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


lowercase_  : 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)

lowercase_  : Union[str, Any]								=					logging.getLogger()







def       A__   (						):
 SCREAMING_SNAKE_CASE__: int=   argparse.ArgumentParser()
 parser.add_argument('''-f'''   )
 SCREAMING_SNAKE_CASE__: str=   parser.parse_args()
 return args.f







def       A__   (						snake_case_			:		str							,					snake_case_			:		Optional[Any]="eval"   ):
 SCREAMING_SNAKE_CASE__: List[str]=   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}'   )


lowercase_  : Optional[int]								=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: str=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Tuple=   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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_flax_glue.main()
      SCREAMING_SNAKE_CASE__: List[str]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.75 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Dict=   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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_clm_flax.main()
      SCREAMING_SNAKE_CASE__: Optional[Any]=   get_results(lowerCAmelCase )
      self.assertLess(result['''eval_perplexity''']			,			100 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Tuple=   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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_summarization_flax.main()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase			,			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     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: str=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_mlm_flax.main()
      SCREAMING_SNAKE_CASE__: List[Any]=   get_results(lowerCAmelCase )
      self.assertLess(result['''eval_perplexity''']			,			42 )
    @slow
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Dict=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Optional[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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_ta_mlm_flax.main()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.42 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[str]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   7 if get_gpu_count() > 1 else 2

     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: int=   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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_flax_ner.main()
      SCREAMING_SNAKE_CASE__: List[Any]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.75 )
      self.assertGreaterEqual(result['''eval_f1''']			,			0.3 )



    @slow
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: str=   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(lowerCAmelCase			,			'''argv'''			,			lowerCAmelCase ):
      run_qa.main()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_f1''']			,			30 )
      self.assertGreaterEqual(result['''eval_exact''']			,			30 )









"""simple docstring"""
def 							snake_case (  A__		):
			UpperCAmelCase_	:					int     	=   [0 for i in range(len(A__		)		)]

			# initialize interval's left pointer and right pointer
			UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   0, 0

			for i in range(1      ,len(A__		)		):
						# case when current index is inside the interval
						if i <= right_pointer:
									UpperCAmelCase_	:					Any     	=   min(right_pointer - i + 1      ,z_result[i - left_pointer]		)
									UpperCAmelCase_	:					Union[str, Any]     	=   min_edge

						while go_next(A__      ,A__      ,A__		):
									z_result[i] += 1

						# if new index's result gives us more right interval,
						# we've to update left_pointer and right_pointer
						if i + z_result[i] - 1 > right_pointer:
									UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   i, i + z_result[i] - 1

			return z_result

def 							snake_case (  A__      ,A__      ,A__		):
			return i + z_result[i] < len(A__		) and s[z_result[i]] == s[i + z_result[i]]

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Optional[int]     	=   0
			# concatenate 'pattern' and 'input_str' and call z_function
			# with concatenated string
			UpperCAmelCase_	:					Tuple     	=   z_function(pattern + input_str		)

			for val in z_result:
						# if value is greater then length of the pattern string
						# that means this index is starting position of substring
						# which is equal to pattern string
						if val >= len(A__		):
									answer += 1

			return answer


if __name__ == "__main__":
			import doctest

			doctest.testmod()



