


"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE :    Any									=				get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

SCREAMING_SNAKE_CASE :    List[Any]									=				25_0004
SCREAMING_SNAKE_CASE :    Dict									=				25_0020



@require_sentencepiece
@require_tokenizers
class 			_UpperCAmelCase (							__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=MBartTokenizer
		lowerCamelCase__				=MBartTokenizerFast
		lowerCamelCase__				=True
		lowerCamelCase__				=True



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().setUp()

			# We have a SentencePiece fixture for testing
			__snake_case	:						Union[str, Any]						=				MBartTokenizer(a_				,       keep_accents=a_					)
			tokenizer.save_pretrained(self.tmpdirname					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				MBartTokenizer(a_				,       keep_accents=a_					)

			__snake_case	:						Optional[int]						=				tokenizer.tokenize('''This is a test'''					)
			self.assertListEqual(a_				,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']					)

			self.assertListEqual(
			    tokenizer.convert_tokens_to_ids(a_					)				,       [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,       )

			__snake_case	:						Optional[int]						=				tokenizer.tokenize('''I was born in 92000, and this is falsé.'''					)
			self.assertListEqual(
			    a_				,       [
			        SPIECE_UNDERLINE + '''I''',
			        SPIECE_UNDERLINE + '''was''',
			        SPIECE_UNDERLINE + '''b''',
			        '''or''',
			        '''n''',
			        SPIECE_UNDERLINE + '''in''',
			        SPIECE_UNDERLINE + '''''',
			        '''9''',
			        '''2''',
			        '''0''',
			        '''0''',
			        '''0''',
			        ''',''',
			        SPIECE_UNDERLINE + '''and''',
			        SPIECE_UNDERLINE + '''this''',
			        SPIECE_UNDERLINE + '''is''',
			        SPIECE_UNDERLINE + '''f''',
			        '''al''',
			        '''s''',
			        '''é''',
			        '''.''',
			    ]				,       )
			__snake_case	:						List[Any]						=				tokenizer.convert_tokens_to_ids(a_					)
			self.assertListEqual(
			    a_				,       [
			        value + tokenizer.fairseq_offset
			        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
			        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
			    ]				,       )

			__snake_case	:						Optional[Any]						=				tokenizer.convert_ids_to_tokens(a_					)
			self.assertListEqual(
			    a_				,       [
			        SPIECE_UNDERLINE + '''I''',
			        SPIECE_UNDERLINE + '''was''',
			        SPIECE_UNDERLINE + '''b''',
			        '''or''',
			        '''n''',
			        SPIECE_UNDERLINE + '''in''',
			        SPIECE_UNDERLINE + '''''',
			        '''<unk>''',
			        '''2''',
			        '''0''',
			        '''0''',
			        '''0''',
			        ''',''',
			        SPIECE_UNDERLINE + '''and''',
			        SPIECE_UNDERLINE + '''this''',
			        SPIECE_UNDERLINE + '''is''',
			        SPIECE_UNDERLINE + '''f''',
			        '''al''',
			        '''s''',
			        '''<unk>''',
			        '''.''',
			    ]				,       )



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			if not self.test_slow_tokenizer:
				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
				return

			__snake_case	:						List[Any]						=				(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
				with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
					__snake_case	:						Tuple						=				self.rust_tokenizer_class.from_pretrained(a_				,       **a_					)
					__snake_case	:						List[str]						=				self.tokenizer_class.from_pretrained(a_				,       **a_					)

					__snake_case	:						Any						=				tempfile.mkdtemp()

					__snake_case	:						List[Any]						=				tokenizer_r.save_pretrained(a_					)
					__snake_case	:						Union[str, Any]						=				tokenizer_p.save_pretrained(a_					)

					# Checks it save with the same files + the tokenizer.json file for the fast one
					self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files					)					)
					__snake_case	:						int						=				tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f					)
					self.assertSequenceEqual(a_				,       a_					)

					# Checks everything loads correctly in the same way
					__snake_case	:						int						=				tokenizer_r.from_pretrained(a_					)
					__snake_case	:						Optional[Any]						=				tokenizer_p.from_pretrained(a_					)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(a_				,       a_					)					)
						# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
						# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

					shutil.rmtree(a_					)

					# Save tokenizer rust, legacy_format=True
					__snake_case	:						Any						=				tempfile.mkdtemp()

					__snake_case	:						Tuple						=				tokenizer_r.save_pretrained(a_				,       legacy_format=a_					)
					__snake_case	:						Union[str, Any]						=				tokenizer_p.save_pretrained(a_					)

					# Checks it save with the same files
					self.assertSequenceEqual(a_				,       a_					)

					# Checks everything loads correctly in the same way
					__snake_case	:						Any						=				tokenizer_r.from_pretrained(a_					)
					__snake_case	:						Any						=				tokenizer_p.from_pretrained(a_					)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(a_				,       a_					)					)

					shutil.rmtree(a_					)

