



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__      :				List[Any]   	=			getLogger(__name__)

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__ = 8						,  A__ = 1_024						,  A__="val"						,  A__=None						,  A__=False						,  A__="summarization"						,  A__=None						,  A__=1						,  A__ = None						,  A__=""						,  **A__						,  ):
      lowercase__     =  str(A__	)
      assert local_rank is not None
      torch.distributed.init_process_group(backend='nccl'						,  rank=A__	)

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

      lowercase__     =  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:
            lowercase__     =  tokenizer.model_max_length
      if prefix is None:
            lowercase__     =  prefix or getattr(model.config						,  'prefix'						,  ''	) or ''
      lowercase__     =  SeqaSeqDataset(
          A__						,  A__						,  A__						,  max_target_length=1_024						,  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.
      lowercase__     =  ds.make_sortish_sampler(A__						,  distributed=A__						,  add_extra_examples=A__						,  shuffle=A__	)
      lowercase__     =  DataLoader(A__						,  sampler=A__						,  batch_size=A__						,  collate_fn=ds.collate_fn	)
      lowercase__     =  []
      for batch in tqdm(A__	):
            lowercase__     =  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__						,  )
            lowercase__     =  tokenizer.batch_decode(A__						,  skip_special_tokens=A__						,  clean_up_tokenization_spaces=A__	)
            lowercase__     =  batch['ids']
            if num_return_sequences > 1:
                  lowercase__     =  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   _lowerCAmelCase   (						):
      lowercase__     =  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'	)
      lowercase__     =  time.time()
      lowercase__,	lowercase__     =  parser.parse_known_args()
      lowercase__     =  parse_numeric_n_bool_cl_kwargs(A__	)
      if generate_kwargs and args.local_rank <= 0:
            print(F'''parsed the following generate kwargs: {generate_kwargs}'''	)
      lowercase__     =  Path(args.save_dir + '_tmp'	)
      Path(A__	).mkdir(exist_ok=A__	)  # this handles locking.
      lowercase__     =  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.
      lowercase__     =  {}
      if args.src_lang is not None:
            lowercase__     =  args.src_lang
      if args.tgt_lang is not None:
            lowercase__     =  args.tgt_lang

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

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

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











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__      :				Optional[int]   	=			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Any   	=			["BlenderbotTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				Dict   	=			[
           "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BlenderbotForCausalLM",
           "BlenderbotForConditionalGeneration",
           "BlenderbotModel",
           "BlenderbotPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				List[str]   	=			[
           "TFBlenderbotForConditionalGeneration",
           "TFBlenderbotModel",
           "TFBlenderbotPreTrainedModel",
       ]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__      :				int   	=			[
           "FlaxBlenderbotForConditionalGeneration",
           "FlaxBlenderbotModel",
           "FlaxBlenderbotPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_blenderbot import (
           BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BlenderbotConfig,
           BlenderbotOnnxConfig,
       )
       from .tokenization_blenderbot import BlenderbotTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_blenderbot import (
                  BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BlenderbotForCausalLM,
                  BlenderbotForConditionalGeneration,
                  BlenderbotModel,
                  BlenderbotPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_blenderbot import (
                  TFBlenderbotForConditionalGeneration,
                  TFBlenderbotModel,
                  TFBlenderbotPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_blenderbot import (
                  FlaxBlenderbotForConditionalGeneration,
                  FlaxBlenderbotModel,
                  FlaxBlenderbotPreTrainedModel,
              )

else:
       import sys

       a__      :				Optional[Any]   	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
