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__						:						Tuple		  =			getLogger(__name__)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase = 8      ,    lowerCamelCase = 1_0_2_4      ,    lowerCamelCase="val"      ,    lowerCamelCase=None      ,    lowerCamelCase=False      ,    lowerCamelCase="summarization"      ,    lowerCamelCase=None      ,    lowerCamelCase=1      ,    lowerCamelCase = None      ,    lowerCamelCase=""      ,    **lowerCamelCase      ,    ):
				UpperCAmelCase__    =							str(snake_case_ )
				assert local_rank is not None
				torch.distributed.init_process_group(backend='nccl'      ,    rank=snake_case_ )

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

				UpperCAmelCase__    =							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:
								UpperCAmelCase__    =							tokenizer.model_max_length
				if prefix is None:
								UpperCAmelCase__    =							prefix or getattr(model.config      ,    'prefix'      ,    '' ) or ''''''
				UpperCAmelCase__    =							SeqaSeqDataset(
				    snake_case_      ,    snake_case_      ,    snake_case_      ,    max_target_length=1_0_2_4      ,    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.
				UpperCAmelCase__    =							ds.make_sortish_sampler(snake_case_      ,    distributed=snake_case_      ,    add_extra_examples=snake_case_      ,    shuffle=snake_case_ )
				UpperCAmelCase__    =							DataLoader(snake_case_      ,    sampler=snake_case_      ,    batch_size=snake_case_      ,    collate_fn=ds.collate_fn )
				UpperCAmelCase__    =							[]
				for batch in tqdm(snake_case_ ):
								UpperCAmelCase__    =							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_      ,    )
								UpperCAmelCase__    =							tokenizer.batch_decode(snake_case_      ,    skip_special_tokens=snake_case_      ,    clean_up_tokenization_spaces=snake_case_ )
								UpperCAmelCase__    =							batch['''ids''']
								if num_return_sequences > 1:
												UpperCAmelCase__    =							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 						a_		(      ):
				UpperCAmelCase__    =							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=6_0_0      ,    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' )
				UpperCAmelCase__    =							time.time()
				UpperCAmelCase__    =							parser.parse_known_args()
				UpperCAmelCase__    =							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}''' )
				UpperCAmelCase__    =							Path(args.save_dir + '_tmp' )
				Path(snake_case_ ).mkdir(exist_ok=snake_case_ )  # this handles locking.
				UpperCAmelCase__    =							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__    =							{}
				if args.src_lang is not None:
								UpperCAmelCase__    =							args.src_lang
				if args.tgt_lang is not None:
								UpperCAmelCase__    =							args.tgt_lang

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

								# Calculate metrics, save metrics,  and save _generations.txt
								UpperCAmelCase__    =							'''translation''' in args.task
								UpperCAmelCase__    =							calculate_bleu if calc_bleu else calculate_rouge
								UpperCAmelCase__    =							'''bleu''' if calc_bleu else '''rouge'''
								UpperCAmelCase__    =							score_fn(snake_case_      ,    snake_case_ )
								UpperCAmelCase__    =							len(snake_case_ )
								UpperCAmelCase__    =							time.time() - start_time
								UpperCAmelCase__    =							round(runtime / metrics['n_obs']      ,    4 )
								UpperCAmelCase__    =							num_replicas
								# TODO(@stas00): add whatever metadata to metrics
								UpperCAmelCase__    =							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 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							[]
				for partial_result in partial_results:
								records.extend(snake_case_ )
				UpperCAmelCase__    =							sorted(snake_case_      ,    key=lambda lowerCamelCase : x["id"] )
				UpperCAmelCase__    =							[x['''pred'''] for x in records]
				return preds



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				# WAIT FOR lots of .json files
				UpperCAmelCase__    =							time.time()
				logger.info('waiting for all nodes to finish' )
				UpperCAmelCase__    =							None
				while (time.time() - start_wait) < timeout:
								UpperCAmelCase__    =							list(save_dir.glob('rank_*.json' ) )
								if len(snake_case_ ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												UpperCAmelCase__    =							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()