					# Save tokenizer rust, legacy_format=False
					__snake_case	:						Union[str, Any]						=				tempfile.mkdtemp()

					__snake_case	:						str						=				tokenizer_r.save_pretrained(a_				,       legacy_format=a_					)
					__snake_case	:						Dict						=				tokenizer_p.save_pretrained(a_					)

					# Checks it saved the tokenizer.json file
					self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files					)					)

					# Checks everything loads correctly in the same way
					__snake_case	:						List[str]						=				tokenizer_r.from_pretrained(a_					)
					__snake_case	:						str						=				tokenizer_p.from_pretrained(a_					)

					# Check special tokens are set accordingly on Rust and Python
					for key in tokenizer_pp.special_tokens_map:
						self.assertTrue(hasattr(a_				,       a_					)					)

					shutil.rmtree(a_					)



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






		'''simple docstring'''

		lowerCamelCase__				='facebook/mbart-large-en-ro'
		lowerCamelCase__				=[
		    ' UN Chief Says There Is No Military Solution in Syria',
		    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
		]
		lowerCamelCase__				=[
		    'Şeful ONU declară că nu există o soluţie militară în Siria',
		    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
		    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
		    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
		]
		lowerCamelCase__				=[8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2, EN_CODE]



		@classmethod
		def 				SCREAMING_SNAKE_CASE  (cls					):





			'''simple docstring'''

			__snake_case	:						MBartTokenizer						=				MBartTokenizer.from_pretrained(
			    cls.checkpoint_name				,       src_lang='''en_XX'''				,       tgt_lang='''ro_RO'''					)
			__snake_case	:						int						=				1
			return cls



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']				,       25_00_01					)
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']				,       25_00_04					)
			self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']				,       25_00_20					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
			self.assertListEqual(self.expected_src_tokens				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.assertIn(a_				,       self.tokenizer.all_special_ids					)
			__snake_case	:						List[str]						=				[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
			__snake_case	:						Any						=				self.tokenizer.decode(a_				,       skip_special_tokens=a_					)
			__snake_case	:						Optional[int]						=				self.tokenizer.decode(generated_ids[1:]				,       skip_special_tokens=a_					)
			self.assertEqual(a_				,       a_					)
			self.assertNotIn(self.tokenizer.eos_token				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				['''this is gunna be a long sentence ''' * 20]
			assert isinstance(src_text[0]				,       a_					)
			__snake_case	:						Tuple						=				10
			__snake_case	:						Optional[Any]						=				self.tokenizer(a_				,       max_length=a_				,       truncation=a_					).input_ids[0]
			self.assertEqual(ids[-2]				,       2					)
			self.assertEqual(ids[-1]				,       a_					)
			self.assertEqual(len(a_					)				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']					)				,       [25_00_26, 25_00_01]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				tempfile.mkdtemp()
			__snake_case	:						List[str]						=				self.tokenizer.fairseq_tokens_to_ids
			self.tokenizer.save_pretrained(a_					)
			__snake_case	:						List[Any]						=				MBartTokenizer.from_pretrained(a_					)
			self.assertDictEqual(new_tok.fairseq_tokens_to_ids				,       a_					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				self.tokenizer(self.src_text				,       text_target=self.tgt_text				,       padding=a_				,       return_tensors='''pt'''					)
			__snake_case	:						Union[str, Any]						=				shift_tokens_right(batch['''labels''']				,       self.tokenizer.pad_token_id					)

			# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
			assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
			assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
			assert batch.decoder_input_ids[1][-1] == 2
			assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				self.tokenizer(
			    self.src_text				,       text_target=self.tgt_text				,       padding=a_				,       truncation=a_				,       max_length=len(self.expected_src_tokens					)				,       return_tensors='''pt'''				,       )

			__snake_case	:						int						=				shift_tokens_right(batch['''labels''']				,       self.tokenizer.pad_token_id					)

			self.assertIsInstance(a_				,       a_					)

			self.assertEqual((2, 14)				,       batch.input_ids.shape					)
			self.assertEqual((2, 14)				,       batch.attention_mask.shape					)
			__snake_case	:						Tuple						=				batch.input_ids.tolist()[0]
			self.assertListEqual(self.expected_src_tokens				,       a_					)
			self.assertEqual(2				,       batch.decoder_input_ids[0, -1]					)  # EOS
			# Test that special tokens are reset
			self.assertEqual(self.tokenizer.prefix_tokens				,       []					)
			self.assertEqual(self.tokenizer.suffix_tokens				,       [self.tokenizer.eos_token_id, EN_CODE]					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						int						=				self.tokenizer(self.src_text				,       padding=a_				,       truncation=a_				,       max_length=3				,       return_tensors='''pt'''					)
			__snake_case	:						int						=				self.tokenizer(
			    text_target=self.tgt_text				,       padding=a_				,       truncation=a_				,       max_length=10				,       return_tensors='''pt'''					)
			__snake_case	:						Any						=				targets['''input_ids''']
			__snake_case	:						List[str]						=				shift_tokens_right(a_				,       self.tokenizer.pad_token_id					)

			self.assertEqual(batch.input_ids.shape[1]				,       3					)
			self.assertEqual(batch.decoder_input_ids.shape[1]				,       10					)



