
'''simple docstring'''



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

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase__ :						int    							=					[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-generation",
        "text-classification",
        "token-classification",
        "language-modeling",
        "multiple-choice",
        "question-answering",
        "summarization",
        "translation",
        "image-classification",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
     import run_clm
     import run_image_classification
     import run_mlm
     import run_ner
     import run_qa as run_squad
     import run_summarization
     import run_swag
     import run_text_classification
     import run_translation


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase__ :						Dict    							=					logging.getLogger()
def 				lowerCAmelCase_		(			)       ->       Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser()
   parser.add_argument("""-f"""  )
   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()
   return args.f
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			{}
   _SCREAMING_SNAKE_CASE       	=			os.path.join(SCREAMING_SNAKE_CASE_      , """all_results.json"""  )
   if os.path.exists(SCREAMING_SNAKE_CASE_  ):
      with open(SCREAMING_SNAKE_CASE_      , """r"""  ) as f:
         _SCREAMING_SNAKE_CASE       	=			json.load(SCREAMING_SNAKE_CASE_  )
   else:
      raise ValueError(F"can't find {path}"  )
   return results
def 				lowerCAmelCase_		(			)       ->       Any:




   """simple docstring"""
   return bool(tf.config.list_physical_devices("""GPU"""  )  )


UpperCamelCase__ :						Optional[Any]    							=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 				_a			(_lowerCamelCase):





       """simple docstring"""




       @skip("""Skipping until shape inference for to_tf_dataset PR is merged.""" )
       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			F"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

          if is_cuda_available():
             testargs.append("""--fp16""" )

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_text_classification.main()
             # Reset the mixed precision policy so we don't break other tests
             tf.keras.mixed_precision.set_global_policy("""float32""" )
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""eval_accuracy"""]    ,							0.75 )






       def     UpperCamelCase			(							self )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			F"\n            run_clm.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 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

          if len(tf.config.list_physical_devices("""GPU""" ) ) > 1:
             # Skipping because there are not enough batches to train the model + would need a drop_last to work.
             return

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_clm.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertLess(result["""eval_perplexity"""]    ,							1_00 )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			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            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_mlm.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertLess(result["""eval_perplexity"""]    ,							42 )






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

          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			F"\n            run_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            --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(A__    ,							"""argv"""    ,							A__ ):
             run_ner.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""accuracy"""]    ,							0.75 )






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			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            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_squad.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""f1"""]    ,							30 )
             self.assertGreaterEqual(result["""exact"""]    ,							30 )






       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			F"\n            run_swag.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=20\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_swag.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""val_accuracy"""]    ,							0.8 )






       @slow
       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			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            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_summarization.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""rouge1"""]    ,							10 )
             self.assertGreaterEqual(result["""rouge2"""]    ,							2 )
             self.assertGreaterEqual(result["""rougeL"""]    ,							7 )
             self.assertGreaterEqual(result["""rougeLsum"""]    ,							7 )






       @slow
       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			F"\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        ".split()

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_translation.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""bleu"""]    ,							30 )






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.get_auto_remove_tmp_dir()
          _SCREAMING_SNAKE_CASE       	=			F"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

          with patch.object(A__    ,							"""argv"""    ,							A__ ):
             run_image_classification.main()
             _SCREAMING_SNAKE_CASE       	=			get_results(A__ )
             self.assertGreaterEqual(result["""accuracy"""]    ,							0.7 )








'''simple docstring'''



def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   print("""\nVertex Distance"""  )
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if dist[i] != float("""inf"""  ):
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , int(dist[i]  )      , end="""\t"""  )
      else:
         print(SCREAMING_SNAKE_CASE_      , """\t"""      , """INF"""      , end="""\t"""  )
      print()
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       Optional[Any]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			float("""inf"""  )
   _SCREAMING_SNAKE_CASE       	=			-1
   for i in range(SCREAMING_SNAKE_CASE_  ):
      if (not vset[i]) and mdist[i] < min_val:
         _SCREAMING_SNAKE_CASE       	=			i
         _SCREAMING_SNAKE_CASE       	=			mdist[i]
   return min_ind
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )       ->       int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			[float("""inf"""  ) for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			[False for _ in range(SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			0.0

   for _ in range(v - 1  ):
      _SCREAMING_SNAKE_CASE       	=			min_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			True

      for i in range(SCREAMING_SNAKE_CASE_  ):
         if (
             (not vset[i])
             and graph[u][i] != float("""inf"""  )
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _SCREAMING_SNAKE_CASE       	=			mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__ :						int    							=					int(input("Enter number of vertices: ").strip())
     UpperCamelCase__ :						Tuple    							=					int(input("Enter number of edges: ").strip())

     UpperCamelCase__ :						Optional[Any]    							=					[[float("inf") for i in range(V)] for j in range(V)]

     for i in range(V):
          UpperCamelCase__ :						int    							=					0.0

     for i in range(E):
          print("\nEdge ", i + 1)
          UpperCamelCase__ :						Union[str, Any]    							=					int(input("Enter source:").strip())
          UpperCamelCase__ :						Tuple    							=					int(input("Enter destination:").strip())
          UpperCamelCase__ :						Any    							=					float(input("Enter weight:").strip())
          UpperCamelCase__ :						List[Any]    							=					weight

     UpperCamelCase__ :						Optional[Any]    							=					int(input("\nEnter shortest path source:").strip())
     dijkstra(graph, V, gsrc)






