

import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
lowerCAmelCase__							=			{
    "translation": ["bleu"],
    "summarization": ROUGE_KEYS,
}
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	search.split()
  UpperCAmelCase          =	dict((g.split("="      ) for g in groups)      )
  UpperCAmelCase          =	list(entries.keys()      )
  UpperCAmelCase          =	[[F"--{k} {v}" for v in vs.split(":"      )] for k, vs in entries.items()]
  UpperCAmelCase          =	[list(__A      ) for x in itertools.product(*__A      )]
  return matrix, entry_names
def   _lowerCAmelCase(						):
  UpperCAmelCase          =	sys.argv[0]

  UpperCAmelCase          =	argparse.ArgumentParser(
      usage=(
          "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
          " refer to `run_eval.py -h` for the complete list."
      )      )
  parser.add_argument(
      "--search" ,							type=__A ,							required=__A ,							help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\"" ,							)
  parser.add_argument(
      "--bs" ,							type=__A ,							default=8 ,							required=__A ,							help="initial batch size (may get reduced if it's too big)"      )
  parser.add_argument("--task" ,							type=__A ,							help="used for task_specific_params + metrics"      )
  parser.add_argument(
      "--info" ,							nargs="?" ,							type=__A ,							const=datetime_now() ,							help=(
          "add custom notes to be printed before the results table. If no value is passed, the current datetime"
          " string will be used."
      ) ,							)
  UpperCAmelCase   ,						UpperCAmelCase          =	parser.parse_known_args()
  # we share some of the args
  args_main.extend(["--task", args.task]      )
  UpperCAmelCase          =	[prog] + args_main

  # to support variations like translation_en_to_de"
  UpperCAmelCase          =	"translation" if "translation" in args.task else "summarization"

  UpperCAmelCase   ,						UpperCAmelCase          =	parse_search_arg(args.search      )
  UpperCAmelCase          =	task_score_names[task]  # score cols first
  UpperCAmelCase          =	{col: len(str(__A      )      ) for col in col_names}
  UpperCAmelCase          =	[]
  for r in matrix:
    UpperCAmelCase          =	dict((x.replace("--" ,							""      ).split() for x in r)      )
    UpperCAmelCase          =	" ".join(__A      ).split()
    args_exp.extend(["--bs", str(args.bs      )]      )  # in case we need to reduce its size due to CUDA OOM
    UpperCAmelCase          =	args_normal + args_exp

    # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

    UpperCAmelCase          =	run_generate(verbose=__A      )
    # make sure scores are first in the table
    UpperCAmelCase          =	OrderedDict()
    for score in task_score_names[task]:
      UpperCAmelCase          =	scores[score]
    result.update(__A      )
    results.append(__A      )

    # find widest entries
    for k, v in result.items():
      UpperCAmelCase          =	len(str(__A      )      )
      if l > col_widths[k]:
        UpperCAmelCase          =	l

  UpperCAmelCase          =	sorted(__A ,							key=operator.itemgetter(*task_score_names[task]      ) ,							reverse=__A      )
  print(" | ".join([F"{col:{col_widths[col]}}" for col in col_names]      )      )
  print(" | ".join([F"{'-'*col_widths[col]}" for col in col_names]      )      )
  for row in results_sorted:
    print(" | ".join([F"{row[col]:{col_widths[col]}}" for col in col_names]      )      )

  UpperCAmelCase          =	results_sorted[0]
  for score in task_score_names[task]:
    del best[score]
  UpperCAmelCase          =	[F"--{k} {v}" for k, v in best.items()]
  UpperCAmelCase          =	["--bs", str(args.bs      )]
  if args.info:
    print(F"\nInfo: {args.info}"      )
  print("\nBest score args:"      )
  print(" ".join(args_main + best_args + dyn_args      )      )

  return results_sorted


if __name__ == "__main__":
      # Usage:
      # [normal-run_eval_search.py cmd plus] \
      # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
      #
      # Example:
      # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
      # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
      # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
      # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
      run_search()






import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available
def   _lowerCAmelCase(						__A ,							__A ,							__A ,							__A=None      ):
  UpperCAmelCase          =	None
  # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
  if str(weight.dtype      ) == "torch.bfloat16":
    # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
    UpperCAmelCase          =	weight.view(torch.intaa      )
    UpperCAmelCase          =	"bfloat16"
  UpperCAmelCase          =	weight.cpu().numpy()
  UpperCAmelCase          =	os.path.join(__A ,							F"{weight_name}.dat"      )
  if index is not None:
    if dtype is None:
      UpperCAmelCase          =	str(array.dtype      )
    UpperCAmelCase          =	{"dtype": dtype, "shape": list(array.shape      )}
  if array.ndim == 0:
    UpperCAmelCase          =	array[None]
  UpperCAmelCase          =	np.memmap(__A ,							dtype=array.dtype ,							mode="w+" ,							shape=array.shape      )
  UpperCAmelCase          =	array[:]
  file_array.flush()
  return index
def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	tuple(weight_info["shape"]      )
  if shape == ():
    # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
    UpperCAmelCase          =	(1,)

