




"""simple docstring"""






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,
)


lowercase__     						=						getLogger(__name__)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = 8							,    lowercase__ = 1024							,    lowercase__="val"							,    lowercase__=None							,    lowercase__=False							,    lowercase__="summarization"							,    lowercase__=None							,    lowercase__=1							,    lowercase__ = None							,    lowercase__=""							,    **lowercase__							,    ):
				_lowerCamelCase     :			List[str]    =    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     :			Any    =    AutoModelForSeqaSeqLM.from_pretrained(lowercase__		).cuda()
				if fpaa:
								_lowerCamelCase     :			Optional[Any]    =    model.half()
				# determine if we need to increase num_beams
				use_task_specific_params(lowercase__							,    lowercase__		)  # update config with task specific params
				_lowerCamelCase     :			Any    =    generate_kwargs.pop('num_beams'							,    model.config.num_beams		)  # AttributeError risk?
				if num_return_sequences > num_beams:
								_lowerCamelCase     :			Dict    =    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     :			Tuple    =    tokenizer.model_max_length
				if prefix is None:
								_lowerCamelCase     :			Optional[Any]    =    prefix or getattr(model.config							,    'prefix'							,    ''		) or ''
				_lowerCamelCase     :			int    =    SeqaSeqDataset(
				    lowercase__							,    lowercase__							,    lowercase__							,    max_target_length=1024							,    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     :			List[Any]    =    ds.make_sortish_sampler(lowercase__							,    distributed=lowercase__							,    add_extra_examples=lowercase__							,    shuffle=lowercase__		)
				_lowerCamelCase     :			List[Any]    =    DataLoader(lowercase__							,    sampler=lowercase__							,    batch_size=lowercase__							,    collate_fn=ds.collate_fn		)
				_lowerCamelCase     :			Optional[Any]    =    []
				for batch in tqdm(lowercase__		):
								_lowerCamelCase     :			Tuple    =    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     :			Tuple    =    tokenizer.batch_decode(lowercase__							,    skip_special_tokens=lowercase__							,    clean_up_tokenization_spaces=lowercase__		)
								_lowerCamelCase     :			str    =    batch['ids']
								if num_return_sequences > 1:
												_lowerCamelCase     :			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      _snake_case			(				):
				_lowerCamelCase     :			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, _lowerCamelCase     :			List[Any]    =    parser.parse_known_args()
				_lowerCamelCase     :			Any    =    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     :			List[Any]    =    Path(args.save_dir + '_tmp'		)
				Path(lowercase__		).mkdir(exist_ok=lowercase__		)  # this handles locking.
				_lowerCamelCase     :			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     :			Optional[Any]    =    {}
				if args.src_lang is not None:
								_lowerCamelCase     :			Tuple    =    args.src_lang
				if args.tgt_lang is not None:
								_lowerCamelCase     :			Tuple    =    args.tgt_lang

				Path(args.save_dir		).mkdir(exist_ok=lowercase__		)
				_lowerCamelCase, _lowerCamelCase     :			Optional[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     :			Tuple    =    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     :			str    =    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     :			Any    =    Path(args.data_dir		).joinpath(args.type_path + '.target'		)
								with open(lowercase__		) as f:
												_lowerCamelCase     :			int    =    [x.rstrip() for x in f.readlines()][: len(lowercase__		)]

								# Calculate metrics, save metrics,  and save _generations.txt
								_lowerCamelCase     :			str    =    'translation' in args.task
								_lowerCamelCase     :			Tuple    =    calculate_bleu if calc_bleu else calculate_rouge
								_lowerCamelCase     :			List[str]    =    'bleu' if calc_bleu else 'rouge'
								_lowerCamelCase     :			Dict    =    score_fn(lowercase__							,    lowercase__		)
								_lowerCamelCase     :			Any    =    len(lowercase__		)
								_lowerCamelCase     :			Dict    =    time.time() - start_time
								_lowerCamelCase     :			Optional[int]    =    round(runtime / metrics['n_obs']							,    4		)
								_lowerCamelCase     :			Optional[Any]    =    num_replicas
								# TODO(@stas00): add whatever metadata to metrics
								_lowerCamelCase     :			Union[str, 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      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			str    =    []
				for partial_result in partial_results:
								records.extend(lowercase__		)
				_lowerCamelCase     :			int    =    sorted(lowercase__							,    key=lambda lowercase__		: x["id"]		)
				_lowerCamelCase     :			List[Any]    =    [x['pred'] for x in records]
				return preds







