
"""simple docstring"""


from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__A 		= {
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				list[str]:
 _lowerCAmelCase			={start}
 _lowerCAmelCase			=[start]
 _lowerCAmelCase			=Queue()
 queue.put(__UpperCamelCase )
 while not queue.empty():
  _lowerCAmelCase			=queue.get()
  for w in graph[v]:
   if w not in explored:
    explored.add(__UpperCamelCase )
    result.append(__UpperCamelCase )
    queue.put(__UpperCamelCase )
 return result



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				list[str]:
 _lowerCAmelCase			={start}
 _lowerCAmelCase			=[start]
 _lowerCAmelCase			=deque([start] )
 while queue:
  _lowerCAmelCase			=queue.popleft()
  for child in graph[v]:
   if child not in visited:
    visited.add(__UpperCamelCase )
    result.append(__UpperCamelCase )
    queue.append(__UpperCamelCase )
 return result



def 					_lowerCamelCase(__UpperCamelCase )       ->				None:
 _lowerCAmelCase			=F'''from __main__ import G, {name}'''
 _lowerCAmelCase			=10000
 _lowerCAmelCase			=timeit(F'''{name}(G, \'A\')'''					,				setup=__UpperCamelCase					,				number=__UpperCamelCase )
 print(F'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   benchmark_function('breadth_first_search')
   benchmark_function('breadth_first_search_with_deque')
   # breadth_first_search                finished 10000 runs in 0.20999 seconds
   # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





"""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


__A 		= [
    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)

__A 		= logging.getLogger()



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



def 					_lowerCamelCase(__UpperCamelCase )       ->				List[str]:
	_lowerCAmelCase			={}
	_lowerCAmelCase			=os.path.join(__UpperCamelCase					,				"""all_results.json""" )
	if os.path.exists(__UpperCamelCase ):
		with open(__UpperCamelCase					,				"""r""" ) as f:
			_lowerCAmelCase			=json.load(__UpperCamelCase )
	else:
		raise ValueError(F'''can\'t find {path}''' )
	return results



def 					_lowerCamelCase()       ->				List[Any]:
	return bool(tf.config.list_physical_devices("""GPU""" ) )


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







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@skip("""Skipping until shape inference for to_tf_dataset PR is merged."""						)
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					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(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""eval_accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_clm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  1_00						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_mlm.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertLess(result["""eval_perplexity"""]  ,  42						)



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				# 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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_ner.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7_5						)



			def       _lowerCAmelCase	(							self						)				->  str:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_squad.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""f1"""]  ,  30						)
					self.assertGreaterEqual(result["""exact"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_swag.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""val_accuracy"""]  ,  0.8						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_summarization.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""rouge1"""]  ,  10						)
					self.assertGreaterEqual(result["""rouge2"""]  ,  2						)
					self.assertGreaterEqual(result["""rougeL"""]  ,  7						)
					self.assertGreaterEqual(result["""rougeLsum"""]  ,  7						)



			@slow
			def       _lowerCAmelCase	(							self						)				->  Any:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_translation.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""bleu"""]  ,  30						)



			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				_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(__UpperCAmelCase  ,  """argv"""  ,  __UpperCAmelCase						):
					run_image_classification.main()
					_lowerCAmelCase			=get_results(__UpperCAmelCase						)
					self.assertGreaterEqual(result["""accuracy"""]  ,  0.7						)



