
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_A   =			getLogger(__name__)



def 						lowercase_      (       A__			,		A__			,		A__			,		A__ = 8			,		A__ = 1024			,		A__="val"			,		A__=None			,		A__=False			,		A__="summarization"			,		A__=None			,		A__=1			,		A__ = None			,		A__=""			,		**A__			,		)							->							Dict:
    """simple docstring"""

    snake_case								= str(A__		)
    assert local_rank is not None
    torch.distributed.init_process_group(backend="nccl"			,		rank=A__		)

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

    snake_case								= AutoTokenizer.from_pretrained(A__		)
    logger.info(F'Inferred tokenizer type: {tokenizer.__class__}'		)  # if this is wrong, check config.model_type.

    if max_source_length is None:
        snake_case								= tokenizer.model_max_length
    if prefix is None:
        snake_case								= prefix or getattr(model.config			,		"prefix"			,		""		) or ""
    snake_case								= SeqaSeqDataset(
        A__			,		A__			,		A__			,		max_target_length=1024			,		type_path=A__			,		n_obs=A__			,		prefix=A__			,		**A__			,		)
    # 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								= ds.make_sortish_sampler(A__			,		distributed=A__			,		add_extra_examples=A__			,		shuffle=A__		)
    snake_case								= DataLoader(A__			,		sampler=A__			,		batch_size=A__			,		collate_fn=ds.collate_fn		)
    snake_case								= []
    for batch in tqdm(A__		):
        snake_case								= model.generate(
            input_ids=batch["input_ids"].to(model.device		)			,		attention_mask=batch["attention_mask"].to(model.device		)			,		num_return_sequences=A__			,		num_beams=A__			,		**A__			,		)
        snake_case								= tokenizer.batch_decode(A__			,		skip_special_tokens=A__			,		clean_up_tokenization_spaces=A__		)
        snake_case								= batch["ids"]
        if num_return_sequences > 1:
            snake_case								= chunks(A__			,		A__		)  # batch size chunks, each of size num_return_seq
        for i, pred in enumerate(A__		):
            results.append({"pred": pred, "id": ids[i].item()}		)
    save_json(A__			,		A__		)
    return results, sampler.num_replicas



def 						lowercase_      (       )							->							Optional[Any]:
    """simple docstring"""

    snake_case								= 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=A__			,		help="like cnn_dm/test.source"		)
    parser.add_argument(
        "--model_name"			,		type=A__			,		help="like facebook/bart-large-cnn,t5-base, etc."			,		default="sshleifer/distilbart-xsum-12-3"			,		)
    parser.add_argument("--save_dir"			,		type=A__			,		help="where to save"			,		default="tmp_gen"		)
    parser.add_argument("--max_source_length"			,		type=A__			,		default=A__		)
    parser.add_argument(
        "--type_path"			,		type=A__			,		default="test"			,		help="which subset to evaluate typically train/val/test"		)
    parser.add_argument("--task"			,		type=A__			,		default="summarization"			,		help="used for task_specific_params + metrics"		)
    parser.add_argument("--bs"			,		type=A__			,		default=8			,		required=A__			,		help="batch size"		)
    parser.add_argument(
        "--local_rank"			,		type=A__			,		default=-1			,		required=A__			,		help="should be passed by distributed.launch"		)

    parser.add_argument(
        "--n_obs"			,		type=A__			,		default=A__			,		required=A__			,		help="How many observations. Defaults to all."		)
    parser.add_argument(
        "--num_return_sequences"			,		type=A__			,		default=1			,		required=A__			,		help="How many sequences to return"		)
    parser.add_argument(
        "--sync_timeout"			,		type=A__			,		default=600			,		required=A__			,		help="How long should master process wait for other processes to finish."			,		)
    parser.add_argument("--src_lang"			,		type=A__			,		default=A__			,		required=A__		)
    parser.add_argument("--tgt_lang"			,		type=A__			,		default=A__			,		required=A__		)
    parser.add_argument(
        "--prefix"			,		type=A__			,		required=A__			,		default=A__			,		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								= time.time()
    snake_case					,							snake_case								= parser.parse_known_args()
    snake_case								= parse_numeric_n_bool_cl_kwargs(A__		)
    if generate_kwargs and args.local_rank <= 0:
        print(F'parsed the following generate kwargs: {generate_kwargs}'		)
    snake_case								= Path(args.save_dir + "_tmp"		)
    Path(A__		).mkdir(exist_ok=A__		)  # this handles locking.
    snake_case								= 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								= {}
    if args.src_lang is not None:
        snake_case								= args.src_lang
    if args.tgt_lang is not None:
        snake_case								= args.tgt_lang

