


'''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


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


def       _lowercase							(					):
 __A	:		Tuple	  =      argparse.ArgumentParser()
 parser.add_argument('-f'      )
 __A	:		str	  =      parser.parse_args()
 return args.f


def       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       List[Any]="eval"      ):
 __A	:		Tuple	  =      os.path.join(UpperCamelCase__,					f"""{split}_results.json"""      )
 if os.path.exists(UpperCamelCase__      ):
  with open(UpperCamelCase__,					'r'      ) as f:
   return json.load(UpperCamelCase__      )
 raise ValueError(f"""can't find {path}"""      )


UpperCAmelCase_			:  List[Any]										=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_flax_glue.main()
   __A	:		Union[str, Any]	  =      get_results(__lowercase    )
   self.assertGreaterEqual(result['eval_accuracy']   ,   0.7_5    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      self.get_auto_remove_tmp_dir()
  __A	:		str	  =      F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_clm_flax.main()
   __A	:		List[str]	  =      get_results(__lowercase    )
   self.assertLess(result['eval_perplexity']   ,   100    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      self.get_auto_remove_tmp_dir()
  __A	:		Optional[int]	  =      F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_summarization_flax.main()
   __A	:		int	  =      get_results(__lowercase   ,   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 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      self.get_auto_remove_tmp_dir()
  __A	:		Optional[Any]	  =      F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_mlm_flax.main()
   __A	:		Optional[int]	  =      get_results(__lowercase    )
   self.assertLess(result['eval_perplexity']   ,   42    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.get_auto_remove_tmp_dir()
  __A	:		Optional[Any]	  =      F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_ta_mlm_flax.main()
   __A	:		Dict	  =      get_results(__lowercase    )
   self.assertGreaterEqual(result['eval_accuracy']   ,   0.4_2    )


 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      7 if get_gpu_count() > 1 else 2

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

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_flax_ner.main()
   __A	:		Union[str, Any]	  =      get_results(__lowercase    )
   self.assertGreaterEqual(result['eval_accuracy']   ,   0.7_5    )
   self.assertGreaterEqual(result['eval_f1']   ,   0.3    )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







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

  with patch.object(__lowercase   ,   'argv'   ,   __lowercase    ):
   run_qa.main()
   __A	:		List[str]	  =      get_results(__lowercase    )
   self.assertGreaterEqual(result['eval_f1']   ,   30    )
   self.assertGreaterEqual(result['eval_exact']   ,   30    )










'''simple docstring'''
def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Dict	  =      set()
 # edges = list of graph's edges
 __A	:		Any	  =      get_edges(UpperCamelCase__      )

 # While there are still elements in edges list, take an arbitrary edge
 # (from_node, to_node) and add his extremity to chosen_vertices and then
 # remove all arcs adjacent to the from_node and to_node
 while edges:
  __A  ,__A	:		List[Any]	  =      edges.pop()
  chosen_vertices.add(UpperCamelCase__      )
  chosen_vertices.add(UpperCamelCase__      )
  for edge in edges.copy():
   if from_node in edge or to_node in edge:
    edges.discard(UpperCamelCase__      )
 return chosen_vertices


def       _lowercase							(					UpperCamelCase__							:       dict      ):
 __A	:		Any	  =      set()
 for from_node, to_nodes in graph.items():
  for to_node in to_nodes:
   edges.add((from_node, to_node)      )
 return edges


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 # print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")






