


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[str]									=       getLogger(__name__)



def     __lowerCAmelCase						(							snake_case	:    Tuple						,      snake_case	:    str						,      snake_case	:    str						,      snake_case	:    int = 8						,      snake_case	:    int = 1024						,      snake_case	:    Union[str, Any]="val"						,      snake_case	:    Union[str, Any]=None						,      snake_case	:    str=False						,      snake_case	:    int="summarization"						,      snake_case	:    List[Any]=None						,      snake_case	:    List[Any]=1						,      snake_case	:    Dict = None						,      snake_case	:    Any=""						,      **snake_case	:    Optional[int]						,      )       ->							Dict:
   __lowerCamelCase:				Optional[Any]     =	str(snake_case )
   assert local_rank is not None
   torch.distributed.init_process_group(backend="""nccl"""						,      rank=snake_case )

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

   __lowerCamelCase:				Optional[Any]     =	AutoTokenizer.from_pretrained(snake_case )
   logger.info(f'Inferred tokenizer type: {tokenizer.__class__}' )  # if this is wrong, check config.model_type.

   if max_source_length is None:
      __lowerCamelCase:				Optional[Any]     =	tokenizer.model_max_length
   if prefix is None:
      __lowerCamelCase:				Any     =	prefix or getattr(model.config						,      """prefix"""						,      """""" ) or """"""
   __lowerCamelCase:				int     =	SeqaSeqDataset(
       snake_case						,      snake_case						,      snake_case						,      max_target_length=1024						,      type_path=snake_case						,      n_obs=snake_case						,      prefix=snake_case						,      **snake_case						,      )
   # 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(snake_case						,      distributed=snake_case						,      add_extra_examples=snake_case						,      shuffle=snake_case )
   __lowerCamelCase:				int     =	DataLoader(snake_case						,      sampler=snake_case						,      batch_size=snake_case						,      collate_fn=ds.collate_fn )
   __lowerCamelCase:				List[str]     =	[]
   for batch in tqdm(snake_case ):
      __lowerCamelCase:				List[Any]     =	model.generate(
          input_ids=batch["""input_ids"""].to(model.device )						,      attention_mask=batch["""attention_mask"""].to(model.device )						,      num_return_sequences=snake_case						,      num_beams=snake_case						,      **snake_case						,      )
      __lowerCamelCase:				Any     =	tokenizer.batch_decode(snake_case						,      skip_special_tokens=snake_case						,      clean_up_tokenization_spaces=snake_case )
      __lowerCamelCase:				Optional[int]     =	batch["""ids"""]
      if num_return_sequences > 1:
         __lowerCamelCase:				str     =	chunks(snake_case						,      snake_case )  # batch size chunks, each of size num_return_seq
      for i, pred in enumerate(snake_case ):
         results.append({"""pred""": pred, """id""": ids[i].item()} )
   save_json(snake_case						,      snake_case )
   return results, sampler.num_replicas



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

   parser.add_argument(
       """--n_obs"""						,      type=snake_case						,      default=snake_case						,      required=snake_case						,      help="""How many observations. Defaults to all.""" )
   parser.add_argument(
       """--num_return_sequences"""						,      type=snake_case						,      default=1						,      required=snake_case						,      help="""How many sequences to return""" )
   parser.add_argument(
       """--sync_timeout"""						,      type=snake_case						,      default=600						,      required=snake_case						,      help="""How long should master process wait for other processes to finish."""						,      )
   parser.add_argument("""--src_lang"""						,      type=snake_case						,      default=snake_case						,      required=snake_case )
   parser.add_argument("""--tgt_lang"""						,      type=snake_case						,      default=snake_case						,      required=snake_case )
   parser.add_argument(
       """--prefix"""						,      type=snake_case						,      required=snake_case						,      default=snake_case						,      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:				Optional[int]     =	time.time()
   __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	parser.parse_known_args()
   __lowerCamelCase:				Tuple     =	parse_numeric_n_bool_cl_kwargs(snake_case )
   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(snake_case ).mkdir(exist_ok=snake_case )  # this handles locking.
   __lowerCamelCase:				int     =	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:				Any     =	{}
   if args.src_lang is not None:
      __lowerCamelCase:				Tuple     =	args.src_lang
   if args.tgt_lang is not None:
      __lowerCamelCase:				Optional[Any]     =	args.tgt_lang

   Path(args.save_dir ).mkdir(exist_ok=snake_case )
   __lowerCamelCase   ,     __lowerCamelCase:				Any     =	eval_data_dir(
       args.data_dir						,      snake_case						,      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=snake_case						,      **snake_case						,      )