    Path(args.save_dir		).mkdir(exist_ok=A__		)
    snake_case					,							snake_case								= eval_data_dir(
        args.data_dir			,		A__			,		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=A__			,		**A__			,		)

    if args.local_rank <= 0:
        snake_case								= Path(args.save_dir		)
        save_dir.mkdir(exist_ok=A__		)
        snake_case								= gather_results_from_each_node(A__			,		A__			,		args.sync_timeout		)
        snake_case								= combine_partial_results(A__		)
        if args.num_return_sequences > 1:
            snake_case								= save_dir.joinpath("pseudolabel_results.json"		)
            print(F'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'		)
            save_json(A__			,		A__		)
            return
        snake_case								= Path(args.data_dir		).joinpath(args.type_path + ".target"		)
        with open(A__		) as f:
            snake_case								= [x.rstrip() for x in f.readlines()][: len(A__		)]

        # Calculate metrics, save metrics,  and save _generations.txt
        snake_case								= "translation" in args.task
        snake_case								= calculate_bleu if calc_bleu else calculate_rouge
        snake_case								= "bleu" if calc_bleu else "rouge"
        snake_case								= score_fn(A__			,		A__		)
        snake_case								= len(A__		)
        snake_case								= time.time() - start_time
        snake_case								= round(runtime / metrics["n_obs"]			,		4		)
        snake_case								= num_replicas
        # TODO(@stas00): add whatever metadata to metrics
        snake_case								= save_dir.joinpath(F'{args.type_path}_{metric_name}.json'		)
        save_json(A__			,		A__			,		indent=A__		)
        print(A__		)
        write_txt_file(A__			,		save_dir.joinpath(F'{args.type_path}_generations.txt'		)		)
        if args.debug:
            write_txt_file(A__			,		save_dir.joinpath(F'{args.type_path}.target'		)		)
        else:
            shutil.rmtree(A__		)



def 						lowercase_      (       A__		)							->							List:
    """simple docstring"""

    snake_case								= []
    for partial_result in partial_results:
        records.extend(A__		)
    snake_case								= sorted(A__			,		key=lambda A__		: x["id"]		)
    snake_case								= [x["pred"] for x in records]
    return preds



def 						lowercase_      (       A__			,		A__			,		A__		)							->							List[Dict[str, List]]:
    """simple docstring"""

    snake_case								= time.time()
    logger.info("waiting for all nodes to finish"		)
    snake_case								= None
    while (time.time() - start_wait) < timeout:
        snake_case								= list(save_dir.glob("rank_*.json"		)		)
        if len(A__		) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            snake_case								= lmap(A__			,		A__		)
            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()



import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_A   =			"bert-base-cased"
_A   =			"google/pegasus-xsum"
_A   =			[" Sam ate lunch today.", "Sams lunch ingredients."]
_A   =			["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_A   =			"patrickvonplaten/t5-tiny-random"
_A   =			"sshleifer/bart-tiny-random"
_A   =			"sshleifer/tiny-mbart"
_A   =			"sshleifer/tiny-marian-en-de"



def 						lowercase_      (       A__			,		A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case								= "\n".join(A__		)
    Path(A__		).open("w"		).writelines(A__		)



def 						lowercase_      (       A__		)							->							List[Any]:
    """simple docstring"""

    for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(A__			,		F'{split}.source'		)			,		A__		)
        _dump_articles(os.path.join(A__			,		F'{split}.target'		)			,		A__		)
    return tmp_dir







class 					lowerCamelCase		(  A_  ):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     @slow
     def 		UpperCAmelCase(self				:   Tuple    ,   _A				:   List[str]       )						->						Optional[int]:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= 8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         snake_case					,							snake_case								= "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   src_lang=_A    ,   tgt_lang=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert isinstance(_A    ,   _A       )
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_src_len
             # show that targets are the same len
             assert batch["labels"].shape[1] == max_tgt_len
             if tok_name != MBART_TINY:
                 continue
             # check language codes in correct place
             snake_case								= shift_tokens_right(batch["labels"]    ,   tokenizer.pad_token_id       )
             assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
             assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
             assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

