






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


__UpperCAmelCase							:   List[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)

__UpperCAmelCase							:   Tuple							=  logging.getLogger()





def 					lowercase_ (    )     -> Dict:





				'''simple docstring'''


				snake_case__      :List[Any]         =   argparse.ArgumentParser()
				parser.add_argument("-f"      )
				snake_case__      :Tuple         =   parser.parse_args()
				return args.f





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Any="eval"      )     -> Union[str, Any]:





				'''simple docstring'''


				snake_case__      :Any         =   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}'      )


__UpperCAmelCase							:   Any							=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 					_snake_case						(			_A ):






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[int]         =   self.get_auto_remove_tmp_dir()
					snake_case__      :int         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_flax_glue.main()
									snake_case__      :Optional[int]         =   get_results(UpperCamelCase       )
									self.assertGreaterEqual(result["eval_accuracy"]					,0.75       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Union[str, Any]         =   self.get_auto_remove_tmp_dir()
					snake_case__      :int         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_clm_flax.main()
									snake_case__      :Tuple         =   get_results(UpperCamelCase       )
									self.assertLess(result["eval_perplexity"]					,100       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[Any]         =   self.get_auto_remove_tmp_dir()
					snake_case__      :List[Any]         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_summarization_flax.main()
									snake_case__      :str         =   get_results(UpperCamelCase					,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    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :int         =   self.get_auto_remove_tmp_dir()
					snake_case__      :int         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_mlm_flax.main()
									snake_case__      :Optional[Any]         =   get_results(UpperCamelCase       )
									self.assertLess(result["eval_perplexity"]					,42       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Dict         =   self.get_auto_remove_tmp_dir()
					snake_case__      :Any         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_ta_mlm_flax.main()
									snake_case__      :Union[str, Any]         =   get_results(UpperCamelCase       )
									self.assertGreaterEqual(result["eval_accuracy"]					,0.42       )






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

					snake_case__      :Dict         =   self.get_auto_remove_tmp_dir()
					snake_case__      :Any         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_flax_ner.main()
									snake_case__      :List[str]         =   get_results(UpperCamelCase       )
									self.assertGreaterEqual(result["eval_accuracy"]					,0.75       )
									self.assertGreaterEqual(result["eval_f1"]					,0.3       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Any         =   self.get_auto_remove_tmp_dir()
					snake_case__      :int         =   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(UpperCamelCase					,"argv"					,UpperCamelCase       ):
									run_qa.main()
									snake_case__      :Optional[int]         =   get_results(UpperCamelCase       )
									self.assertGreaterEqual(result["eval_f1"]					,30       )
									self.assertGreaterEqual(result["eval_exact"]					,30       )







from __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


				if depth < 0:
								raise ValueError("Depth cannot be less than 0"      )

				if len(__snake_case      ) == 0:
								raise ValueError("Scores cannot be empty"      )

				if depth == height:
								return scores[node_index]

				if is_max:
								return max(
								    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

				return min(
				    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()