"""simple docstring"""






import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase__						:						Any		  =			get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				XLMProphetNetTokenizer
					snake_case__       					=				False
					snake_case__       					=				True







					def 	__lowerCAmelCase		(       self     :			Any    ):
									super().setUp()

									# We have a SentencePiece fixture for testing
									UpperCAmelCase__    =							XLMProphetNetTokenizer(lowerCamelCase__					,keep_accents=lowerCamelCase__    )
									tokenizer.save_pretrained(self.tmpdirname    )







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							'[PAD]'
									UpperCAmelCase__    =							0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__    )					,lowerCamelCase__    )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__    )					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							list(self.get_tokenizer().get_vocab().keys()    )

									self.assertEqual(vocab_keys[0]					,'[PAD]'    )
									self.assertEqual(vocab_keys[1]					,'[CLS]'    )
									self.assertEqual(vocab_keys[-1]					,'j'    )
									self.assertEqual(len(lowerCamelCase__    )					,1_012    )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									self.assertEqual(self.get_tokenizer().vocab_size					,1_012    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							XLMProphetNetTokenizer(lowerCamelCase__					,keep_accents=lowerCamelCase__    )

									UpperCAmelCase__    =							tokenizer.tokenize('This is a test'    )
									self.assertListEqual(lowerCamelCase__					,['▁This', '▁is', '▁a', '▁t', 'est']    )

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(lowerCamelCase__    )					,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,)

									UpperCAmelCase__    =							tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
									self.assertListEqual(
									    lowerCamelCase__					,[
									        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',
									        '.',
									    ]					,)
									UpperCAmelCase__    =							tokenizer.convert_tokens_to_ids(lowerCamelCase__    )
									self.assertListEqual(
									    lowerCamelCase__					,[
									        value + tokenizer.fairseq_offset
									        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
									    ]					,)

									UpperCAmelCase__    =							tokenizer.convert_ids_to_tokens(lowerCamelCase__    )
									self.assertListEqual(
									    lowerCamelCase__					,[
									        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]',
									        '.',
									    ]					,)







					@cached_property
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							'Hello World!'
									UpperCAmelCase__    =							[35_389, 6_672, 49, 2]
									self.assertListEqual(lowerCamelCase__					,self.big_tokenizer.encode(lowerCamelCase__    )    )







					@slow
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									# fmt: off
									UpperCAmelCase__    =							{'input_ids': [[11_073, 82_783, 18, 26, 82_783, 549, 51_540, 248, 17_209, 1_301, 217, 20, 215_186, 1_325, 147, 17_209, 1_301, 217, 20, 56_370, 53, 122_020, 20, 16_477, 27, 87_355, 4_548, 20, 4_728, 78_392, 17, 159_969, 18, 26, 24_491, 629, 15, 538, 22_704, 5_439, 15, 2_788, 24_491, 9_885, 15, 43_534, 605, 15, 814, 18_403, 33_200, 29, 15, 43_534, 24_458, 12_410, 111, 24_966, 83_669, 9_637, 144_068, 26, 850, 22_346, 27, 147, 24_966, 83_669, 83_490, 26, 39_113, 735, 27, 689, 656, 2_800, 1_339, 4_600, 53, 122_020, 115_785, 34, 816, 1_339, 46_887, 18, 147, 53_905, 1_951, 42_238, 41_170, 17_732, 834, 436, 15, 27_523, 98_733, 217, 147, 5_542, 4_981, 930, 17_347, 16, 2], [20_091, 629, 94, 82_786, 58, 490, 20, 1_528, 84, 53_905, 344, 80_592, 110_128, 18_822, 5_267, 1_306, 62, 152_537, 308, 7_997, 401, 124_427, 549, 35_442, 225, 109, 15_055, 25_748, 147, 7_119, 43_712, 34, 767, 135_366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63_784, 119_466, 17, 147_808, 88_214, 18, 656, 81, 32, 3_296, 10_280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=lowerCamelCase__					,model_name='microsoft/xprophetnet-large-wiki100-cased'					,revision='1acad1643ddd54a44df6a1b797ada8373685d90e'					,)



