
"""simple docstring"""
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowercase_       :						List[str]     					=	logging.getLogger()

lowercase_       :						Tuple     					=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       os.makedirs(snake_case__  ,     exist_ok=snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = {"source": "What is love ?", "target": "life"}
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = {"train": 12, "val": 2, "test": 2}
       for split in ["train", "test", "val"]:
         for field in ["source", "target"]:
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "\n".join([contents[field]] * n_lines[split]				)
           with open(os.path.join(snake_case__  ,     F'''{split}.{field}'''				)  ,     "w"				) as f:
             f.write(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = "pytorch"				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.get_auto_remove_tmp_dir()
       _SCREAMING_SNAKE_CASE   :				Any							    = os.path.join(snake_case__  ,     "output"				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = os.path.join(snake_case__  ,     "data"				)
       self._create_dummy_data(data_dir=snake_case__				)

       _SCREAMING_SNAKE_CASE   :				int							    = F'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

       if gpus > 0:
         testargs.append(F'''--gpus={gpus}'''				)
         if is_apex_available():
           testargs.append("--fp16"				)
       else:
         testargs.append("--gpus=0"				)
         testargs.append("--distributed_backend=ddp_cpu"				)
         testargs.append("--num_processes=2"				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
       execute_subprocess_async(snake_case__  ,     env=self.get_env()				)

       _SCREAMING_SNAKE_CASE   :				List[Any]							    = os.path.join(snake_case__  ,     "metrics.json"				)
       with open(snake_case__				) as f:
         _SCREAMING_SNAKE_CASE   :				str							    = json.load(snake_case__				)
       return result
     @require_torch_gpu
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self._run_finetune(gpus=1				)
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,     0.2				)
     @require_torch_multi_gpu
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self._run_finetune(gpus=2				)
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,     0.2				)
     @require_torch_gpu
     @require_ray
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = self._run_finetune(gpus=1  ,     distributed_retriever="ray"				)
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,     0.2				)




     @require_torch_multi_gpu
     @require_ray
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Any							    = self._run_finetune(gpus=1  ,     distributed_retriever="ray"				)
       self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,     0.2				)





"""simple docstring"""
from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt





def    _lowerCAmelCase    (     lowerCamelCase__							:							Sequence[float],						lowerCamelCase__							:							int,						lowerCamelCase__							:							int				)  ->    tuple[int | None, int | None, float]:
  if not arr:
    return None, None, 0
  if low == high:
    return low, high, arr[low]

  _SCREAMING_SNAKE_CASE   :				Any							    = (low + high) // 2
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Optional[int]							    = max_subarray(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[str]							    = max_subarray(lowerCamelCase__,						mid + 1,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				str							    = max_cross_sum(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  if left_sum >= right_sum and left_sum >= cross_sum:
    return left_low, left_high, left_sum
  elif right_sum >= left_sum and right_sum >= cross_sum:
    return right_low, right_high, right_sum
  return cross_left, cross_right, cross_sum





def    _lowerCAmelCase    (     lowerCamelCase__							:							Sequence[float],						lowerCamelCase__							:							int,						lowerCamelCase__							:							int,						lowerCamelCase__							:							int				)  ->    tuple[int, int, float]:
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				List[Any]							    = float("-inf"				), -1
  _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Dict							    = float("-inf"				), -1

  _SCREAMING_SNAKE_CASE   :				int | float							    = 0
  for i in range(lowerCamelCase__,						low - 1,						-1				):
    summ += arr[i]
    if summ > left_sum:
      _SCREAMING_SNAKE_CASE   :				int							    = summ
      _SCREAMING_SNAKE_CASE   :				Tuple							    = i

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = 0
  for i in range(mid + 1,						high + 1				):
    summ += arr[i]
    if summ > right_sum:
      _SCREAMING_SNAKE_CASE   :				int							    = summ
      _SCREAMING_SNAKE_CASE   :				Any							    = i

  return max_left, max_right, (left_sum + right_sum)





def    _lowerCAmelCase    (     lowerCamelCase__							:							int				)  ->    float:
  _SCREAMING_SNAKE_CASE   :				Any							    = [randint(1,						lowerCamelCase__				) for _ in range(lowerCamelCase__				)]
  _SCREAMING_SNAKE_CASE   :				int							    = time.time()
  max_subarray(lowerCamelCase__,						0,						input_size - 1				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = time.time()
  return end - start





def    _lowerCAmelCase    (     )  ->    None:
  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = [1_0, 1_0_0, 1_0_0_0, 1_0_0_0_0, 5_0_0_0_0, 1_0_0_0_0_0, 2_0_0_0_0_0, 3_0_0_0_0_0, 4_0_0_0_0_0, 5_0_0_0_0_0]
  _SCREAMING_SNAKE_CASE   :				int							    = [time_max_subarray(lowerCamelCase__				) for input_size in input_sizes]
  print("No of Inputs\t\tTime Taken"				)
  for input_size, runtime in zip(lowerCamelCase__,						lowerCamelCase__				):
    print(lowerCamelCase__,						"\t\t",						lowerCamelCase__				)
  plt.plot(lowerCamelCase__,						lowerCamelCase__				)
  plt.xlabel("Number of Inputs"				)
  plt.ylabel("Time taken in seconds"				)
  plt.show()


if __name__ == "__main__":
      from doctest import testmod

      testmod()



