



from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel

@require_tf
class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       BlenderbotSmallConfig
			UpperCamelCase									=       {}
			UpperCamelCase									=       """gelu"""




			def __init__(   self							: List[str]       ,       a_							: Any       ,       a_							: Union[str, Any]=13       ,       a_							: Optional[int]=7       ,       a_							: Any=True       ,       a_							: List[Any]=False       ,       a_							: int=99       ,       a_							: Optional[Any]=32       ,       a_							: int=2       ,       a_							: Dict=4       ,       a_							: Union[str, Any]=37       ,       a_							: Optional[int]=0.1       ,       a_							: Tuple=0.1       ,       a_							: List[str]=20       ,       a_							: int=2       ,       a_							: Tuple=1       ,       a_							: Union[str, Any]=0       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	parent
					__UpperCAmelCase    :     int													=	batch_size
					__UpperCAmelCase    :     Dict													=	seq_length
					__UpperCAmelCase    :     Optional[int]													=	is_training
					__UpperCAmelCase    :     str													=	use_labels
					__UpperCAmelCase    :     List[Any]													=	vocab_size
					__UpperCAmelCase    :     str													=	hidden_size
					__UpperCAmelCase    :     Any													=	num_hidden_layers
					__UpperCAmelCase    :     Optional[int]													=	num_attention_heads
					__UpperCAmelCase    :     Dict													=	intermediate_size

					__UpperCAmelCase    :     Union[str, Any]													=	hidden_dropout_prob
					__UpperCAmelCase    :     Optional[Any]													=	attention_probs_dropout_prob
					__UpperCAmelCase    :     Optional[int]													=	max_position_embeddings
					__UpperCAmelCase    :     List[Any]													=	eos_token_id
					__UpperCAmelCase    :     str													=	pad_token_id
					__UpperCAmelCase    :     Optional[Any]													=	bos_token_id




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	ids_tensor([self.batch_size, self.seq_length - 1]       ,       self.vocab_size )
					__UpperCAmelCase    :     int													=	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )       ,       1 )
					__UpperCAmelCase    :     str													=	tf.concat([input_ids, eos_tensor]       ,       axis=1 )

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

					__UpperCAmelCase    :     Dict													=	self.config_cls(
					    vocab_size=self.vocab_size       ,       d_model=self.hidden_size       ,       encoder_layers=self.num_hidden_layers       ,       decoder_layers=self.num_hidden_layers       ,       encoder_attention_heads=self.num_attention_heads       ,       decoder_attention_heads=self.num_attention_heads       ,       encoder_ffn_dim=self.intermediate_size       ,       decoder_ffn_dim=self.intermediate_size       ,       dropout=self.hidden_dropout_prob       ,       attention_dropout=self.attention_probs_dropout_prob       ,       max_position_embeddings=self.max_position_embeddings       ,       eos_token_ids=[2]       ,       bos_token_id=self.bos_token_id       ,       pad_token_id=self.pad_token_id       ,       decoder_start_token_id=self.pad_token_id       ,       **self.config_updates       ,       )
					__UpperCAmelCase    :     List[str]													=	prepare_blenderbot_small_inputs_dict(a_       ,       a_       ,       a_ )
					return config, inputs_dict




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Optional[Any]       ,       a_							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	TFBlenderbotSmallModel(config=a_ ).get_decoder()
					__UpperCAmelCase    :     List[str]													=	inputs_dict['''input_ids''']

					__UpperCAmelCase    :     Dict													=	input_ids[:1, :]
					__UpperCAmelCase    :     List[Any]													=	inputs_dict['''attention_mask'''][:1, :]
					__UpperCAmelCase    :     List[str]													=	inputs_dict['''head_mask''']
					__UpperCAmelCase    :     Any													=	1

					# first forward pass
					__UpperCAmelCase    :     Union[str, Any]													=	model(a_       ,       attention_mask=a_       ,       head_mask=a_       ,       use_cache=a_ )

