




import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_PRETRAINING_MAPPING,
      MegatronBertForCausalLM,
      MegatronBertForMaskedLM,
      MegatronBertForMultipleChoice,
      MegatronBertForNextSentencePrediction,
      MegatronBertForPreTraining,
      MegatronBertForQuestionAnswering,
      MegatronBertForSequenceClassification,
      MegatronBertForTokenClassification,
      MegatronBertModel,
  )







class 						lowercase							:

     def __init__(  self		,     _a		,     _a=13		,     _a=7		,     _a=True		,     _a=True		,     _a=True		,     _a=True		,     _a=99		,     _a=64		,     _a=32		,     _a=5		,     _a=4		,     _a=37		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=16		,     _a=2		,     _a=0.02		,     _a=3		,     _a=4		,     _a=None		,     )						->							Optional[int]:
           _A							:     Optional[int]			=						parent
           _A							:     Optional[Any]			=						batch_size
           _A							:     Tuple			=						seq_length
           _A							:     Optional[Any]			=						is_training
           _A							:     List[Any]			=						use_input_mask
           _A							:     Any			=						use_token_type_ids
           _A							:     List[Any]			=						use_labels
           _A							:     Optional[int]			=						vocab_size
           _A							:     str			=						hidden_size
           _A							:     List[str]			=						embedding_size
           _A							:     List[str]			=						num_hidden_layers
           _A							:     List[str]			=						num_attention_heads
           _A							:     Dict			=						intermediate_size
           _A							:     Dict			=						hidden_act
           _A							:     Any			=						hidden_dropout_prob
           _A							:     List[Any]			=						attention_probs_dropout_prob
           _A							:     str			=						max_position_embeddings
           _A							:     List[str]			=						type_vocab_size
           _A							:     int			=						type_sequence_label_size
           _A							:     Optional[Any]			=						initializer_range
           _A							:     Union[str, Any]			=						num_labels
           _A							:     Any			=						num_choices
           _A							:     List[str]			=						scope

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     str			=						ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size				)

           _A							:     List[str]			=						None
           if self.use_input_mask:
                 _A							:     Tuple			=						random_attention_mask([self.batch_size, self.seq_length]				)

           _A							:     Union[str, Any]			=						None
           if self.use_token_type_ids:
                 _A							:     Any			=						ids_tensor([self.batch_size, self.seq_length]		,     self.type_vocab_size				)

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

           _A							:     List[Any]			=						self.get_config()

           return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

     def 					a__	(  self				)						->							Optional[int]:
           return MegatronBertConfig(
               vocab_size=self.vocab_size		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     embedding_size=self.embedding_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     max_position_embeddings=self.max_position_embeddings		,     type_vocab_size=self.type_vocab_size		,     is_decoder=_a		,     initializer_range=self.initializer_range		,     )

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Any:
           _A							:     Tuple			=						MegatronBertModel(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     str			=						model(_a		,     attention_mask=_a		,     token_type_ids=_a				)
           _A							:     Any			=						model(_a		,     token_type_ids=_a				)
           _A							:     int			=						model(_a				)

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

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							List[str]:
           _A							:     List[Any]			=						MegatronBertForMaskedLM(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     List[Any]			=						model(_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a				)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Union[str, Any]:
           _A							:     Optional[int]			=						MegatronBertForCausalLM(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     List[Any]			=						model(_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a				)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Union[str, Any]:
           _A							:     List[str]			=						MegatronBertForNextSentencePrediction(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     List[str]			=						model(
               _a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, 2)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							List[str]:
           _A							:     Dict			=						MegatronBertForPreTraining(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     str			=						model(
               _a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     next_sentence_label=_a		,     )
           self.parent.assertEqual(result.prediction_logits.shape		,     (self.batch_size, self.seq_length, self.vocab_size)				)
           self.parent.assertEqual(result.seq_relationship_logits.shape		,     (self.batch_size, 2)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							List[str]:
           _A							:     Dict			=						MegatronBertForQuestionAnswering(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     Dict			=						model(
               _a		,     attention_mask=_a		,     token_type_ids=_a		,     start_positions=_a		,     end_positions=_a		,     )
           self.parent.assertEqual(result.start_logits.shape		,     (self.batch_size, self.seq_length)				)
           self.parent.assertEqual(result.end_logits.shape		,     (self.batch_size, self.seq_length)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Tuple:
           _A							:     Tuple			=						self.num_labels
           _A							:     Any			=						MegatronBertForSequenceClassification(_a				)
           model.to(_a				)
           model.eval()
           _A							:     str			=						model(_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a				)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							int:
           _A							:     Union[str, Any]			=						self.num_labels
           _A							:     Optional[int]			=						MegatronBertForTokenClassification(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     Dict			=						model(_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a				)
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Union[str, Any]:
           _A							:     str			=						self.num_choices
           _A							:     Optional[Any]			=						MegatronBertForMultipleChoice(config=_a				)
           model.to(_a				)
           model.eval()
           _A							:     List[str]			=						input_ids.unsqueeze(1				).expand(-1		,     self.num_choices		,     -1				).contiguous()
           _A							:     int			=						token_type_ids.unsqueeze(1				).expand(-1		,     self.num_choices		,     -1				).contiguous()
           _A							:     Any			=						input_mask.unsqueeze(1				).expand(-1		,     self.num_choices		,     -1				).contiguous()
           _A							:     Optional[int]			=						model(
               _a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_choices)				)



