




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 warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A   =			logging.get_logger(__name__)

_A   =			{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							List[Any]									=		"segformer"





     def __init__(self				:   List[Any]    ,   _A				:   int=3    ,   _A				:   List[Any]=4    ,   _A				:   Any=[2, 2, 2, 2]    ,   _A				:   Dict=[8, 4, 2, 1]    ,   _A				:   List[Any]=[3_2, 6_4, 1_6_0, 2_5_6]    ,   _A				:   Tuple=[7, 3, 3, 3]    ,   _A				:   Optional[int]=[4, 2, 2, 2]    ,   _A				:   Dict=[1, 2, 5, 8]    ,   _A				:   int=[4, 4, 4, 4]    ,   _A				:   Dict="gelu"    ,   _A				:   Tuple=0.0    ,   _A				:   Optional[Any]=0.0    ,   _A				:   List[Any]=0.1    ,   _A				:   Union[str, Any]=0.02    ,   _A				:   Dict=0.1    ,   _A				:   List[Any]=1E-6    ,   _A				:   List[str]=2_5_6    ,   _A				:   Optional[Any]=2_5_5    ,   **_A				:   str    ,   )						->						Tuple:
         super().__init__(**_A       )

         if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
             warnings.warn(
                 "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
                 " removed, as the behaviour will default to that of reshape_last_stage = True."    ,   _A    ,   )

         snake_case								= num_channels
         snake_case								= num_encoder_blocks
         snake_case								= depths
         snake_case								= sr_ratios
         snake_case								= hidden_sizes
         snake_case								= patch_sizes
         snake_case								= strides
         snake_case								= mlp_ratios
         snake_case								= num_attention_heads
         snake_case								= hidden_act
         snake_case								= hidden_dropout_prob
         snake_case								= attention_probs_dropout_prob
         snake_case								= classifier_dropout_prob
         snake_case								= initializer_range
         snake_case								= drop_path_rate
         snake_case								= layer_norm_eps
         snake_case								= decoder_hidden_size
         snake_case								= kwargs.get("reshape_last_stage"    ,   _A       )
         snake_case								= semantic_loss_ignore_index







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Optional[Any]									=		version.parse("1.11"  )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]       )





     @property
     def 		UpperCAmelCase(self				:   Tuple       )						->						float:
         return 1E-4





     @property
     def 		UpperCAmelCase(self				:   List[str]       )						->						int:
         return 1_2

