
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCamelCase__    :		List[Any]          =   logging.getLogger()

def 		UpperCamelCase	(      )		->	Tuple:


  '''simple docstring'''






  lowercase__  :   Optional[Any]    		=      argparse.ArgumentParser()
  parser.add_argument("""-f"""   )
  lowercase__  :   Union[str, Any]    		=      parser.parse_args()
  return args.f

def 		UpperCamelCase	(      lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   List[Any]    		=      {}
  lowercase__  :   Union[str, Any]    		=      os.path.join(lowercase_ , """all_results.json"""   )
  if os.path.exists(lowercase_   ):
    with open(lowercase_ , """r"""   ) as f:
      lowercase__  :   List[Any]    		=      json.load(lowercase_   )
  else:
    raise ValueError(F'can\'t find {path}'   )
  return results

def 		UpperCamelCase	(      )		->	Optional[Any]:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      torch.cuda.is_available() and torch_device == """cuda"""
  return is_using_cuda and is_apex_available()


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



class       _snake_case   (	UpperCAmelCase_							):


 @classmethod
 def      lowercase__     (					cls):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      tempfile.mkdtemp()
   lowercase__  :   Tuple    		=      os.path.join(cls.tmpdir					,  """default_config.yml""")
   write_basic_config(save_location=cls.configPath)
   lowercase__  :   List[str]    		=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]


 @classmethod
 def      lowercase__     (					cls):


   '''simple docstring'''




   shutil.rmtree(cls.tmpdir)


 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[Any]    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   Dict    		=      f'\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.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            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

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

   run_command(self._launch_args + testargs)
   lowercase__  :   Optional[Any]    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertGreaterEqual(result["""eval_accuracy"""]					,  0.7_5)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """glue_no_trainer""")))


 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   str    		=      f'\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

   if torch.cuda.device_count() > 1:
     # Skipping because there are not enough batches to train the model + would need a drop_last to work.
     return

   run_command(self._launch_args + testargs)
   lowercase__  :   Optional[Any]    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertLess(result["""perplexity"""]					,  1_00)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """clm_no_trainer""")))


 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   str    		=      f'\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.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            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   Dict    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertLess(result["""perplexity"""]					,  42)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """mlm_no_trainer""")))


 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      7 if get_gpu_count() > 1 else 2

   lowercase__  :   List[Any]    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   Any    		=      f'\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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            --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            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   Optional[Any]    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertGreaterEqual(result["""eval_accuracy"""]					,  0.7_5)
   self.assertLess(result["""train_loss"""]					,  0.5)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """ner_no_trainer""")))


 @unittest.skip(reason="""Fix me @muellerzr""")
 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   Tuple    		=      f'\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   Optional[int]    		=      get_results(SCREAMING_SNAKE_CASE_)
   # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
   self.assertGreaterEqual(result["""eval_f1"""]					,  28)
   self.assertGreaterEqual(result["""eval_exact"""]					,  28)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """qa_no_trainer""")))


 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   Tuple    		=      f'\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   List[Any]    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertGreaterEqual(result["""eval_accuracy"""]					,  0.8)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """swag_no_trainer""")))


 @slow
 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Any    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   Dict    		=      f'\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   Union[str, Any]    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertGreaterEqual(result["""eval_rouge1"""]					,  10)
   self.assertGreaterEqual(result["""eval_rouge2"""]					,  2)
   self.assertGreaterEqual(result["""eval_rougeL"""]					,  7)
   self.assertGreaterEqual(result["""eval_rougeLsum"""]					,  7)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """summarization_no_trainer""")))


 @slow
 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   List[str]    		=      f'\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/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            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\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            --checkpointing_steps epoch\n            --with_tracking\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   Any    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertGreaterEqual(result["""eval_bleu"""]					,  30)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """epoch_0""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """translation_no_trainer""")))


 @slow
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   List[str]    		=      logging.StreamHandler(sys.stdout)
   logger.addHandler(SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[int]    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   int    		=      f'\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        '.split()

   run_command(self._launch_args + testargs)
   lowercase__  :   int    		=      get_results(SCREAMING_SNAKE_CASE_)
   self.assertGreaterEqual(result["""eval_overall_accuracy"""]					,  0.1_0)



 @mock.patch.dict(os.environ					,  {"""WANDB_MODE""": """offline"""})
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Dict    		=      self.get_auto_remove_tmp_dir()
   lowercase__  :   List[Any]    		=      f'\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        '.split()

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

   run_command(self._launch_args + testargs)
   lowercase__  :   Any    		=      get_results(SCREAMING_SNAKE_CASE_)
   # The base model scores a 25%
   self.assertGreaterEqual(result["""eval_accuracy"""]					,  0.6)
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """step_1""")))
   self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_					,  """image_classification_no_trainer""")))








lowerCamelCase__    :		str          =   [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_ , lowercase_   )		->	str:


  '''simple docstring'''






  lowercase__  :   Any    		=      [False] * len(lowercase_   )
  lowercase__  :   List[Any]    		=      [s]
  lowercase__  :   int    		=      True

  while queue:
    lowercase__  :   Dict    		=      queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowercase_   )
        lowercase__  :   Optional[Any]    		=      True
        lowercase__  :   int    		=      u

  return visited[t]

def 		UpperCamelCase	(      lowercase_ , lowercase_ , lowercase_   )		->	int:


  '''simple docstring'''






  lowercase__  :   Optional[int]    		=      [-1] * (len(lowercase_   ))
  lowercase__  :   Optional[Any]    		=      0
  lowercase__  :   List[Any]    		=      []
  lowercase__  :   Optional[Any]    		=      [i[:] for i in graph]  # Record original cut, copy.
  while bfs(lowercase_ , lowercase_ , lowercase_ , lowercase_   ):
    lowercase__  :   Optional[int]    		=      float("""Inf"""   )
    lowercase__  :   Tuple    		=      sink

    while s != source:
      # Find the minimum value in select path
      lowercase__  :   List[str]    		=      min(lowercase_ , graph[parent[s]][s]   )
      lowercase__  :   List[str]    		=      parent[s]

    max_flow += path_flow
    lowercase__  :   str    		=      sink

    while v != source:
      lowercase__  :   int    		=      parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase__  :   Union[str, Any]    		=      parent[v]

  for i in range(len(lowercase_   )   ):
    for j in range(len(graph[0]   )   ):
      if graph[i][j] == 0 and temp[i][j] > 0:
        res.append((i, j)   )

  return res


if __name__ == "__main__":
     print(mincut(test_graph, source=0, sink=5))