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__		):
				# WAIT FOR lots of .json files
				_lowerCamelCase     :			str    =    time.time()
				logger.info('waiting for all nodes to finish'		)
				_lowerCamelCase     :			Dict    =    None
				while (time.time() - start_wait) < timeout:
								_lowerCamelCase     :			Union[str, Any]    =    list(save_dir.glob('rank_*.json'		)		)
								if len(lowercase__		) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												_lowerCamelCase     :			str    =    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()

"""simple docstring"""




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


_A			:       Optional[int]   		=       logging.getLogger(__name__)

class 		a__						(      a_						):
   def __init__(			self	,    _a=-1				):
          # in NER datasets, the last column is usually reserved for NER label
          lowercase    :				List[str]       		=							label_idx
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				Optional[Any]       		=							mode.value
          lowercase    :				List[str]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				str       		=							1
          lowercase    :				Optional[int]       		=							[]
          with open(_a	,    encoding="utf-8"				) as f:
                 lowercase    :				List[Any]       		=							[]
                 lowercase    :				Optional[int]       		=							[]
                 for line in f:
                        if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                               if words:
                                      examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                                      guid_index += 1
                                      lowercase    :				int       		=							[]
                                      lowercase    :				int       		=							[]
                        else:
                               lowercase    :				Optional[Any]       		=							line.split(" "				)
                               words.append(splits[0]				)
                               if len(_a				) > 1:
                                      labels.append(splits[self.label_idx].replace("\n"	,    ""				)				)
                               else:
                                      # Examples could have no label for mode = "test"
                                      labels.append("O"				)
                 if words:
                        examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				List[str]       		=							0
          for line in test_input_reader:
                 if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                        writer.write(_a				)
                        if not preds_list[example_id]:
                               example_id += 1
                 elif preds_list[example_id]:
                        lowercase    :				Any       		=							line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"
                        writer.write(_a				)
                 else:
                        logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,    line.split()[0]				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Optional[Any]       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				List[Any]       		=							["O"] + labels
                 return labels
          else:
                 return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]


class 		a__						(      a_						):
   def __init__(			self				):
          # in CONLL2003 dataset chunk column is second-to-last
          super().__init__(label_idx=-2				)

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        lowercase    :				Tuple       		=							f.read().splitlines()
                 if "O" not in labels:
                        lowercase    :				Optional[int]       		=							["O"] + labels
                 return labels
          else:
                 return [
                     "O",
                     "B-ADVP",
                     "B-INTJ",
                     "B-LST",
                     "B-PRT",
                     "B-NP",
                     "B-SBAR",
                     "B-VP",
                     "B-ADJP",
                     "B-CONJP",
                     "B-PP",
                     "I-ADVP",
                     "I-INTJ",
                     "I-LST",
                     "I-PRT",
                     "I-NP",
                     "I-SBAR",
                     "I-VP",
                     "I-ADJP",
                     "I-CONJP",
                     "I-PP",
                 ]


class 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				List[Any]       		=							mode.value
          lowercase    :				Optional[int]       		=							os.path.join(_a	,    f"""{mode}.txt"""				)
          lowercase    :				Tuple       		=							1
          lowercase    :				List[str]       		=							[]

          with open(_a	,    encoding="utf-8"				) as f:
                 for sentence in parse_incr(_a				):
                        lowercase    :				Optional[Any]       		=							[]
                        lowercase    :				str       		=							[]
                        for token in sentence:
                               words.append(token["form"]				)
                               labels.append(token["upos"]				)
                        assert len(_a				) == len(_a				)
                        if words:
                               examples.append(InputExample(guid=f"""{mode}-{guid_index}"""	,    words=_a	,    labels=_a				)				)
                               guid_index += 1
          return examples
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a				):
          lowercase    :				str       		=							0
          for sentence in parse_incr(_a				):
                 lowercase    :				List[Any]       		=							preds_list[example_id]
                 lowercase    :				List[str]       		=							""
                 for token in sentence:
                        out += f"""{token["form"]} ({token["upos"]}|{s_p.pop(0				)}) """
                 out += "\n"
                 writer.write(_a				)
                 example_id += 1

   def 			__magic_name__			(			self	,    _a				):
          if path:
                 with open(_a	,    "r"				) as f:
                        return f.read().splitlines()
          else:
                 return [
                     "ADJ",
                     "ADP",
                     "ADV",
                     "AUX",
                     "CCONJ",
                     "DET",
                     "INTJ",
                     "NOUN",
                     "NUM",
                     "PART",
                     "PRON",
                     "PROPN",
                     "PUNCT",
                     "SCONJ",
                     "SYM",
                     "VERB",
                     "X",
                 ]


