
__UpperCAmelCase   						=     float("inf")





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      [0] * n
          SCREAMING_SNAKE_CASE_	=      [0] * n
          SCREAMING_SNAKE_CASE_	=      [0] * n
          SCREAMING_SNAKE_CASE_	=      [[] for _ in range(__lowercase	)]



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A ,     _A=0	)				->				Dict:
          self.adj[a].append([b, len(self.adj[b]	), c, 0]	)
          self.adj[b].append([a, len(self.adj[a]	) - 1, rcap, 0]	)



       def     _UpperCamelCase	(							self ,     _A ,     _A ,     _A	)				->				List[Any]:
          if vertex == sink or not flow:
             return flow

          for i in range(self.ptr[vertex] ,     len(self.adj[vertex]	)	):
             SCREAMING_SNAKE_CASE_	=      self.adj[vertex][i]
             if self.lvl[e[0]] == self.lvl[vertex] + 1:
                SCREAMING_SNAKE_CASE_	=      self.depth_first_search(e[0] ,     __lowercase ,     min(__lowercase ,     e[2] - e[3]	)	)
                if p:
                   self.adj[vertex][i][3] += p
                   self.adj[e[0]][e[1]][3] -= p
                   return p
             SCREAMING_SNAKE_CASE_	=      self.ptr[vertex] + 1
          return 0




       def     _UpperCamelCase	(							self ,     _A ,     _A	)				->				List[Any]:
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      0, source
          for l in range(31	):  # noqa: E741  l = 30 maybe faster for random data
             while True:
                SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      [0] * len(self.q	), [0] * len(self.q	)
                SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      0, 1, 1
                while qi < qe and not self.lvl[sink]:
                   SCREAMING_SNAKE_CASE_	=      self.q[qi]
                   qi += 1
                   for e in self.adj[v]:
                      if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                         SCREAMING_SNAKE_CASE_	=      e[0]
                         qe += 1
                         SCREAMING_SNAKE_CASE_	=      self.lvl[v] + 1

                SCREAMING_SNAKE_CASE_	=      self.depth_first_search(__lowercase ,     __lowercase ,     __lowercase	)
                while p:
                   flow += p
                   SCREAMING_SNAKE_CASE_	=      self.depth_first_search(__lowercase ,     __lowercase ,     __lowercase	)

                if not self.lvl[sink]:
                   break

          return flow







__UpperCAmelCase   						=     Dinic(10)
__UpperCAmelCase   						=     0
__UpperCAmelCase   						=     9
for vertex in range(1, 5):
   graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
   graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
   graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))






'''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					:			str				     =					[
    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					:			Optional[Any]				     =					logging.getLogger()

def 				__magic_name__(    ):
						__lowerCAmelCase											=					argparse.ArgumentParser()
						parser.add_argument('''-f''')
						__lowerCAmelCase											=					parser.parse_args()
						return args.f

def 				__magic_name__(    lowerCamelCase):
						__lowerCAmelCase											=					{}
						__lowerCAmelCase											=					os.path.join(lowerCamelCase,					'''all_results.json''')
						if os.path.exists(lowerCamelCase):
												with open(lowerCamelCase,					'''r''') as f:
																		__lowerCAmelCase											=					json.load(lowerCamelCase)
						else:
												raise ValueError(F"""can't find {path}""")
						return results

def 				__magic_name__(    ):
						return bool(tf.config.list_physical_devices('''GPU'''))


_UpperCAmelCase					:			str				     =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class    a__					(			__A   ):







				"""simple docstring"""



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

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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_text_classification.main()
																# Reset the mixed precision policy so we don't break other tests
																tf.keras.mixed_precision.set_global_policy('''float32'''		)
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''eval_accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_clm.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 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.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(__lowercase							,      '''argv'''							,      __lowercase		):
																run_clm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      1_00		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_mlm.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertLess(result['''eval_perplexity''']							,      42		)




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

										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					F"""
            run_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
            --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_ner.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7_5		)




				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					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
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_squad.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''f1''']							,      30		)
																self.assertGreaterEqual(result['''exact''']							,      30		)




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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_swag.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''val_accuracy''']							,      0.8		)




				@slow
				def        _snake_case	(self		):
										__lowerCAmelCase											=					self.get_auto_remove_tmp_dir()
										__lowerCAmelCase											=					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
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

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




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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_translation.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''bleu''']							,      30		)




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

										with patch.object(__lowercase							,      '''argv'''							,      __lowercase		):
																run_image_classification.main()
																__lowerCAmelCase											=					get_results(__lowercase		)
																self.assertGreaterEqual(result['''accuracy''']							,      0.7		)

