
'''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,
)


snake_case									=    getLogger(__name__)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_ = 8      ,			lowerCamelCase_ = 1_0_2_4      ,			lowerCamelCase_="val"      ,			lowerCamelCase_=None      ,			lowerCamelCase_=False      ,			lowerCamelCase_="summarization"      ,			lowerCamelCase_=None      ,			lowerCamelCase_=1      ,			lowerCamelCase_ = None      ,			lowerCamelCase_=""      ,			**lowerCamelCase_      ,			):







 """simple docstring"""


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

 lowerCAmelCase__     :   Optional[int]     		=	Path(lowerCamelCase_			)
 lowerCAmelCase__     :   List[str]     		=	save_dir.joinpath(f'''rank_{local_rank}_output.json'''			)
 torch.cuda.set_device(lowerCamelCase_			)
 lowerCAmelCase__     :   Union[str, Any]     		=	AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).cuda()
 if fpaa:
  lowerCAmelCase__     :   Optional[Any]     		=	model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(lowerCamelCase_      ,			lowerCamelCase_			)  # 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__     :   Union[str, Any]     		=	num_return_sequences

 lowerCAmelCase__     :   Dict     		=	AutoTokenizer.from_pretrained(lowerCamelCase_			)
 logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''			)  # if this is wrong, check config.model_type.

 if max_source_length is None:
  lowerCAmelCase__     :   Optional[int]     		=	tokenizer.model_max_length
 if prefix is None:
  lowerCAmelCase__     :   Tuple     		=	prefix or getattr(model.config      ,			"prefix"      ,			""			) or ""
 lowerCAmelCase__     :   List[str]     		=	SeqaSeqDataset(
     lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_      ,			max_target_length=1_0_2_4      ,			type_path=lowerCamelCase_      ,			n_obs=lowerCamelCase_      ,			prefix=lowerCamelCase_      ,			**lowerCamelCase_      ,			)
 # 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__     :   int     		=	ds.make_sortish_sampler(lowerCamelCase_      ,			distributed=lowerCamelCase_      ,			add_extra_examples=lowerCamelCase_      ,			shuffle=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	DataLoader(lowerCamelCase_      ,			sampler=lowerCamelCase_      ,			batch_size=lowerCamelCase_      ,			collate_fn=ds.collate_fn			)
 lowerCAmelCase__     :   int     		=	[]
 for batch in tqdm(lowerCamelCase_			):
  lowerCAmelCase__     :   List[str]     		=	model.generate(
      input_ids=batch["input_ids"].to(model.device			)      ,			attention_mask=batch["attention_mask"].to(model.device			)      ,			num_return_sequences=lowerCamelCase_      ,			num_beams=lowerCamelCase_      ,			**lowerCamelCase_      ,			)
  lowerCAmelCase__     :   List[str]     		=	tokenizer.batch_decode(lowerCamelCase_      ,			skip_special_tokens=lowerCamelCase_      ,			clean_up_tokenization_spaces=lowerCamelCase_			)
  lowerCAmelCase__     :   str     		=	batch["ids"]
  if num_return_sequences > 1:
   lowerCAmelCase__     :   List[Any]     		=	chunks(lowerCamelCase_      ,			lowerCamelCase_			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCamelCase_			):
   results.append({"pred": pred, "id": ids[i].item()}			)
 save_json(lowerCamelCase_      ,			lowerCamelCase_			)
 return results, sampler.num_replicas


def        UpperCAmelCase_ (  ):







 """simple docstring"""


