
from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)



@flax.struct.dataclass
class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							jnp.ndarray
   _SCREAMING_SNAKE_CASE      :							jnp.ndarray





class 		UpperCamelCase__  (       nn.Module    ):
   _SCREAMING_SNAKE_CASE      :							int
   _SCREAMING_SNAKE_CASE      :							Tuple[int]	       =      (16, 32, 96, 256)
   _SCREAMING_SNAKE_CASE      :							jnp.dtype	       =      jnp.floataa
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  int						=  nn.Conv(
        self.block_out_channels[0]					,				kernel_size=(3, 3)					,				padding=((1, 1), (1, 1))					,				dtype=self.dtype					,				)

    __a :  List[str]						=  []
    for i in range(len(self.block_out_channels   ) - 1   ):
     __a :  int						=  self.block_out_channels[i]
     __a :  List[str]						=  self.block_out_channels[i + 1]
     __a :  int						=  nn.Conv(
         snake_case_					,				kernel_size=(3, 3)					,				padding=((1, 1), (1, 1))					,				dtype=self.dtype					,				)
     blocks.append(snake_case_   )
     __a :  List[str]						=  nn.Conv(
         snake_case_					,				kernel_size=(3, 3)					,				strides=(2, 2)					,				padding=((1, 1), (1, 1))					,				dtype=self.dtype					,				)
     blocks.append(snake_case_   )
    __a :  Optional[Any]						=  blocks

    __a :  Optional[int]						=  nn.Conv(
        self.conditioning_embedding_channels					,				kernel_size=(3, 3)					,				padding=((1, 1), (1, 1))					,				kernel_init=nn.initializers.zeros_init()					,				bias_init=nn.initializers.zeros_init()					,				dtype=self.dtype					,				)







   def __call__(self :					List[str]					,				snake_case_ :					str   ):
    __a :  str						=  self.conv_in(snake_case_   )
    __a :  Dict						=  nn.silu(snake_case_   )

    for block in self.blocks:
     __a :  List[Any]						=  block(snake_case_   )
     __a :  str						=  nn.silu(snake_case_   )

    __a :  List[str]						=  self.conv_out(snake_case_   )

    return embedding





@flax_register_to_config
class 		UpperCamelCase__  (       nn.Module    ,__lowercase    ,__lowercase    ):
   _SCREAMING_SNAKE_CASE      :							int	       =      32
   _SCREAMING_SNAKE_CASE      :							int	       =      4
   _SCREAMING_SNAKE_CASE      :							Tuple[str]	       =      (
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "DownBlock2D",
   )
   _SCREAMING_SNAKE_CASE      :							Union[bool, Tuple[bool]]	       =      False
   _SCREAMING_SNAKE_CASE      :							Tuple[int]	       =      (320, 640, 1_280, 1_280)
   _SCREAMING_SNAKE_CASE      :							int	       =      2
   _SCREAMING_SNAKE_CASE      :							Union[int, Tuple[int]]	       =      8
   _SCREAMING_SNAKE_CASE      :							Optional[Union[int, Tuple[int]]]	       =      None
   _SCREAMING_SNAKE_CASE      :							int	       =      1_280
   _SCREAMING_SNAKE_CASE      :							float	       =      0.0
   _SCREAMING_SNAKE_CASE      :							bool	       =      False
   _SCREAMING_SNAKE_CASE      :							jnp.dtype	       =      jnp.floataa
   _SCREAMING_SNAKE_CASE      :							bool	       =      True
   _SCREAMING_SNAKE_CASE      :							int	       =      0
   _SCREAMING_SNAKE_CASE      :							str	       =      "rgb"
   _SCREAMING_SNAKE_CASE      :							Tuple[int]	       =      (16, 32, 96, 256)
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					jax.random.KeyArray   ):
    # init input tensors
    __a :  Union[str, Any]						=  (1, self.in_channels, self.sample_size, self.sample_size)
    __a :  List[Any]						=  jnp.zeros(snake_case_					,				dtype=jnp.floataa   )
    __a :  Dict						=  jnp.ones((1,)					,				dtype=jnp.intaa   )
    __a :  str						=  jnp.zeros((1, 1, self.cross_attention_dim)					,				dtype=jnp.floataa   )
    __a :  Dict						=  (1, 3, self.sample_size * 8, self.sample_size * 8)
    __a :  List[str]						=  jnp.zeros(snake_case_					,				dtype=jnp.floataa   )