             break  # No need to test every batch





     @parameterized.expand([BART_TINY, BERT_BASE_CASED]       )
     def 		UpperCAmelCase(self				:   str    ,   _A				:   Dict       )						->						Dict:
         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in ARTICLES       )
         snake_case								= max(len(tokenizer.encode(_A       )       ) for a in SUMMARIES       )
         snake_case								= 4
         snake_case								= LegacySeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=2_0    ,   max_target_length=_A    ,   )
         snake_case								= DataLoader(_A    ,   batch_size=2    ,   collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
             assert batch["attention_mask"].shape == batch["input_ids"].shape
             # show that articles were trimmed.
             assert batch["input_ids"].shape[1] == max_len_source
             assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
             # show that targets were truncated
             assert batch["labels"].shape[1] == trunc_target  # Truncated
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						Optional[Any]:
         snake_case								= AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         snake_case								= tmp_dir.joinpath("train.source"       ).open().readlines()
         snake_case								= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(_A    ,   _A    ,   1_2_8    ,   _A       )
         snake_case								= {x.name for x in tmp_dir.iterdir()}
         snake_case								= {x.name for x in save_dir.iterdir()}
         snake_case								= save_dir.joinpath("train.source"       ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(_A       ) < len(_A       )
         assert len(_A       ) == 1
         assert len(packed_examples[0]       ) == sum(len(_A       ) for x in orig_examples       )
         assert orig_paths == new_paths





     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,   reason="This test requires fairseq"       )
     def 		UpperCAmelCase(self				:   Optional[int]       )						->						Union[str, Any]:
         if not FAIRSEQ_AVAILABLE:
             return
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=6_4       )
         snake_case								= 6_4
         snake_case								= ds.make_dynamic_sampler(_A    ,   required_batch_size_multiple=_A       )
         snake_case								= [len(_A       ) for x in batch_sampler]
         assert len(set(_A       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(_A       ) == len(_A       )  # no dropped or added examples
         snake_case								= DataLoader(_A    ,   batch_sampler=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= []
         snake_case								= []
         for batch in data_loader:
             snake_case								= batch["input_ids"].shape
             snake_case								= src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             snake_case								= np.product(batch["input_ids"].shape       )
             num_src_per_batch.append(_A       )
             if num_src_tokens > (max_tokens * 1.1):
                 failures.append(_A       )
         assert num_src_per_batch[0] == max(_A       )
         if failures:
             raise AssertionError(f'too many tokens in {len(_A       )} batches'       )





     def 		UpperCAmelCase(self				:   int       )						->						str:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset(max_len=5_1_2       )
         snake_case								= 2
         snake_case								= ds.make_sortish_sampler(_A    ,   shuffle=_A       )

         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2       )
         snake_case								= DataLoader(_A    ,   batch_size=_A    ,   collate_fn=ds.collate_fn    ,   num_workers=2    ,   sampler=_A       )

         snake_case								= tokenizer.pad_token_id

         def count_pad_tokens(_A				:   Dict    ,   _A				:   Union[str, Any]="input_ids"       ):
             return [batch[k].eq(_A       ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(_A    ,   k="labels"       )       ) < sum(count_pad_tokens(_A    ,   k="labels"       )       )
         assert sum(count_pad_tokens(_A       )       ) < sum(count_pad_tokens(_A       )       )
         assert len(_A       ) == len(_A       )





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   _A				:   Union[str, Any]=1_0_0_0    ,   _A				:   Optional[int]=1_2_8       )						->						List[Any]:
         if os.getenv("USE_REAL_DATA"    ,   _A       ):
             snake_case								= "examples/seq2seq/wmt_en_ro"
             snake_case								= max_len * 2 * 6_4
             if not Path(_A       ).joinpath("train.len"       ).exists():
                 save_len_file(_A    ,   _A       )
         else:
             snake_case								= "examples/seq2seq/test_data/wmt_en_ro"
             snake_case								= max_len * 4
             save_len_file(_A    ,   _A       )

         snake_case								= AutoTokenizer.from_pretrained(_A       )
         snake_case								= SeqaSeqDataset(
             _A    ,   data_dir=_A    ,   type_path="train"    ,   max_source_length=_A    ,   max_target_length=_A    ,   n_obs=_A    ,   )
         return ds, max_tokens, tokenizer





     def 		UpperCAmelCase(self				:   List[Any]       )						->						Union[str, Any]:
         snake_case					,							snake_case					,							snake_case								= self._get_dataset()
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=0    ,   add_extra_examples=_A       )       )
         snake_case								= set(DistributedSortishSampler(_A    ,   2_5_6    ,   num_replicas=2    ,   rank=1    ,   add_extra_examples=_A       )       )
         assert idsa.intersection(_A       ) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,   )
     def 		UpperCAmelCase(self				:   Any    ,   _A				:   Optional[Any]       )						->						Union[str, Any]:
         snake_case								= AutoTokenizer.from_pretrained(_A    ,   use_fast=_A       )
         if tok_name == MBART_TINY:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   src_lang="EN"    ,   tgt_lang="FR"    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             snake_case								= SeqaSeqDataset(
                 _A    ,   data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )    ,   type_path="train"    ,   max_source_length=4    ,   max_target_length=8    ,   )
             snake_case								= train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
             assert len(_A       ) == 1 if tok_name == BART_TINY else len(_A       ) == 0

