





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,
)


_UpperCAmelCase				:       Union[str, Any]	   =				getLogger(__name__)


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase = 8						,	_UpperCAmelCase = 1024						,	_UpperCAmelCase="val"						,	_UpperCAmelCase=None						,	_UpperCAmelCase=False						,	_UpperCAmelCase="summarization"						,	_UpperCAmelCase=None						,	_UpperCAmelCase=1						,	_UpperCAmelCase = None						,	_UpperCAmelCase=""						,	**_UpperCAmelCase						,	)			->     Dict:
    lowerCamelCase__    :      List[str]					 =		str(_UpperCAmelCase )
    assert local_rank is not None
    torch.distributed.init_process_group(backend='nccl'						,	rank=_UpperCAmelCase )

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

    lowerCamelCase__    :      List[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:
        lowerCamelCase__    :      Tuple					 =		tokenizer.model_max_length
    if prefix is None:
        lowerCamelCase__    :      List[str]					 =		prefix or getattr(model.config						,	'prefix'						,	'' ) or ''
    lowerCamelCase__    :      List[str]					 =		SeqaSeqDataset(
        _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	max_target_length=1024						,	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.
    lowerCamelCase__    :      Optional[Any]					 =		ds.make_sortish_sampler(_UpperCAmelCase						,	distributed=_UpperCAmelCase						,	add_extra_examples=_UpperCAmelCase						,	shuffle=_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		DataLoader(_UpperCAmelCase						,	sampler=_UpperCAmelCase						,	batch_size=_UpperCAmelCase						,	collate_fn=ds.collate_fn )
    lowerCamelCase__    :      Dict					 =		[]
    for batch in tqdm(_UpperCAmelCase ):
        lowerCamelCase__    :      Union[str, 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						,	)
        lowerCamelCase__    :      Dict					 =		tokenizer.batch_decode(_UpperCAmelCase						,	skip_special_tokens=_UpperCAmelCase						,	clean_up_tokenization_spaces=_UpperCAmelCase )
        lowerCamelCase__    :      Any					 =		batch['ids']
        if num_return_sequences > 1:
            lowerCamelCase__    :      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      (       )			->     Dict:
    lowerCamelCase__    :      Dict					 =		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=600						,	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' )
    lowerCamelCase__    :      int					 =		time.time()
    lowerCamelCase__ ,							lowerCamelCase__    :      Optional[int]					 =		parser.parse_known_args()
    lowerCamelCase__    :      Union[str, 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}""" )
    lowerCamelCase__    :      Union[str, Any]					 =		Path(args.save_dir + '_tmp' )
    Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )  # this handles locking.
    lowerCamelCase__    :      str					 =		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.
    lowerCamelCase__    :      Any					 =		{}
    if args.src_lang is not None:
        lowerCamelCase__    :      int					 =		args.src_lang
    if args.tgt_lang is not None:
        lowerCamelCase__    :      Union[str, Any]					 =		args.tgt_lang

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

        # Calculate metrics, save metrics,  and save _generations.txt
        lowerCamelCase__    :      Tuple					 =		'translation' in args.task
        lowerCamelCase__    :      List[str]					 =		calculate_bleu if calc_bleu else calculate_rouge
        lowerCamelCase__    :      List[Any]					 =		'bleu' if calc_bleu else 'rouge'
        lowerCamelCase__    :      Dict					 =		score_fn(_UpperCAmelCase						,	_UpperCAmelCase )
        lowerCamelCase__    :      Optional[int]					 =		len(_UpperCAmelCase )
        lowerCamelCase__    :      str					 =		time.time() - start_time
        lowerCamelCase__    :      int					 =		round(runtime / metrics['n_obs']						,	4 )
        lowerCamelCase__    :      List[str]					 =		num_replicas
        # TODO(@stas00): add whatever metadata to metrics
        lowerCamelCase__    :      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:
    lowerCamelCase__    :      Union[str, Any]					 =		[]
    for partial_result in partial_results:
        records.extend(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		sorted(_UpperCAmelCase						,	key=lambda _UpperCAmelCase : x["id"] )
    lowerCamelCase__    :      Union[str, Any]					 =		[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
    lowerCamelCase__    :      Optional[Any]					 =		time.time()
    logger.info('waiting for all nodes to finish' )
    lowerCamelCase__    :      List[str]					 =		None
    while (time.time() - start_wait) < timeout:
        lowerCamelCase__    :      Any					 =		list(save_dir.glob('rank_*.json' ) )
        if len(_UpperCAmelCase ) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            lowerCamelCase__    :      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()






