,				**__UpperCamelCase	,				)	->						Dict:
				a__	:							Any      						= str(__UpperCamelCase )
				assert local_rank is not None
				torch.distributed.init_process_group(backend="nccl"	,				rank=__UpperCamelCase )

				a__	:							int      						= Path(__UpperCamelCase )
				a__	:							List[Any]      						= save_dir.joinpath(F'rank_{local_rank}_output.json' )
				torch.cuda.set_device(__UpperCamelCase )
				a__	:							Any      						= AutoModelForSeqaSeqLM.from_pretrained(__UpperCamelCase ).cuda()
				if fpaa:
								a__	:							Optional[int]      						= model.half()
				# determine if we need to increase num_beams
				use_task_specific_params(__UpperCamelCase	,				__UpperCamelCase )  # update config with task specific params
				a__	:							Optional[Any]      						= generate_kwargs.pop("num_beams"	,				model.config.num_beams )  # AttributeError risk?
				if num_return_sequences > num_beams:
								a__	:							Optional[Any]      						= num_return_sequences

				a__	:							Any      						= AutoTokenizer.from_pretrained(__UpperCamelCase )
				logger.info(F'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.

				if max_source_length is None:
								a__	:							Optional[Any]      						= tokenizer.model_max_length
				if prefix is None:
								a__	:							str      						= prefix or getattr(model.config	,				"prefix"	,				"" ) or ""
				a__	:							str      						= SeqaSeqDataset(
				    __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				max_target_length=10_24	,				type_path=__UpperCamelCase	,				n_obs=__UpperCamelCase	,				prefix=__UpperCamelCase	,				**__UpperCamelCase	,				)
				# I set shuffle=True for a more accurate progress bar.
				# If all the longest samples are first, the prog bar estimate is too high at the beginning.
				a__	:							Any      						= ds.make_sortish_sampler(__UpperCamelCase	,				distributed=__UpperCamelCase	,				add_extra_examples=__UpperCamelCase	,				shuffle=__UpperCamelCase )
				a__	:							Optional[int]      						= DataLoader(__UpperCamelCase	,				sampler=__UpperCamelCase	,				batch_size=__UpperCamelCase	,				collate_fn=ds.collate_fn )
				a__	:							Optional[int]      						= []
				for batch in tqdm(__UpperCamelCase ):
								a__	:							Optional[Any]      						= model.generate(
								    input_ids=batch["input_ids"].to(model.device )	,				attention_mask=batch["attention_mask"].to(model.device )	,				num_return_sequences=__UpperCamelCase	,				num_beams=__UpperCamelCase	,				**__UpperCamelCase	,				)
								a__	:							Any      						= tokenizer.batch_decode(__UpperCamelCase	,				skip_special_tokens=__UpperCamelCase	,				clean_up_tokenization_spaces=__UpperCamelCase )
								a__	:							Dict      						= batch["ids"]
								if num_return_sequences > 1:
												a__	:							int      						= chunks(__UpperCamelCase	,				__UpperCamelCase )  # batch size chunks, each of size num_return_seq
								for i, pred in enumerate(__UpperCamelCase ):
												results.append({"pred": pred, "id": ids[i].item()} )
				save_json(__UpperCamelCase	,				__UpperCamelCase )
				return results, sampler.num_replicas






def     SCREAMING_SNAKE_CASE(      )	->						List[str]:
				a__	:							List[str]      						= argparse.ArgumentParser(
				    epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate" )
				parser.add_argument("--data_dir"	,				type=__UpperCamelCase	,				help="like cnn_dm/test.source" )
				parser.add_argument(
				    "--model_name"	,				type=__UpperCamelCase	,				help="like facebook/bart-large-cnn,t5-base, etc."	,				default="sshleifer/distilbart-xsum-12-3"	,				)
				parser.add_argument("--save_dir"	,				type=__UpperCamelCase	,				help="where to save"	,				default="tmp_gen" )
				parser.add_argument("--max_source_length"	,				type=__UpperCamelCase	,				default=__UpperCamelCase )
				parser.add_argument(
				    "--type_path"	,				type=__UpperCamelCase	,				default="test"	,				help="which subset to evaluate typically train/val/test" )
				parser.add_argument("--task"	,				type=__UpperCamelCase	,				default="summarization"	,				help="used for task_specific_params + metrics" )
				parser.add_argument("--bs"	,				type=__UpperCamelCase	,				default=8	,				required=__UpperCamelCase	,				help="batch size" )
				parser.add_argument(
				    "--local_rank"	,				type=__UpperCamelCase	,				default=-1	,				required=__UpperCamelCase	,				help="should be passed by distributed.launch" )