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

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



def     __lowerCAmelCase						(							snake_case	:    Optional[Any] )       ->							List:
   __lowerCamelCase:				int     =	[]
   for partial_result in partial_results:
      records.extend(snake_case )
   __lowerCamelCase:				List[str]     =	sorted(snake_case						,      key=lambda snake_case : x["id"] )
   __lowerCamelCase:				Dict     =	[x["""pred"""] for x in records]
   return preds



def     __lowerCAmelCase						(							snake_case	:    Dict						,      snake_case	:    int						,      snake_case	:    int )       ->							List[Dict[str, List]]:
   # WAIT FOR lots of .json files
   __lowerCamelCase:				Optional[int]     =	time.time()
   logger.info("""waiting for all nodes to finish""" )
   __lowerCamelCase:				int     =	None
   while (time.time() - start_wait) < timeout:
      __lowerCamelCase:				int     =	list(save_dir.glob("""rank_*.json""" ) )
      if len(snake_case ) < num_replicas:
         continue
      try:
         # make sure all json files are fully saved
         __lowerCamelCase:				Any     =	lmap(snake_case						,      snake_case )
         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()






class        a					:





     def __init__(    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				str = ""    ,	SCREAMING_SNAKE_CASE_					:				bool = False							):
        # Mapping from the first character of the prefix of the node
        __lowerCamelCase:				dict[str, RadixNode]     =	{}

        # A node will be a leaf if the tree contains its word
        __lowerCamelCase:				str     =	is_leaf

        __lowerCamelCase:				Optional[int]     =	prefix





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				Optional[Any]     =	0
        for q, w in zip(self.prefix    ,	SCREAMING_SNAKE_CASE_							):
           if q != w:
              break

           x += 1

        return self.prefix[:x], self.prefix[x:], word[x:]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				list[str]							):
        for word in words:
           self.insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        # Case 1: If the word is the prefix of the node
        # Solution: We set the current node as leaf
        if self.prefix == word:
           __lowerCamelCase:				Union[str, Any]     =	True

        # Case 2: The node has no edges that have a prefix to the word
        # Solution: We create an edge from the current node to a new one
        # containing the word
        elif word[0] not in self.nodes:
           __lowerCamelCase:				Any     =	RadixNode(prefix=SCREAMING_SNAKE_CASE_    ,	is_leaf=SCREAMING_SNAKE_CASE_							)

        else:
           __lowerCamelCase:				Union[str, Any]     =	self.nodes[word[0]]
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				List[str]     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
              self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_							)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
              __lowerCamelCase:				List[Any]     =	remaining_prefix

              __lowerCamelCase:				Optional[Any]     =	self.nodes[matching_string[0]]
              __lowerCamelCase:				Optional[int]     =	RadixNode(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Union[str, Any]     =	aux_node

              if remaining_word == "":
                 __lowerCamelCase:				Optional[int]     =	True
              else:
                 self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				int     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Any     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
              return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
              return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
              return incoming_node.find(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				str     =	self.nodes.get(word[0]    ,	SCREAMING_SNAKE_CASE_							)
        if not incoming_node:
           return False
        else:
           __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Dict     =	incoming_node.match(
               SCREAMING_SNAKE_CASE_							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
              return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
              return incoming_node.delete(SCREAMING_SNAKE_CASE_							)
           else:
              # If it is not a leaf, we don't have to delete
              if not incoming_node.is_leaf:
                 return False
              else:
                 # We delete the nodes if no edges go from it
                 if len(incoming_node.nodes							) == 0:
                    del self.nodes[word[0]]
                    # We merge the current node with its only child
                    if len(self.nodes							) == 1 and not self.is_leaf:
                       __lowerCamelCase:				List[Any]     =	list(self.nodes.values()							)[0]
                       __lowerCamelCase:				Any     =	merging_node.is_leaf
                       self.prefix += merging_node.prefix
                       __lowerCamelCase:				Tuple     =	merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                 elif len(incoming_node.nodes							) > 1:
                    __lowerCamelCase:				int     =	False
                 # If there is 1 edge, we merge it with its child
                 else:
                    __lowerCamelCase:				Union[str, Any]     =	list(incoming_node.nodes.values()							)[0]
                    __lowerCamelCase:				List[str]     =	merging_node.is_leaf
                    incoming_node.prefix += merging_node.prefix
                    __lowerCamelCase:				Union[str, Any]     =	merging_node.nodes

                 return True







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int = 0							):
        if self.prefix != "":
           print("""-""" * height    ,	self.prefix    ,	"""  (leaf)""" if self.is_leaf else """"""							)

        for value in self.nodes.values():
           value.print_tree(height + 1							)



def     __lowerCAmelCase						(							)       ->							bool:
   __lowerCamelCase:				Optional[int]     =	"""banana bananas bandana band apple all beast""".split()
   __lowerCamelCase:				Optional[Any]     =	RadixNode()
   root.insert_many(snake_case )

   assert all(root.find(snake_case ) for word in words )
   assert not root.find("""bandanas""" )
   assert not root.find("""apps""" )
   root.delete("""all""" )
   assert not root.find("""all""" )
   root.delete("""banana""" )
   assert not root.find("""banana""" )
   assert root.find("""bananas""" )

   return True



def     __lowerCAmelCase						(							)       ->							None:
   assert test_trie()



def     __lowerCAmelCase						(							)       ->							None:
   __lowerCamelCase:				int     =	RadixNode()
   __lowerCamelCase:				str     =	"""banana bananas bandanas bandana band apple all beast""".split()
   root.insert_many(snake_case )

   print("""Words:"""						,      snake_case )
   print("""Tree:""" )
   root.print_tree()


if __name__ == "__main__":
   main()