    __a				,       __a :  List[str]						=  jax.random.split(snake_case_   )
    __a :  Any						=  {'''params''': params_rng, '''dropout''': dropout_rng}

    return self.init(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				snake_case_   )["params"]
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Dict						=  self.block_out_channels
    __a :  int						=  block_out_channels[0] * 4

    # If `num_attention_heads` is not defined (which is the case for most models)
    # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
    # The reason for this behavior is to correct for incorrectly named variables that were introduced
    # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
    # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
    # which is why we correct for the naming here.
    __a :  Any						=  self.num_attention_heads or self.attention_head_dim

    # input
    __a :  Any						=  nn.Conv(
        block_out_channels[0]					,				kernel_size=(3, 3)					,				strides=(1, 1)					,				padding=((1, 1), (1, 1))					,				dtype=self.dtype					,				)

    # time
    __a :  Optional[Any]						=  FlaxTimesteps(
        block_out_channels[0]					,				flip_sin_to_cos=self.flip_sin_to_cos					,				freq_shift=self.config.freq_shift   )
    __a :  Dict						=  FlaxTimestepEmbedding(snake_case_					,				dtype=self.dtype   )

    __a :  Optional[Any]						=  FlaxControlNetConditioningEmbedding(
        conditioning_embedding_channels=block_out_channels[0]					,				block_out_channels=self.conditioning_embedding_out_channels					,				)

    __a :  Tuple						=  self.only_cross_attention
    if isinstance(snake_case_					,				snake_case_   ):
     __a :  int						=  (only_cross_attention,) * len(self.down_block_types   )

    if isinstance(snake_case_					,				snake_case_   ):
     __a :  Optional[int]						=  (num_attention_heads,) * len(self.down_block_types   )

    # down
    __a :  str						=  []
    __a :  Optional[int]						=  []

    __a :  List[Any]						=  block_out_channels[0]

    __a :  str						=  nn.Conv(
        snake_case_					,				kernel_size=(1, 1)					,				padding='''VALID'''					,				kernel_init=nn.initializers.zeros_init()					,				bias_init=nn.initializers.zeros_init()					,				dtype=self.dtype					,				)
    controlnet_down_blocks.append(snake_case_   )

    for i, down_block_type in enumerate(self.down_block_types   ):
     __a :  List[str]						=  output_channel
     __a :  Dict						=  block_out_channels[i]
     __a :  int						=  i == len(snake_case_   ) - 1

     if down_block_type == "CrossAttnDownBlock2D":
      __a :  List[str]						=  FlaxCrossAttnDownBlockaD(
          in_channels=snake_case_					,				out_channels=snake_case_					,				dropout=self.dropout					,				num_layers=self.layers_per_block					,				num_attention_heads=num_attention_heads[i]					,				add_downsample=not is_final_block					,				use_linear_projection=self.use_linear_projection					,				only_cross_attention=only_cross_attention[i]					,				dtype=self.dtype					,				)
     else:
      __a :  Optional[Any]						=  FlaxDownBlockaD(
          in_channels=snake_case_					,				out_channels=snake_case_					,				dropout=self.dropout					,				num_layers=self.layers_per_block					,				add_downsample=not is_final_block					,				dtype=self.dtype					,				)

     down_blocks.append(snake_case_   )

     for _ in range(self.layers_per_block   ):
      __a :  Optional[Any]						=  nn.Conv(
          snake_case_					,				kernel_size=(1, 1)					,				padding='''VALID'''					,				kernel_init=nn.initializers.zeros_init()					,				bias_init=nn.initializers.zeros_init()					,				dtype=self.dtype					,				)
      controlnet_down_blocks.append(snake_case_   )

     if not is_final_block:
      __a :  Optional[int]						=  nn.Conv(
          snake_case_					,				kernel_size=(1, 1)					,				padding='''VALID'''					,				kernel_init=nn.initializers.zeros_init()					,				bias_init=nn.initializers.zeros_init()					,				dtype=self.dtype					,				)
      controlnet_down_blocks.append(snake_case_   )

    __a :  Optional[Any]						=  down_blocks
    __a :  Optional[int]						=  controlnet_down_blocks

    # mid
    __a :  Optional[int]						=  block_out_channels[-1]
    __a :  List[Any]						=  FlaxUNetMidBlockaDCrossAttn(
        in_channels=snake_case_					,				dropout=self.dropout					,				num_attention_heads=num_attention_heads[-1]					,				use_linear_projection=self.use_linear_projection					,				dtype=self.dtype					,				)