				parser.add_argument(
				    "--n_obs"	,				type=__UpperCamelCase	,				default=__UpperCamelCase	,				required=__UpperCamelCase	,				help="How many observations. Defaults to all." )
				parser.add_argument(
				    "--num_return_sequences"	,				type=__UpperCamelCase	,				default=1	,				required=__UpperCamelCase	,				help="How many sequences to return" )
				parser.add_argument(
				    "--sync_timeout"	,				type=__UpperCamelCase	,				default=6_00	,				required=__UpperCamelCase	,				help="How long should master process wait for other processes to finish."	,				)
				parser.add_argument("--src_lang"	,				type=__UpperCamelCase	,				default=__UpperCamelCase	,				required=__UpperCamelCase )
				parser.add_argument("--tgt_lang"	,				type=__UpperCamelCase	,				default=__UpperCamelCase	,				required=__UpperCamelCase )
				parser.add_argument(
				    "--prefix"	,				type=__UpperCamelCase	,				required=__UpperCamelCase	,				default=__UpperCamelCase	,				help="will be added to the begininng of src examples" )
				parser.add_argument("--fp16"	,				action="store_true" )
				parser.add_argument("--debug"	,				action="store_true" )
				a__	:							str      						= time.time()
				a__						,   a__	:							Dict      						= parser.parse_known_args()
				a__	:							Optional[Any]      						= parse_numeric_n_bool_cl_kwargs(__UpperCamelCase )
				if generate_kwargs and args.local_rank <= 0:
								print(F'parsed the following generate kwargs: {generate_kwargs}' )
				a__	:							List[str]      						= Path(args.save_dir + "_tmp" )
				Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )  # this handles locking.
				a__	:							List[Any]      						= list(json_save_dir.glob("rank_*.json" ) )
				if intermediate_files:
								raise ValueError(F'Found files at {json_save_dir} please move or remove them.' )
								# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
				a__	:							List[Any]      						= {}
				if args.src_lang is not None:
								a__	:							str      						= args.src_lang
				if args.tgt_lang is not None:
								a__	:							int      						= args.tgt_lang

				Path(args.save_dir ).mkdir(exist_ok=__UpperCamelCase )
				a__						,   a__	:							Tuple      						= eval_data_dir(
				    args.data_dir	,				__UpperCamelCase	,				args.model_name	,				type_path=args.type_path	,				bs=args.bs	,				fpaa=args.fpaa	,				task=args.task	,				local_rank=args.local_rank	,				n_obs=args.n_obs	,				max_source_length=args.max_source_length	,				num_return_sequences=args.num_return_sequences	,				prefix=args.prefix	,				dataset_kwargs=__UpperCamelCase	,				**__UpperCamelCase	,				)

				if args.local_rank <= 0:
								a__	:							str      						= Path(args.save_dir )
								save_dir.mkdir(exist_ok=__UpperCamelCase )
								a__	:							Optional[int]      						= gather_results_from_each_node(__UpperCamelCase	,				__UpperCamelCase	,				args.sync_timeout )
								a__	:							Tuple      						= combine_partial_results(__UpperCamelCase )
								if args.num_return_sequences > 1:
												a__	:							Optional[int]      						= save_dir.joinpath("pseudolabel_results.json" )
												print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/' )
												save_json(__UpperCamelCase	,				__UpperCamelCase )
												return
								a__	:							Any      						= Path(args.data_dir ).joinpath(args.type_path + ".target" )
								with open(__UpperCamelCase ) as f:
												a__	:							Optional[int]      						= [x.rstrip() for x in f.readlines()][: len(__UpperCamelCase )]

								# Calculate metrics, save metrics,  and save _generations.txt
								a__	:							Any      						= "translation" in args.task
								a__	:							List[Any]      						= calculate_bleu if calc_bleu else calculate_rouge
								a__	:							List[Any]      						= "bleu" if calc_bleu else "rouge"
								a__	:							Dict      						= score_fn(__UpperCamelCase	,				__UpperCamelCase )
								a__	:							int      						= len(__UpperCamelCase )
								a__	:							Union[str, Any]      						= time.time() - start_time
								a__	:							List[Any]      						= round(runtime / metrics["n_obs"]	,				4 )
								a__	:							List[str]      						= num_replicas
								# TODO(@stas00): add whatever metadata to metrics
								a__	:							int      						= save_dir.joinpath(F'{args.type_path}_{metric_name}.json' )
								save_json(__UpperCamelCase	,				__UpperCamelCase	,				indent=__UpperCamelCase )
								print(__UpperCamelCase )
								write_txt_file(__UpperCamelCase	,				save_dir.joinpath(F'{args.type_path}_generations.txt' ) )
								if args.debug:
												write_txt_file(__UpperCamelCase	,				save_dir.joinpath(F'{args.type_path}.target' ) )
								else:
												shutil.rmtree(__UpperCamelCase )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List:
				a__	:							Optional[Any]      						= []
				for partial_result in partial_results:
								records.extend(__UpperCamelCase )
				a__	:							Tuple      						= sorted(__UpperCamelCase	,				key=lambda __UpperCamelCase : x["id"] )
				a__	:							Tuple      						= [x["pred"] for x in records]
				return preds






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						List[Dict[str, List]]:
				# WAIT FOR lots of .json files
				a__	:							Optional[Any]      						= time.time()
				logger.info("waiting for all nodes to finish" )
				a__	:							int      						= None
				while (time.time() - start_wait) < timeout:
								a__	:							List[str]      						= list(save_dir.glob("rank_*.json" ) )
								if len(__UpperCamelCase ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												a__	:							int      						= lmap(__UpperCamelCase	,				__UpperCamelCase )
												return json_data
								except JSONDecodeError:
												continue
				else:
								raise TimeoutError("Rank 0 gave up on waiting for other processes" )
				# Unreachable


if __name__ == "__main__":
						# Usage for MT:
						run_generate()