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						List[Any]						=				self.tokenizer._build_translation_inputs(
			    '''A test'''				,       return_tensors='''pt'''				,       src_lang='''en_XX'''				,       tgt_lang='''ar_AR'''					)

			self.assertEqual(
			    nested_simplify(a_					)				,       {
			        # A, test, EOS, en_XX
			        '''input_ids''': [[62, 30_34, 2, 25_00_04]],
			        '''attention_mask''': [[1, 1, 1, 1]],
			        # ar_AR
			        '''forced_bos_token_id''': 25_00_01,
			    }				,       )









"""simple docstring"""
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,
)


SCREAMING_SNAKE_CASE :    List[str]									=				getLogger(__name__)






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      str     ,   _snake_case						:      str     ,   _snake_case						:      int = 8     ,   _snake_case						:      int = 1_024     ,   _snake_case						:      Any="val"     ,   _snake_case						:      Tuple=None     ,   _snake_case						:      Any=False     ,   _snake_case						:      str="summarization"     ,   _snake_case						:      Dict=None     ,   _snake_case						:      Optional[Any]=1     ,   _snake_case						:      Dict = None     ,   _snake_case						:      List[Any]=""     ,   **_snake_case						:      int     ,   )     ->Dict:
	"""simple docstring"""





	__snake_case	:						int						=				str(_snake_case     )
	assert local_rank is not None
	torch.distributed.init_process_group(backend='''nccl'''     ,   rank=_snake_case     )

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

	__snake_case	:						Dict						=				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:
		__snake_case	:						List[str]						=				tokenizer.model_max_length
	if prefix is None:
		__snake_case	:						List[str]						=				prefix or getattr(model.config     ,   '''prefix'''     ,   ''''''     ) or ''''''
	__snake_case	:						List[str]						=				SeqaSeqDataset(
	    _snake_case     ,   _snake_case     ,   _snake_case     ,   max_target_length=1_024     ,   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.
	__snake_case	:						Union[str, Any]						=				ds.make_sortish_sampler(_snake_case     ,   distributed=_snake_case     ,   add_extra_examples=_snake_case     ,   shuffle=_snake_case     )
	__snake_case	:						List[Any]						=				DataLoader(_snake_case     ,   sampler=_snake_case     ,   batch_size=_snake_case     ,   collate_fn=ds.collate_fn     )
	__snake_case	:						Union[str, Any]						=				[]
	for batch in tqdm(_snake_case     ):
		__snake_case	:						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     ,   )
		__snake_case	:						List[Any]						=				tokenizer.batch_decode(_snake_case     ,   skip_special_tokens=_snake_case     ,   clean_up_tokenization_spaces=_snake_case     )
		__snake_case	:						List[str]						=				batch['''ids''']
		if num_return_sequences > 1:
			__snake_case	:						Dict						=				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 		lowercase							(     )     ->int:
	"""simple docstring"""





	__snake_case	:						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=_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'''     )
	__snake_case	:						str						=				time.time()
	__snake_case	,							__snake_case	:						Any						=				parser.parse_known_args()
	__snake_case	:						List[Any]						=				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}"""     )
	__snake_case	:						List[Any]						=				Path(args.save_dir + '''_tmp'''     )
	Path(_snake_case     ).mkdir(exist_ok=_snake_case     )  # this handles locking.
	__snake_case	:						Optional[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.
	__snake_case	:						Dict						=				{}
	if args.src_lang is not None:
		__snake_case	:						Dict						=				args.src_lang
	if args.tgt_lang is not None:
		__snake_case	:						Dict						=				args.tgt_lang

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

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





	__snake_case	:						List[Any]						=				[]
	for partial_result in partial_results:
		records.extend(_snake_case     )
	__snake_case	:						List[str]						=				sorted(_snake_case     ,   key=lambda _snake_case     : x["id"]     )
	__snake_case	:						Tuple						=				[x['''pred'''] for x in records]
	return preds






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      List[str]     ,   _snake_case						:      List[Any]     )     ->List[Dict[str, List]]:
	"""simple docstring"""





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





