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)
SCREAMING_SNAKE_CASE :	List[str]							     =		logging.getLogger()

SCREAMING_SNAKE_CASE :	Any							     =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class    A_ (       a_ ):

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

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str = "pytorch"       ):
   __a       =  self.get_auto_remove_tmp_dir()
   __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							"output"       )
   __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							"data"       )
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE       )

   __a       =  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"       )

   __a       =  [sys.executable, str(Path(finetune_rag.__file__       ).resolve()       )] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE     ,							env=self.get_env()       )

   __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							"metrics.json"       )
   with open(__SCREAMING_SNAKE_CASE       ) as f:
    __a       =  json.load(__SCREAMING_SNAKE_CASE       )
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  self._run_finetune(gpus=1       )
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]     ,							0.2       )

  @require_torch_multi_gpu
  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self._run_finetune(gpus=2       )
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]     ,							0.2       )

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  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 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  self._run_finetune(gpus=1     ,							distributed_retriever="ray"       )
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]     ,							0.2       )



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


