
'''simple docstring'''



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)

_lowercase    =		logging.getLogger()


def A				():
						_lowerCAmelCase  						=			argparse.ArgumentParser()
						parser.add_argument("""-f"""		)
						_lowerCAmelCase  						=			parser.parse_args()
						return args.f


def A				(__lowerCamelCase				:str		):
						_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 A				():
						_lowerCAmelCase  						=			torch.cuda.is_available() and torch_device == """cuda"""
						return is_using_cuda and is_apex_available()


_lowercase    =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@classmethod
						def  _lowercase     ( cls				):

												"""simple docstring"""



												_lowerCAmelCase  						=			tempfile.mkdtemp()
												_lowerCAmelCase  						=			os.path.join(cls.tmpdir   ,       """default_config.yml"""				)
												write_basic_config(save_location=cls.configPath				)
												_lowerCAmelCase  						=			["""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"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.75				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """glue_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertLess(result["""perplexity"""]   ,       100				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """clm_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertLess(result["""perplexity"""]   ,       42				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """mlm_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			7 if get_gpu_count() > 1 else 2

												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.75				)
												self.assertLess(result["""train_loss"""]   ,       0.5				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """ner_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												# 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(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """qa_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.8				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """swag_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												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(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """summarization_no_trainer"""				)				)				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertGreaterEqual(result["""eval_bleu"""]   ,       30				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """epoch_0"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """translation_no_trainer"""				)				)				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			logging.StreamHandler(sys.stdout				)
												logger.addHandler(_lowercase				)

												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												self.assertGreaterEqual(result["""eval_overall_accuracy"""]   ,       0.10				)




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

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_auto_remove_tmp_dir()
												_lowerCAmelCase  						=			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				)
												_lowerCAmelCase  						=			get_results(_lowercase				)
												# The base model scores a 25%
												self.assertGreaterEqual(result["""eval_accuracy"""]   ,       0.6				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """step_1"""				)				)				)
												self.assertTrue(os.path.exists(os.path.join(_lowercase   ,       """image_classification_no_trainer"""				)				)				)


'''simple docstring'''



import heapq as hq
import math
from collections.abc import Iterator




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			str(id_				)
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			{}  # {vertex:distance}




						def __lt__( self   ,       _lowercase				):

												"""simple docstring"""



												return self.key < other.key




						def __repr__( self				):

												"""simple docstring"""



												return self.id




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												self.neighbors.append(_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			weight






def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:Optional[int]		):
						# add the neighbors:
						graph[a - 1].add_neighbor(graph[b - 1]		)
						graph[b - 1].add_neighbor(graph[a - 1]		)
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]			,     __lowerCamelCase		)
						graph[b - 1].add_edge(graph[a - 1]			,     __lowerCamelCase		)


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:Vertex		):
						_lowerCAmelCase  						=			[]
						for u in graph:
												_lowerCAmelCase  						=			math.inf
												_lowerCAmelCase  						=			None
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			graph[:]
						while q:
												_lowerCAmelCase  						=			min(__lowerCamelCase		)
												q.remove(__lowerCamelCase		)
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								_lowerCAmelCase  						=			u
																								_lowerCAmelCase  						=			u.edges[v.id]
						for i in range(1			,     len(__lowerCamelCase		)		):
												a.append((int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)		)
						return a


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:Vertex		):
						for u in graph:
												_lowerCAmelCase  						=			math.inf
												_lowerCAmelCase  						=			None
						_lowerCAmelCase  						=			0

						_lowerCAmelCase  						=			list(__lowerCamelCase		)
						hq.heapify(__lowerCamelCase		)

						while h:
												_lowerCAmelCase  						=			hq.heappop(__lowerCamelCase		)
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								_lowerCAmelCase  						=			u
																								_lowerCAmelCase  						=			u.edges[v.id]
																								hq.heapify(__lowerCamelCase		)

						for i in range(1			,     len(__lowerCamelCase		)		):
												yield (int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)


def A				():
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()