"""simple docstring"""




import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_a      :		Optional[int]           =   get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizer
     _UpperCamelCase							:			List[Any]							=		XGLMTokenizerFast
     _UpperCamelCase							:			Dict							=		True
     _UpperCamelCase							:			Tuple							=		True






     def     __A  (		self  ):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     """<pad>"""
       _lowerCAmelCase						:       List[Any]  			=     1

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






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]				,     """<s>"""  )
       self.assertEqual(vocab_keys[1]				,     """<pad>"""  )
       self.assertEqual(len(a__  )				,     1008  )






     def     __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size				,     1008  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     XGLMTokenizer(a__				,     keep_accents=a__  )

       _lowerCAmelCase						:       Dict  			=     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 [285, 46, 10, 170, 382]]				,     )

       _lowerCAmelCase						:       Any  			=     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""",
               """.""",
           ]				,     )
       _lowerCAmelCase						:       List[str]  			=     tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(
           a__				,     [
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]				,     )

       _lowerCAmelCase						:       Optional[int]  			=     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>""",
               """.""",
           ]				,     )






     @cached_property
     def     __A  (		self  ):
       return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""  )






     def     __A  (		self  ):
       with tempfile.NamedTemporaryFile() as f:
         shutil.copyfile(a__				,     f.name  )
         _lowerCAmelCase						:       Union[str, Any]  			=     XGLMTokenizer(f.name				,     keep_accents=a__  )
         _lowerCAmelCase						:       List[str]  			=     pickle.dumps(a__  )
       pickle.loads(a__  )






     def     __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase						:       List[str]  			=     self.get_tokenizer()
       _lowerCAmelCase						:       Optional[Any]  			=     self.get_rust_tokenizer()

       _lowerCAmelCase						:       Tuple  			=     """I was born in 92000, and this is falsé."""

       _lowerCAmelCase						:       List[Any]  			=     tokenizer.tokenize(a__  )
       _lowerCAmelCase						:       Tuple  			=     rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       _lowerCAmelCase						:       str  			=     rust_tokenizer.encode(a__				,     add_special_tokens=a__  )
       self.assertListEqual(a__				,     a__  )

       _lowerCAmelCase						:       int  			=     self.get_rust_tokenizer()
       _lowerCAmelCase						:       Dict  			=     tokenizer.encode(a__  )
       _lowerCAmelCase						:       List[Any]  			=     rust_tokenizer.encode(a__  )
       self.assertListEqual(a__				,     a__  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     """Hello World!"""
       _lowerCAmelCase						:       Optional[int]  			=     [2, 31227, 4447, 35]

       self.assertListEqual(a__				,     self.big_tokenizer.encode(a__  )  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     (
           """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
           """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
       )
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     [2, 1018, 67, 11, 1988, 2617, 5631, 278, 11, 3407, 48, 71630, 28085, 4, 3234, 157, 13, 6, 5, 6, 4, 3526, 768, 15, 659, 57, 298, 3983, 864, 129, 21, 6, 5, 13675, 377, 652, 7580, 10341, 155, 2817, 422, 1666, 7, 1674, 53, 113, 202277, 17892, 33, 60, 87, 4, 3234, 157, 61, 2667, 52376, 19, 88, 23, 735]
       # fmt: on

       self.assertListEqual(a__				,     self.big_tokenizer.encode(a__  )  )






     @slow
     def     __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:       List[str]  			=     {
           """input_ids""": [[2, 108825, 1163, 15, 88010, 473, 15898, 157, 13672, 1857, 312, 8, 238021, 1163, 53, 13672, 1857, 312, 8, 53283, 182396, 8, 18566, 16, 36733, 4101, 8, 230, 244017, 122553, 7, 15, 132597, 4, 293, 12511, 7610, 4, 3414, 132597, 9, 4, 32361, 362, 4, 734, 28512, 32569, 18, 4, 32361, 26096, 14982, 73, 18715, 21433, 235261, 15, 492, 12427, 16, 53, 18715, 21433, 65454, 15, 23659, 563, 16, 278, 597, 2843, 595, 7931, 182396, 64186, 22, 886, 595, 132981, 53, 25540, 3449, 43982, 39901, 5951, 878, 330, 4, 27694, 80269, 312, 53, 6517, 11780, 611, 20408, 5], [2, 6, 132597, 67, 42897, 33, 592, 8, 163729, 25540, 361, 136997, 109514, 173230, 7, 501, 60, 102913, 196, 5631, 235, 63243, 473, 6, 231757, 74, 5277, 7905, 53, 3095, 37317, 22, 454, 183874, 5], [2, 268, 31298, 46530, 6, 132935, 43831, 7, 597, 32, 24, 3688, 9865, 5]],
           """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, 1, 1, 1, 1, 1]]
       }  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=a__				,     model_name="""facebook/xglm-564M"""				,     padding=a__				,     )