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

 parser.add_argument(
     "--n_obs"      ,			type=lowerCamelCase_      ,			default=lowerCamelCase_      ,			required=lowerCamelCase_      ,			help="How many observations. Defaults to all."			)
 parser.add_argument(
     "--num_return_sequences"      ,			type=lowerCamelCase_      ,			default=1      ,			required=lowerCamelCase_      ,			help="How many sequences to return"			)
 parser.add_argument(
     "--sync_timeout"      ,			type=lowerCamelCase_      ,			default=6_0_0      ,			required=lowerCamelCase_      ,			help="How long should master process wait for other processes to finish."      ,			)
 parser.add_argument("--src_lang"      ,			type=lowerCamelCase_      ,			default=lowerCamelCase_      ,			required=lowerCamelCase_			)
 parser.add_argument("--tgt_lang"      ,			type=lowerCamelCase_      ,			default=lowerCamelCase_      ,			required=lowerCamelCase_			)
 parser.add_argument(
     "--prefix"      ,			type=lowerCamelCase_      ,			required=lowerCamelCase_      ,			default=lowerCamelCase_      ,			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__     :   List[Any]     		=	time.time()
 lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	parser.parse_known_args()
 lowerCAmelCase__     :   List[Any]     		=	parse_numeric_n_bool_cl_kwargs(lowerCamelCase_			)
 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(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)  # this handles locking.
 lowerCAmelCase__     :   Tuple     		=	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__     :   Dict     		=	args.src_lang
 if args.tgt_lang is not None:
  lowerCAmelCase__     :   int     		=	args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=lowerCamelCase_			)
 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	eval_data_dir(
     args.data_dir      ,			lowerCamelCase_      ,			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=lowerCamelCase_      ,			**lowerCamelCase_      ,			)

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

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


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   int     		=	time.time()
 logger.info("waiting for all nodes to finish"			)
 lowerCAmelCase__     :   Any     		=	None
 while (time.time() - start_wait) < timeout:
  lowerCAmelCase__     :   Optional[Any]     		=	list(save_dir.glob("rank_*.json"			)			)
  if len(lowerCamelCase_			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   lowerCAmelCase__     :   Any     		=	lmap(lowerCamelCase_      ,			lowerCamelCase_			)
   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 torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


snake_case									=    logging.get_logger(__name__)  # pylint: disable=invalid-name




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : int			,							a__  : List[Any]=768    ):
  '''simple docstring'''

  super().__init__(a__    )
  lowerCAmelCase__     :   int     		=	proj_size

  lowerCAmelCase__     :   str     		=	CLIPVisionModel(a__    )
  lowerCAmelCase__     :   int     		=	PaintByExampleMapper(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	nn.LayerNorm(config.hidden_size    )
  lowerCAmelCase__     :   Optional[Any]     		=	nn.Linear(config.hidden_size			,							self.proj_size    )

  # uncondition for scaling
  lowerCAmelCase__     :   Dict     		=	nn.Parameter(torch.randn((1, 1, self.proj_size)    )    )


 def    _A	(    self  : Tuple			,							a__  : int			,							a__  : Optional[Any]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.model(pixel_values=a__    )
  lowerCAmelCase__     :   Any     		=	clip_output.pooler_output
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mapper(latent_states[:, None]    )
  lowerCAmelCase__     :   List[Any]     		=	self.final_layer_norm(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	self.proj_out(a__    )
  if return_uncond_vector:
   return latent_states, self.uncond_vector

  return latent_states




class    lowerCAmelCase       (     nn.Module  ):


 def __init__(    self  : Optional[Any]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  super().__init__()
  lowerCAmelCase__     :   Optional[Any]     		=	(config.num_hidden_layers + 1) // 5
  lowerCAmelCase__     :   str     		=	config.hidden_size
  lowerCAmelCase__     :   Dict     		=	1
  lowerCAmelCase__     :   int     		=	nn.ModuleList(
      [
          BasicTransformerBlock(a__			,							a__			,							a__			,							activation_fn="gelu"			,							attention_bias=a__    )
          for _ in range(a__    )
      ]    )


 def    _A	(    self  : int			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  for block in self.blocks:
   lowerCAmelCase__     :   List[Any]     		=	block(a__    )

  return hidden_states