					__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					__UpperCAmelCase    :     Dict													=	ids_tensor((self.batch_size, 3)       ,       config.vocab_size )
					__UpperCAmelCase    :     Union[str, Any]													=	tf.cast(ids_tensor((self.batch_size, 3)       ,       2 )       ,       tf.inta )

					# append to next input_ids and
					__UpperCAmelCase    :     Dict													=	tf.concat([input_ids, next_tokens]       ,       axis=-1 )
					__UpperCAmelCase    :     List[str]													=	tf.concat([attention_mask, next_attn_mask]       ,       axis=-1 )

					__UpperCAmelCase    :     int													=	model(a_       ,       attention_mask=a_ )[0]
					__UpperCAmelCase    :     Union[str, Any]													=	model(a_       ,       attention_mask=a_       ,       past_key_values=a_ )[0]

					self.parent.assertEqual(next_tokens.shape[1]       ,       output_from_past.shape[1] )

					# select random slice
					__UpperCAmelCase    :     Optional[Any]													=	int(ids_tensor((1,)       ,       output_from_past.shape[-1] ) )
					__UpperCAmelCase    :     List[str]													=	output_from_no_past[:, -3:, random_slice_idx]
					__UpperCAmelCase    :     int													=	output_from_past[:, :, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(a_       ,       a_       ,       rtol=1e-3 )

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					str=None ,   _UpperCAmelCase							:					List[str]=None ,   _UpperCAmelCase							:					Optional[Any]=None ,   ):







		'''simple docstring'''





		if attention_mask is None:
				__UpperCAmelCase    :     Tuple													=	tf.cast(tf.math.not_equal(_UpperCAmelCase ,   config.pad_token_id						) ,   tf.inta						)
		if decoder_attention_mask is None:
				__UpperCAmelCase    :     int													=	tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape ,   dtype=tf.inta						),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,   config.pad_token_id						) ,   tf.inta						),
				    ] ,   axis=-1 ,   )
		if head_mask is None:
				__UpperCAmelCase    :     Optional[Any]													=	tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
		if decoder_head_mask is None:
				__UpperCAmelCase    :     Optional[int]													=	tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
		if cross_attn_head_mask is None:
				__UpperCAmelCase    :     Union[str, Any]													=	tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}

@require_tf
class 		UpperCAmelCase__			(			__UpperCamelCase						,__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       (
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			UpperCamelCase									=       (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			UpperCamelCase									=       (
			    {
			        """conversational""": TFBlenderbotSmallForConditionalGeneration,
			        """feature-extraction""": TFBlenderbotSmallModel,
			        """summarization""": TFBlenderbotSmallForConditionalGeneration,
			        """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
			        """translation""": TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCamelCase									=       True
			UpperCamelCase									=       False
			UpperCamelCase									=       False




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	TFBlenderbotSmallModelTester(self )
					__UpperCAmelCase    :     Optional[int]													=	ConfigTester(self       ,       config_class=a_ )




			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					self.config_tester.run_common_tests()




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	self.model_tester.prepare_config_and_inputs_for_common()
					self.model_tester.check_decoder_model_past_large_inputs(*a_ )

@require_tokenizers
@require_tf
class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       [
			    """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
			    """  i'm going to throw up.\nand why is that?"""
			]
			UpperCamelCase									=       """facebook/blenderbot_small-90M"""




			@cached_property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )




			@cached_property
			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
					return model




			@slow
			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.tokenizer(self.src_text       ,       return_tensors='''tf''' )
					__UpperCAmelCase    :     Optional[Any]													=	self.model.generate(
					    model_inputs.input_ids       ,       attention_mask=model_inputs.attention_mask       ,       num_beams=2       ,       use_cache=a_       ,       )
					__UpperCAmelCase    :     List[str]													=	self.tokenizer.batch_decode(generated_ids.numpy()       ,       skip_special_tokens=a_ )[0]
					assert generated_words in (
					    "i don't know. i just feel like i'm going to throw up. it's not fun.",
					    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
					    "i'm not sure. i just feel like i've been in a bad situation.",
					)










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,
)


