



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,
)


_snake_case	=						getLogger(__name__)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase = 8       ,				_lowerCamelCase = 1_024       ,				_lowerCamelCase="val"       ,				_lowerCamelCase=None       ,				_lowerCamelCase=False       ,				_lowerCamelCase="summarization"       ,				_lowerCamelCase=None       ,				_lowerCamelCase=1       ,				_lowerCamelCase = None       ,				_lowerCamelCase=""       ,				**_lowerCamelCase       ,				):





       '''simple docstring'''




       _lowerCAmelCase  :							Optional[Any]				     =					str(_lowercase						)
       assert local_rank is not None
       torch.distributed.init_process_group(backend="nccl"       ,				rank=_lowercase						)

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

       _lowerCAmelCase  :							List[Any]				     =					AutoTokenizer.from_pretrained(_lowercase						)
       logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"						)  # if this is wrong, check config.model_type.

       if max_source_length is None:
              _lowerCAmelCase  :							Union[str, Any]				     =					tokenizer.model_max_length
       if prefix is None:
              _lowerCAmelCase  :							Optional[Any]				     =					prefix or getattr(model.config       ,				"prefix"       ,				""						) or ""
       _lowerCAmelCase  :							List[str]				     =					SeqaSeqDataset(
           _lowercase       ,				_lowercase       ,				_lowercase       ,				max_target_length=1_024       ,				type_path=_lowercase       ,				n_obs=_lowercase       ,				prefix=_lowercase       ,				**_lowercase       ,				)
       # 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  :							Dict				     =					ds.make_sortish_sampler(_lowercase       ,				distributed=_lowercase       ,				add_extra_examples=_lowercase       ,				shuffle=_lowercase						)
       _lowerCAmelCase  :							Optional[int]				     =					DataLoader(_lowercase       ,				sampler=_lowercase       ,				batch_size=_lowercase       ,				collate_fn=ds.collate_fn						)
       _lowerCAmelCase  :							List[Any]				     =					[]
       for batch in tqdm(_lowercase						):
              _lowerCAmelCase  :							List[str]				     =					model.generate(
                  input_ids=batch["input_ids"].to(model.device						)       ,				attention_mask=batch["attention_mask"].to(model.device						)       ,				num_return_sequences=_lowercase       ,				num_beams=_lowercase       ,				**_lowercase       ,				)
              _lowerCAmelCase  :							Optional[Any]				     =					tokenizer.batch_decode(_lowercase       ,				skip_special_tokens=_lowercase       ,				clean_up_tokenization_spaces=_lowercase						)
              _lowerCAmelCase  :							Tuple				     =					batch["ids"]
              if num_return_sequences > 1:
                     _lowerCAmelCase  :							Union[str, Any]				     =					chunks(_lowercase       ,				_lowercase						)  # batch size chunks, each of size num_return_seq
              for i, pred in enumerate(_lowercase						):
                     results.append({"pred": pred, "id": ids[i].item()}						)
       save_json(_lowercase       ,				_lowercase						)
       return results, sampler.num_replicas






def        A       (      ):





       '''simple docstring'''




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

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

       Path(args.save_dir						).mkdir(exist_ok=_lowercase						)
       _lowerCAmelCase  :							Any				     =					eval_data_dir(
           args.data_dir       ,				_lowercase       ,				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=_lowercase       ,				**_lowercase       ,				)

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

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






def        A       (      _lowerCamelCase						):





       '''simple docstring'''




       _lowerCAmelCase  :							List[str]				     =					[]
       for partial_result in partial_results:
              records.extend(_lowercase						)
       _lowerCAmelCase  :							str				     =					sorted(_lowercase       ,				key=lambda _lowerCamelCase						: x["id"]						)
       _lowerCAmelCase  :							List[Any]				     =					[x["pred"] for x in records]
       return preds






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





       '''simple docstring'''




       _lowerCAmelCase  :							Optional[Any]				     =					time.time()
       logger.info("waiting for all nodes to finish"						)
       _lowerCAmelCase  :							str				     =					None
       while (time.time() - start_wait) < timeout:
              _lowerCAmelCase  :							int				     =					list(save_dir.glob("rank_*.json"						)						)
              if len(_lowercase						) < num_replicas:
                     continue
              try:
                     # make sure all json files are fully saved
                     _lowerCAmelCase  :							Tuple				     =					lmap(_lowercase       ,				_lowercase						)
                     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()





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a     ,      a):
							lowerCamelCase__         =	'focalnet'







							def __init__(     self,    __a=224,    __a=4,    __a=3,    __a=96,    __a=False,    __a=[192, 384, 768, 768],    __a=[2, 2, 6, 2],    __a=[2, 2, 2, 2],    __a=[3, 3, 3, 3],    __a="gelu",    __a=4.0,    __a=0.0,    __a=0.1,    __a=False,    __a=1E-4,    __a=False,    __a=False,    __a=False,    __a=0.02,    __a=1E-5,    __a=32,    __a=None,    __a=None,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase  :							str				     =					image_size
														_lowerCAmelCase  :							List[str]				     =					patch_size
														_lowerCAmelCase  :							List[Any]				     =					num_channels
														_lowerCAmelCase  :							Tuple				     =					embed_dim
														_lowerCAmelCase  :							List[Any]				     =					use_conv_embed
														_lowerCAmelCase  :							Any				     =					hidden_sizes
														_lowerCAmelCase  :							Tuple				     =					depths
														_lowerCAmelCase  :							Dict				     =					focal_levels
														_lowerCAmelCase  :							Optional[Any]				     =					focal_windows
														_lowerCAmelCase  :							str				     =					hidden_act
														_lowerCAmelCase  :							Union[str, Any]				     =					mlp_ratio
														_lowerCAmelCase  :							Any				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Dict				     =					drop_path_rate
														_lowerCAmelCase  :							str				     =					use_layerscale
														_lowerCAmelCase  :							str				     =					layerscale_value
														_lowerCAmelCase  :							Union[str, Any]				     =					use_post_layernorm
														_lowerCAmelCase  :							Optional[int]				     =					use_post_layernorm_in_modulation
														_lowerCAmelCase  :							str				     =					normalize_modulator
														_lowerCAmelCase  :							Any				     =					initializer_range
														_lowerCAmelCase  :							Union[str, Any]				     =					layer_norm_eps
														_lowerCAmelCase  :							Any				     =					encoder_stride
														_lowerCAmelCase  :							List[str]				     =					["stem"] + [f"stage{idx}" for idx in range(1,    len(self.depths) + 1)]
														_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					get_aligned_output_features_output_indices(
														    out_features=__a,    out_indices=__a,    stage_names=self.stage_names)