     def 					a__	(  self				)						->							int:
           _A							:     Optional[int]			=						self.prepare_config_and_inputs()
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     int			=						config_and_inputs
           _A							:     Optional[int]			=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
           return config, inputs_dict







@require_torch
class 						lowercase							(   UpperCamelCase__,UpperCamelCase__,unittest.TestCase		):
     _a							 =						(
         (
             MegatronBertModel,
             MegatronBertForMaskedLM,
             MegatronBertForCausalLM,
             MegatronBertForMultipleChoice,
             MegatronBertForNextSentencePrediction,
             MegatronBertForPreTraining,
             MegatronBertForQuestionAnswering,
             MegatronBertForSequenceClassification,
             MegatronBertForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     _a							 =						(
         {
             "feature-extraction": MegatronBertModel,
             "fill-mask": MegatronBertForMaskedLM,
             "question-answering": MegatronBertForQuestionAnswering,
             "text-classification": MegatronBertForSequenceClassification,
             "text-generation": MegatronBertForCausalLM,
             "token-classification": MegatronBertForTokenClassification,
             "zero-shot": MegatronBertForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _a							 =						True
     # test_resize_embeddings = False
     _a							 =						False

     def 					a__	(  self		,     _a		,     _a		,     _a=False				)						->							Optional[int]:
           _A							:     List[str]			=						super()._prepare_for_class(_a		,     _a		,     return_labels=_a				)

           if return_labels:
                 if model_class in get_values(_a				):
                       _A							:     Any			=						torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.seq_length)		,     dtype=torch.long		,     device=_a				)
                       _A							:     Any			=						torch.zeros(
                           self.model_tester.batch_size		,     dtype=torch.long		,     device=_a				)
           return inputs_dict

     def 					a__	(  self				)						->							Dict:
           _A							:     Dict			=						MegatronBertModelTester(self				)
           _A							:     str			=						ConfigTester(self		,     config_class=_a		,     hidden_size=37				)

     def 					a__	(  self				)						->							str:
           self.config_tester.run_common_tests()

     def 					a__	(  self				)						->							Optional[int]:
           _A							:     List[str]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_model(*_a				)

     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_masked_lm(*_a				)

     def 					a__	(  self				)						->							Dict:
           _A							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*_a				)

     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*_a				)

     def 					a__	(  self				)						->							List[str]:
           _A							:     int			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_pretraining(*_a				)

     def 					a__	(  self				)						->							int:
           _A							:     Tuple			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_question_answering(*_a				)

     def 					a__	(  self				)						->							Dict:
           _A							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*_a				)



     def 					a__	(  self				)						->							int:
           _A							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_megatron_bert_for_token_classification(*_a				)







def 				lowerCAmelCase_	(     snake_case_     ):
      return torch.tensor(
          snake_case_,dtype=torch.long,device=snake_case_,)