    __a :  Union[str, Any]						=  nn.Conv(
        snake_case_					,				kernel_size=(1, 1)					,				padding='''VALID'''					,				kernel_init=nn.initializers.zeros_init()					,				bias_init=nn.initializers.zeros_init()					,				dtype=self.dtype					,				)







   def __call__(self :					List[str]					,				snake_case_ :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					List[str]					,				snake_case_ :					Optional[int]					,				snake_case_ :					float = 1.0					,				snake_case_ :					bool = True					,				snake_case_ :					bool = False					,				):
    __a :  Any						=  self.controlnet_conditioning_channel_order
    if channel_order == "bgr":
     __a :  List[Any]						=  jnp.flip(snake_case_					,				axis=1   )

    # 1. time
    if not isinstance(snake_case_					,				jnp.ndarray   ):
     __a :  List[Any]						=  jnp.array([timesteps]					,				dtype=jnp.intaa   )
    elif isinstance(snake_case_					,				jnp.ndarray   ) and len(timesteps.shape   ) == 0:
     __a :  Optional[int]						=  timesteps.astype(dtype=jnp.floataa   )
     __a :  Tuple						=  jnp.expand_dims(snake_case_					,				0   )

    __a :  Tuple						=  self.time_proj(snake_case_   )
    __a :  int						=  self.time_embedding(snake_case_   )

    # 2. pre-process
    __a :  str						=  jnp.transpose(snake_case_					,				(0, 2, 3, 1)   )
    __a :  Optional[int]						=  self.conv_in(snake_case_   )

    __a :  Dict						=  jnp.transpose(snake_case_					,				(0, 2, 3, 1)   )
    __a :  Dict						=  self.controlnet_cond_embedding(snake_case_   )
    sample += controlnet_cond

    # 3. down
    __a :  int						=  (sample,)
    for down_block in self.down_blocks:
     if isinstance(snake_case_					,				snake_case_   ):
      __a				,       __a :  Dict						=  down_block(snake_case_					,				snake_case_					,				snake_case_					,				deterministic=not train   )
     else:
      __a				,       __a :  List[Any]						=  down_block(snake_case_					,				snake_case_					,				deterministic=not train   )
     down_block_res_samples += res_samples

    # 4. mid
    __a :  int						=  self.mid_block(snake_case_					,				snake_case_					,				snake_case_					,				deterministic=not train   )

    # 5. contronet blocks
    __a :  Any						=  ()
    for down_block_res_sample, controlnet_block in zip(snake_case_					,				self.controlnet_down_blocks   ):
     __a :  str						=  controlnet_block(snake_case_   )
     controlnet_down_block_res_samples += (down_block_res_sample,)

    __a :  List[Any]						=  controlnet_down_block_res_samples

    __a :  int						=  self.controlnet_mid_block(snake_case_   )

    # 6. scaling
    __a :  Optional[int]						=  [sample * conditioning_scale for sample in down_block_res_samples]
    mid_block_res_sample *= conditioning_scale

    if not return_dict:
     return (down_block_res_samples, mid_block_res_sample)

    return FlaxControlNetOutput(
        down_block_res_samples=snake_case_					,				mid_block_res_sample=snake_case_   )


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    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

 __a :  Tuple						=  Path(lowerCAmelCase__  )
 __a :  Dict						=  save_dir.joinpath(f"rank_{local_rank}_output.json"  )
 torch.cuda.set_device(lowerCAmelCase__  )
 __a :  Dict						=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__  ).cuda()
 if fpaa:
  __a :  str						=  model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(lowerCAmelCase__  ,   lowerCAmelCase__  )  # update config with task specific params
 __a :  List[str]						=  generate_kwargs.pop('''num_beams'''  ,   model.config.num_beams  )  # AttributeError risk?
 if num_return_sequences > num_beams:
  __a :  Dict						=  num_return_sequences

 __a :  Optional[Any]						=  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:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  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.
 __a :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  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__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  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     (				):
 __a :  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=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'''  )
 __a :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  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.
 __a :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  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:
  __a :  int						=  Path(args.save_dir  )
  save_dir.mkdir(exist_ok=lowerCAmelCase__  )
  __a :  List[str]						=  gather_results_from_each_node(lowerCAmelCase__  ,   lowerCAmelCase__  ,   args.sync_timeout  )
  __a :  int						=  combine_partial_results(lowerCAmelCase__  )
  if args.num_return_sequences > 1:
   __a :  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
  __a :  Any						=  Path(args.data_dir  ).joinpath(args.type_path + '''.target'''  )
  with open(lowerCAmelCase__  ) as f:
   __a :  Optional[int]						=  [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase__  )]

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




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