


'''simple docstring'''







import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import 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 (
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
           NystromformerModel,
       )
       from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
class 						UpperCamelCase__       :



     """simple docstring"""






     def __init__(			self   ,						snake_case__   ,						snake_case__=13   ,						snake_case__=7   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=99   ,						snake_case__=32   ,						snake_case__=5   ,						snake_case__=4   ,						snake_case__=37   ,						snake_case__="gelu"   ,						snake_case__=0.1   ,						snake_case__=0.1   ,						snake_case__=512   ,						snake_case__=16   ,						snake_case__=2   ,						snake_case__=0.02   ,						snake_case__=3   ,						snake_case__=4   ,						snake_case__=None   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							parent
            _lowerCAmelCase     :				Optional[int]       =							batch_size
            _lowerCAmelCase     :				Optional[int]       =							seq_length
            _lowerCAmelCase     :				List[str]       =							is_training
            _lowerCAmelCase     :				Any       =							use_input_mask
            _lowerCAmelCase     :				str       =							use_token_type_ids
            _lowerCAmelCase     :				List[Any]       =							use_labels
            _lowerCAmelCase     :				List[Any]       =							vocab_size
            _lowerCAmelCase     :				Union[str, Any]       =							hidden_size
            _lowerCAmelCase     :				Any       =							num_hidden_layers
            _lowerCAmelCase     :				Any       =							num_attention_heads
            _lowerCAmelCase     :				List[str]       =							intermediate_size
            _lowerCAmelCase     :				Union[str, Any]       =							hidden_act
            _lowerCAmelCase     :				Any       =							hidden_dropout_prob
            _lowerCAmelCase     :				Any       =							attention_probs_dropout_prob
            _lowerCAmelCase     :				List[Any]       =							max_position_embeddings
            _lowerCAmelCase     :				Optional[int]       =							type_vocab_size
            _lowerCAmelCase     :				Dict       =							type_sequence_label_size
            _lowerCAmelCase     :				int       =							initializer_range
            _lowerCAmelCase     :				int       =							num_labels
            _lowerCAmelCase     :				Any       =							num_choices
            _lowerCAmelCase     :				List[str]       =							scope







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size )

            _lowerCAmelCase     :				Union[str, Any]       =							None
            if self.use_input_mask:
                   _lowerCAmelCase     :				Optional[int]       =							random_attention_mask([self.batch_size, self.seq_length] )

            _lowerCAmelCase     :				Optional[int]       =							None
            if self.use_token_type_ids:
                   _lowerCAmelCase     :				Any       =							ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size )

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

            _lowerCAmelCase     :				Optional[Any]       =							self.get_config()

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







     def a						(			self ):

            '''simple docstring'''

            return NystromformerConfig(
                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   ,						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=__UpperCAmelCase   ,						initializer_range=self.initializer_range   ,						)







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							NystromformerModel(config=__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            _lowerCAmelCase     :				List[Any]       =							model(__UpperCAmelCase   ,						attention_mask=__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase )
            _lowerCAmelCase     :				List[str]       =							model(__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase )
            _lowerCAmelCase     :				Tuple       =							model(__UpperCAmelCase )
            self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							NystromformerForMaskedLM(config=__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            _lowerCAmelCase     :				List[Any]       =							model(__UpperCAmelCase   ,						attention_mask=__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase   ,						labels=__UpperCAmelCase )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							NystromformerForQuestionAnswering(config=__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            _lowerCAmelCase     :				Any       =							model(
                __UpperCAmelCase   ,						attention_mask=__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase   ,						start_positions=__UpperCAmelCase   ,						end_positions=__UpperCAmelCase   ,						)
            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   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.num_labels
            _lowerCAmelCase     :				int       =							NystromformerForSequenceClassification(__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            _lowerCAmelCase     :				Optional[Any]       =							model(__UpperCAmelCase   ,						attention_mask=__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase   ,						labels=__UpperCAmelCase )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.num_labels
            _lowerCAmelCase     :				Union[str, Any]       =							NystromformerForTokenClassification(config=__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            _lowerCAmelCase     :				int       =							model(__UpperCAmelCase   ,						attention_mask=__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase   ,						labels=__UpperCAmelCase )
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels) )







