"""simple docstring"""



import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__:Union[str, Any]		       =       [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-classification''',
        '''language-modeling''',
        '''summarization''',
        '''token-classification''',
        '''question-answering''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__:Dict		       =       logging.getLogger()



def      _lowerCamelCase(     ):
       __a	      =  argparse.ArgumentParser()
       parser.add_argument("-f"							)
       __a	      =  parser.parse_args()
       return args.f



def      _lowerCamelCase(     a					,  a="eval"							):
       __a	      =  os.path.join(snake_case_					,  F"{split}_results.json"							)
       if os.path.exists(snake_case_							):
              with open(snake_case_					,  "r"							) as f:
                     return json.load(snake_case_							)
       raise ValueError(F"can't find {path}"							)


SCREAMING_SNAKE_CASE__:Dict		       =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class       snake_case__	(    UpperCAmelCase__    ):


 def 		a__					(      self		):
        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_flax_glue.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__		)
               self.assertGreaterEqual(result["eval_accuracy"]  ,						0.75		)


 @slow
 def 		a__					(      self		):
        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_clm_flax.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__		)
               self.assertLess(result["eval_perplexity"]  ,						100		)


 @slow
 def 		a__					(      self		):
        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_summarization_flax.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__  ,						split="test"		)
               self.assertGreaterEqual(result["test_rouge1"]  ,						10		)
               self.assertGreaterEqual(result["test_rouge2"]  ,						2		)
               self.assertGreaterEqual(result["test_rougeL"]  ,						7		)
               self.assertGreaterEqual(result["test_rougeLsum"]  ,						7		)


 @slow
 def 		a__					(      self		):
        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_mlm_flax.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__		)
               self.assertLess(result["eval_perplexity"]  ,						42		)


 @slow
 def 		a__					(      self		):
        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_ta_mlm_flax.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__		)
               self.assertGreaterEqual(result["eval_accuracy"]  ,						0.42		)


 @slow
 def 		a__					(      self		):
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __a	      =  7 if get_gpu_count() > 1 else 2

        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_flax_ner.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__		)
               self.assertGreaterEqual(result["eval_accuracy"]  ,						0.75		)
               self.assertGreaterEqual(result["eval_f1"]  ,						0.3		)






 @slow
 def 		a__					(      self		):
        __a	      =  self.get_auto_remove_tmp_dir()
        __a	      =  F"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(SCREAMING_SNAKE_CASE__  ,						"argv"  ,						SCREAMING_SNAKE_CASE__		):
               run_qa.main()
               __a	      =  get_results(SCREAMING_SNAKE_CASE__		)
               self.assertGreaterEqual(result["eval_f1"]  ,						30		)
               self.assertGreaterEqual(result["eval_exact"]  ,						30		)

'''simple docstring'''

from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       float
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None
 _SCREAMING_SNAKE_CASE     :       TreeNode | None      	=					None







def      UpperCamelCase_						(					snake_case_       :       TreeNode | None      )					->     bool:






 '''simple docstring'''

 def is_valid_tree(snake_case_       :       TreeNode | None      ) -> bool:
  if node is None:
   return True

  if not isinstance(snake_case_						,		snake_case_      ):
   return False

  try:
   float(node.data      )
  except (TypeError, ValueError):
   return False

  return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

 if not is_valid_tree(snake_case_      ):
  raise ValueError(
      """Each node should be type of TreeNode and data should be float."""      )

 def is_binary_search_tree_recursive_check(
     snake_case_       :       TreeNode | None						,		snake_case_       :       float						,		snake_case_       :       float      ) -> bool:

  if node is None:
   return True

  return (
      left_bound < node.data < right_bound
      and is_binary_search_tree_recursive_check(node.left						,		snake_case_						,		node.data      )
      and is_binary_search_tree_recursive_check(
          node.right						,		node.data						,		snake_case_      )
  )

 return is_binary_search_tree_recursive_check(snake_case_						,		-float("""inf"""      )						,		float("""inf"""      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