_snake_case										=       1e-4







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     @unittest.skip("""Model is not available."""				)
     def 					a__	(  self				)						->							List[Any]:
           _A							:     Optional[Any]			=						"""nvidia/megatron-bert-uncased-345m"""
           if "MYDIR" in os.environ:
                 _A							:     List[str]			=						os.path.join(os.environ["""MYDIR"""]		,     _a				)
           _A							:     Any			=						MegatronBertModel.from_pretrained(_a				)
           model.to(_a				)
           model.half()
           _A							:     Optional[int]			=						_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]				)
           with torch.no_grad():
                 _A							:     Dict			=						model(_a				)[0]
           _A							:     List[str]			=						torch.Size((1, 9, 1024)				)
           self.assertEqual(output.shape		,     _a				)

           _A							:     Tuple			=						[-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
           for ii in range(3				):
                 for jj in range(3				):
                       _A							:     Any			=						output[0, ii, jj]
                       _A							:     Optional[int]			=						expected[3 * ii + jj]
                       _A							:     Tuple			=						"""ii={} jj={} a={} b={}""".format(_a		,     _a		,     _a		,     _a				)
                       self.assertTrue(math.isclose(_a		,     _a		,     rel_tol=_a		,     abs_tol=_a				)		,     msg=_a				)







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,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

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

      _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained(snake_case_     )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

      if max_source_length is None:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						SeqaSeqDataset(
          snake_case_,snake_case_,snake_case_,max_target_length=1024,type_path=snake_case_,n_obs=snake_case_,prefix=snake_case_,**snake_case_,)
      # 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							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     Tuple			=						model.generate(
                input_ids=batch["""input_ids"""].to(model.device     ),attention_mask=batch["""attention_mask"""].to(model.device     ),num_return_sequences=snake_case_,num_beams=snake_case_,**snake_case_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						chunks(snake_case_,snake_case_     )  # batch size chunks, each of size num_return_seq
            for i, pred in enumerate(snake_case_     ):
                  results.append({"""pred""": pred, """id""": ids[i].item()}     )
      save_json(snake_case_,snake_case_     )
      return results, sampler.num_replicas







def 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						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=snake_case_,help="""like cnn_dm/test.source"""     )
      parser.add_argument(
          """--model_name""",type=snake_case_,help="""like facebook/bart-large-cnn,t5-base, etc.""",default="""sshleifer/distilbart-xsum-12-3""",)
      parser.add_argument("""--save_dir""",type=snake_case_,help="""where to save""",default="""tmp_gen"""     )
      parser.add_argument("""--max_source_length""",type=snake_case_,default=snake_case_     )
      parser.add_argument(
          """--type_path""",type=snake_case_,default="""test""",help="""which subset to evaluate typically train/val/test"""     )
      parser.add_argument("""--task""",type=snake_case_,default="""summarization""",help="""used for task_specific_params + metrics"""     )
      parser.add_argument("""--bs""",type=snake_case_,default=8,required=snake_case_,help="""batch size"""     )
      parser.add_argument(
          """--local_rank""",type=snake_case_,default=-1,required=snake_case_,help="""should be passed by distributed.launch"""     )

      parser.add_argument(
          """--n_obs""",type=snake_case_,default=snake_case_,required=snake_case_,help="""How many observations. Defaults to all."""     )
      parser.add_argument(
          """--num_return_sequences""",type=snake_case_,default=1,required=snake_case_,help="""How many sequences to return"""     )
      parser.add_argument(
          """--sync_timeout""",type=snake_case_,default=600,required=snake_case_,help="""How long should master process wait for other processes to finish.""",)
      parser.add_argument("""--src_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument("""--tgt_lang""",type=snake_case_,default=snake_case_,required=snake_case_     )
      parser.add_argument(
          """--prefix""",type=snake_case_,required=snake_case_,default=snake_case_,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							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						parse_numeric_n_bool_cl_kwargs(snake_case_     )
      if generate_kwargs and args.local_rank <= 0:
            print(f'''parsed the following generate kwargs: {generate_kwargs}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     int			=						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							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,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=snake_case_,**snake_case_,)

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

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      # WAIT FOR lots of .json files
      _A							:     Optional[Any]			=						time.time()
      logger.info("""waiting for all nodes to finish"""     )
      _A							:     List[str]			=						None
      while (time.time() - start_wait) < timeout:
            _A							:     str			=						list(save_dir.glob("""rank_*.json"""     )     )
            if len(snake_case_     ) < num_replicas:
                  continue
            try:
                  # make sure all json files are fully saved
                  _A							:     List[str]			=						lmap(snake_case_,snake_case_     )
                  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()