__A   			=getLogger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					int = 10_24 ,   _UpperCAmelCase							:					Any="val" ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Union[str, Any]="summarization" ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Union[str, Any]=1 ,   _UpperCAmelCase							:					Dict = None ,   _UpperCAmelCase							:					Dict="" ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	str(_UpperCAmelCase						)
		assert local_rank is not None
		torch.distributed.init_process_group(backend='''nccl''' ,   rank=_UpperCAmelCase						)

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

		__UpperCAmelCase    :     Tuple													=	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:
				__UpperCAmelCase    :     Optional[Any]													=	tokenizer.model_max_length
		if prefix is None:
				__UpperCAmelCase    :     str													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		__UpperCAmelCase    :     Union[str, Any]													=	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.
		__UpperCAmelCase    :     str													=	ds.make_sortish_sampler(_UpperCAmelCase ,   distributed=_UpperCAmelCase ,   add_extra_examples=_UpperCAmelCase ,   shuffle=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	DataLoader(_UpperCAmelCase ,   sampler=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   collate_fn=ds.collate_fn						)
		__UpperCAmelCase    :     List[Any]													=	[]
		for batch in tqdm(_UpperCAmelCase						):
				__UpperCAmelCase    :     str													=	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 ,   )
				__UpperCAmelCase    :     Optional[int]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	batch['''ids''']
				if num_return_sequences > 1:
						__UpperCAmelCase    :     Any													=	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        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	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'''						)
		__UpperCAmelCase    :     Any													=	time.time()
		__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	parser.parse_known_args()
		__UpperCAmelCase    :     List[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}'						)
		__UpperCAmelCase    :     Union[str, Any]													=	Path(args.save_dir + '''_tmp'''						)
		Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)  # this handles locking.
		__UpperCAmelCase    :     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.
		__UpperCAmelCase    :     List[Any]													=	{}
		if args.src_lang is not None:
				__UpperCAmelCase    :     List[str]													=	args.src_lang
		if args.tgt_lang is not None:
				__UpperCAmelCase    :     List[Any]													=	args.tgt_lang

		Path(args.save_dir						).mkdir(exist_ok=_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	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:
				__UpperCAmelCase    :     int													=	Path(args.save_dir						)
				save_dir.mkdir(exist_ok=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	gather_results_from_each_node(_UpperCAmelCase ,   _UpperCAmelCase ,   args.sync_timeout						)
				__UpperCAmelCase    :     List[Any]													=	combine_partial_results(_UpperCAmelCase						)
				if args.num_return_sequences > 1:
						__UpperCAmelCase    :     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
				__UpperCAmelCase    :     str													=	Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
				with open(_UpperCAmelCase						) as f:
						__UpperCAmelCase    :     int													=	[x.rstrip() for x in f.readlines()][: len(_UpperCAmelCase						)]

				# Calculate metrics, save metrics,  and save _generations.txt
				__UpperCAmelCase    :     Optional[Any]													=	'''translation''' in args.task
				__UpperCAmelCase    :     Optional[int]													=	calculate_bleu if calc_bleu else calculate_rouge
				__UpperCAmelCase    :     Union[str, Any]													=	'''bleu''' if calc_bleu else '''rouge'''
				__UpperCAmelCase    :     Dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	time.time() - start_time
				__UpperCAmelCase    :     List[str]													=	round(runtime / metrics['''n_obs'''] ,   4						)
				__UpperCAmelCase    :     List[str]													=	num_replicas
				# TODO(@stas00): add whatever metadata to metrics
				__UpperCAmelCase    :     List[Any]													=	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        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[]
		for partial_result in partial_results:
				records.extend(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x["id"]						)
		__UpperCAmelCase    :     Union[str, Any]													=	[x['''pred'''] for x in records]
		return preds

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	time.time()
		logger.info('''waiting for all nodes to finish'''						)
		__UpperCAmelCase    :     Any													=	None
		while (time.time() - start_wait) < timeout:
				__UpperCAmelCase    :     List[Any]													=	list(save_dir.glob('''rank_*.json'''						)						)
				if len(_UpperCAmelCase						) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						__UpperCAmelCase    :     Union[str, Any]													=	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()