  UpperCAmelCase          =	weight_info["dtype"]
  if dtype == "bfloat16":
    # NumPy does not support bfloat16 so this was saved as a int16
    UpperCAmelCase          =	"int16"

  UpperCAmelCase          =	np.memmap(__A ,							dtype=__A ,							shape=__A ,							mode="r"      )

  if len(weight_info["shape"]      ) == 0:
    UpperCAmelCase          =	weight[0]
  UpperCAmelCase          =	torch.tensor(__A      )
  if weight_info["dtype"] == "bfloat16":
    UpperCAmelCase          =	weight.view(torch.bfloataa      )

  return weight
def   _lowerCAmelCase(						__A ,							__A      ):
  if index is None or len(__A      ) == 0:
    # Nothing to save
    return

  UpperCAmelCase          =	os.path.join(__A ,							"index.json"      )
  if os.path.isfile(__A      ):
    with open(__A ,							"r" ,							encoding="utf-8"      ) as f:
      UpperCAmelCase          =	json.load(__A      )
  else:
    UpperCAmelCase          =	{}
  current_index.update(__A      )

  with open(__A ,							"w" ,							encoding="utf-8"      ) as f:
    json.dump(__A ,							__A ,							indent=2      )
def   _lowerCAmelCase(						__A ,							__A      ):
  os.makedirs(__A ,							exist_ok=__A      )
  UpperCAmelCase          =	{}
  for name, parameter in state_dict.items():
    UpperCAmelCase          =	offload_weight(__A ,							__A ,							__A ,							index=__A      )

  # Update index
  save_offload_index(__A ,							__A      )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Tuple      ,    lowerCAmelCase__		:			Mapping      ,    lowerCAmelCase__		:			str     )     ->   Tuple:
   UpperCAmelCase          =	dataset
   UpperCAmelCase          =	prefix


 def __getitem__(    self		:			Dict      ,    lowerCAmelCase__		:			Any     )     ->   Optional[int]:
   return self.dataset[f"{self.prefix}{key}"]


 def __iter__(    self		:			Optional[int]     )     ->   Any:
   return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )


 def __len__(    self		:			Union[str, Any]     )     ->   int:
   return len(self.dataset     )






class     __magic_name__     (							_snake_case     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			Dict[str, torch.Tensor] = None      ,    lowerCAmelCase__		:			Optional[Union[str, os.PathLike]] = None      ,    lowerCAmelCase__		:			Mapping = None      ,    lowerCAmelCase__		:			Dict=None      ,    )     ->   Dict:
   if state_dict is None and save_folder is None:
     raise ValueError("Need either a `state_dict` or a `save_folder` containing offloaded weights."     )

   UpperCAmelCase          =	{} if state_dict is None else state_dict
   UpperCAmelCase          =	save_folder
   if index is None and save_folder is not None:
     with open(os.path.join(lowerCAmelCase__      ,    "index.json"     )     ) as f:
       UpperCAmelCase          =	json.load(lowerCAmelCase__     )
   UpperCAmelCase          =	{} if index is None else index
   UpperCAmelCase          =	list(self.state_dict.keys()     )
   self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
   UpperCAmelCase          =	device


 def __getitem__(    self		:			str      ,    lowerCAmelCase__		:			str     )     ->   Dict:
   # State dict gets priority
   if key in self.state_dict:
     return self.state_dict[key]
   UpperCAmelCase          =	self.index[key]
   if weight_info.get("safetensors_file"     ) is not None:
     if not is_safetensors_available():
       raise ImportError("These offloaded weights require the use of safetensors: `pip install safetensors`."     )

     from safetensors import safe_open

     UpperCAmelCase          =	"cpu" if self.device is None else self.device
     with safe_open(weight_info["safetensors_file"]      ,    framework="pt"      ,    device=lowerCAmelCase__     ) as f:
       UpperCAmelCase          =	f.get_tensor(weight_info.get("weight_name"      ,    lowerCAmelCase__     )     )

     if "dtype" in weight_info:
       return tensor.to(getattr(lowerCAmelCase__      ,    weight_info["dtype"]     )     )
     else:
       return tensor

   UpperCAmelCase          =	os.path.join(self.save_folder      ,    f"{key}.dat"     )
   return load_offloaded_weight(lowerCAmelCase__      ,    lowerCAmelCase__     )


 def __iter__(    self		:			str     )     ->   Dict:
   return iter(self.all_keys     )


 def __len__(    self		:			Optional[Any]     )     ->   List[Any]:
   return len(self.all_keys     )

def   _lowerCAmelCase(						__A ,							__A      ):
  UpperCAmelCase          =	{}
  for module_name in submodule_names:
    # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
    # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
    result.update(
        {
            key: param
            for key, param in state_dict.items()
            if key == module_name or key.startswith(module_name + "."      )
        }      )
  return result



