
"""simple docstring"""





# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_a								=						Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_a								=						{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_a								=						"""zero2"""
_a								=						"""zero3"""
_a								=						[ZEROa, ZEROa]



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    parameterized.to_safe_name('''_'''.join(str(__snake_case      ) for x in param.args      )      )
					return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_a								=						list(itertools.product(stages, models.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class        _UpperCAmelCase(       lowerCamelCase      ):



		@parameterized.expand(__a							,						name_func=__a)
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							self.run_and_check(
							    stage=__a							,						model=__a							,						distributed=__a							,						fpaa=__a							,						)



		@require_torch_multi_gpu
		@parameterized.expand(__a							,						name_func=__a)
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			int:






							'''simple docstring'''
							self.run_and_check(
							    stage=__a							,						model=__a							,						distributed=__a							,						fpaa=__a							,						)



		@parameterized.expand(__a							,						name_func=__a)
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			List[str]:






							'''simple docstring'''
							self.run_and_check(
							    stage=__a							,						model=__a							,						distributed=__a							,						fpaa=__a							,						)



		@require_torch_multi_gpu
		@parameterized.expand(__a							,						name_func=__a)
		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Dict:






							'''simple docstring'''
							self.run_and_check(
							    stage=__a							,						model=__a							,						distributed=__a							,						fpaa=__a							,						)



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							# XXX: run_asr is premature and doesn't save any results
							# so all we check for now is that the process didn't fail
							pass



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = 10							,						__a = True							,						__a = True							,						__a = True							,						)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    models[model]

							_UpperCamelCase									=    self.run_trainer(
							    stage=__a							,						model_name=__a							,						eval_steps=__a							,						num_train_epochs=1							,						distributed=__a							,						fpaa=__a							,						)

							self.do_checks(__a)

							return output_dir



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = 10							,						__a = 1							,						__a = True							,						__a = True							,						)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_auto_remove_tmp_dir('''./xxx'''							,						after=__a)
							_UpperCamelCase									=    F'''
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(__a)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        '''.split()

							if fpaa:
												args.extend(['''--fp16'''])

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							_UpperCamelCase									=    F'''--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'''.split()
							_UpperCamelCase									=    [F'''{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py''']
							_UpperCamelCase									=    self.get_launcher(__a)

							_UpperCamelCase									=    launcher + script + args + ds_args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(__a							,						env=self.get_env())

							return output_dir




		def 		UpperCAmelCase				(   self							,						__a=False)			->			Tuple:






							'''simple docstring'''
							# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
							# - it won't be able to handle that
							# 2. for now testing with just 2 gpus max (since some quality tests may give different
							# results with mode gpus because we use very little data)
							_UpperCamelCase									=    min(2							,						get_gpu_count()) if distributed else 1
							return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus}'''.split()








"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# for each node and his adjacency list add them and the rank of the node to queue
					# using heapq module the queue will be filled like a Priority Queue
					# heapq works with a min priority queue, so I used -1*len(v) to build it
					for key, value in graph.items():
										# O(log(n))
										heapq.heappush(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    set()

					# while queue isn't empty and there are still edges
					#   (queue[0][0] is the rank of the node with max rank)
					while queue and queue[0][0] != 0:
										# extract vertex with max rank from queue and add it to chosen_vertices
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# Remove all arcs adjacent to argmax
										for elem in queue:
															# if v haven't adjacent node, skip
															if elem[0] == 0:
																				continue
															# if argmax is reachable from elem
															# remove argmax from elem's adjacent list and update his rank
															if argmax in elem[1][1]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
				print(F"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")