     def a						(			self   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.num_choices
            _lowerCAmelCase     :				List[str]       =							NystromformerForMultipleChoice(config=__UpperCAmelCase )
            model.to(__UpperCAmelCase )
            model.eval()
            _lowerCAmelCase     :				Optional[int]       =							input_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				Optional[int]       =							token_type_ids.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				int       =							input_mask.unsqueeze(1 ).expand(-1   ,						self.num_choices   ,						-1 ).contiguous()
            _lowerCAmelCase     :				List[str]       =							model(
                __UpperCAmelCase   ,						attention_mask=__UpperCAmelCase   ,						token_type_ids=__UpperCAmelCase   ,						labels=__UpperCAmelCase   ,						)
            self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_choices) )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.prepare_config_and_inputs()
            (
                _lowerCAmelCase   
            )     :				List[str]       =							config_and_inputs
            _lowerCAmelCase     :				Optional[int]       =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
            return config, inputs_dict
@require_torch
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_     ,       SCREAMING_SNAKE_CASE_     ,       unittest.TestCase						):



     """simple docstring"""






     __magic_name__           =     (
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     __magic_name__           =     (
         {
             "feature-extraction": NystromformerModel,
             "fill-mask": NystromformerForMaskedLM,
             "question-answering": NystromformerForQuestionAnswering,
             "text-classification": NystromformerForSequenceClassification,
             "token-classification": NystromformerForTokenClassification,
             "zero-shot": NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     __magic_name__           =     False
     __magic_name__           =     False







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							NystromformerModelTester(self )
            _lowerCAmelCase     :				Any       =							ConfigTester(self   ,						config_class=__UpperCAmelCase   ,						hidden_size=37 )







     def a						(			self ):

            '''simple docstring'''

            self.config_tester.run_common_tests()







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__UpperCAmelCase )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                   _lowerCAmelCase     :				Dict       =							type
                   self.model_tester.create_and_check_model(*__UpperCAmelCase )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Dict       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_multiple_choice(*__UpperCAmelCase )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__UpperCAmelCase )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__UpperCAmelCase )







     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[Any]       =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__UpperCAmelCase )







     @slow
     def a						(			self ):

            '''simple docstring'''

            for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _lowerCAmelCase     :				Any       =							NystromformerModel.from_pretrained(__UpperCAmelCase )
                   self.assertIsNotNone(__UpperCAmelCase )
@require_torch
class 						UpperCamelCase__       (			unittest.TestCase						):



     """simple docstring"""






     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							NystromformerModel.from_pretrained('uw-madison/nystromformer-512' )
            _lowerCAmelCase     :				List[Any]       =							torch.tensor([[0, 1, 2, 3, 4, 5]] )

            with torch.no_grad():
                   _lowerCAmelCase     :				int       =							model(__UpperCAmelCase )[0]

            _lowerCAmelCase     :				Tuple       =							torch.Size((1, 6, 768) )
            self.assertEqual(output.shape   ,						__UpperCAmelCase )

            _lowerCAmelCase     :				Dict       =							torch.tensor(
                [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]] )

            self.assertTrue(torch.allclose(output[:, :3, :3]   ,						__UpperCAmelCase   ,						atol=1E-4 ) )







     @slow
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				Any       =							"the [MASK] of Belgium is Brussels"

            _lowerCAmelCase     :				List[Any]       =							AutoTokenizer.from_pretrained('uw-madison/nystromformer-512' )
            _lowerCAmelCase     :				Tuple       =							NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512' )

            _lowerCAmelCase     :				Dict       =							tokenizer(__UpperCAmelCase   ,						return_tensors='pt' )

            with torch.no_grad():
                   _lowerCAmelCase     :				Dict       =							model(encoding.input_ids ).logits

            _lowerCAmelCase     :				Optional[Any]       =							token_logits[:, 2, :].argmax(-1 )[0]

            self.assertEqual(tokenizer.decode(__UpperCAmelCase )   ,						'capital' )






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()


