

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)

a		=logging.getLogger()






def        SCREAMING_SNAKE_CASE__	(  )  ->					Optional[int]:
	__lowerCamelCase    :    str 	=			argparse.ArgumentParser()
	parser.add_argument('-f'      )
	__lowerCamelCase    :    Dict 	=			parser.parse_args()
	return args.f






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    int 	=			{}
	__lowerCamelCase    :    List[Any] 	=			os.path.join(UpperCAmelCase_     ,			'all_results.json'      )
	if os.path.exists(UpperCAmelCase_      ):
		with open(UpperCAmelCase_     ,			'r'      ) as f:
			__lowerCamelCase    :    int 	=			json.load(UpperCAmelCase_      )
	else:
		raise ValueError(F"can't find {path}"      )
	return results






def        SCREAMING_SNAKE_CASE__	(  )  ->					Optional[Any]:
	__lowerCamelCase    :    Dict 	=			torch.cuda.is_available() and torch_device == 'cuda'
	return is_using_cuda and is_apex_available()


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






class 	A_		(	_lowerCamelCase    ):
							@classmethod
							def    lowerCAmelCase					(		cls		:      Dict):
								__lowerCamelCase    :    Optional[int] 	=			tempfile.mkdtemp()
								__lowerCamelCase    :    Any 	=			os.path.join(cls.tmpdir       ,'default_config.yml')
								write_basic_config(save_location=cls.configPath)
								__lowerCamelCase    :    Optional[Any] 	=			['accelerate', 'launch', '--config_file', cls.configPath]
							@classmethod
							def    lowerCAmelCase					(		cls		:      Optional[Any]):
								shutil.rmtree(cls.tmpdir)
							@mock.patch.dict(os.environ       ,{'WANDB_MODE': 'offline'})
							def    lowerCAmelCase					(		self		:      List[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    Tuple 	=			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    :    List[str] 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertGreaterEqual(result['eval_accuracy']       ,0.75)
								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    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    int 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    Optional[int] 	=			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    :    Tuple 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertLess(result['perplexity']       ,1_0_0)
								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    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    Optional[Any] 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    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)
								__lowerCamelCase    :    int 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertLess(result['perplexity']       ,4_2)
								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    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    Any 	=			7 if get_gpu_count() > 1 else 2

								__lowerCamelCase    :    List[str] 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    Union[str, 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)
								__lowerCamelCase    :    List[str] 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertGreaterEqual(result['eval_accuracy']       ,0.75)
								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    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    int 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    Optional[int] 	=			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    :    str 	=			get_results(SCREAMING_SNAKE_CASE__)
								# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
								self.assertGreaterEqual(result['eval_f1']       ,2_8)
								self.assertGreaterEqual(result['eval_exact']       ,2_8)
								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    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[Any] 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    int 	=			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    :    Optional[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    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Any 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    int 	=			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    :    Any 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertGreaterEqual(result['eval_rouge1']       ,1_0)
								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    lowerCAmelCase					(		self		:      List[str]):
								__lowerCamelCase    :    Tuple 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    Optional[int] 	=			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    :    Tuple 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertGreaterEqual(result['eval_bleu']       ,3_0)
								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    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Optional[int] 	=			logging.StreamHandler(sys.stdout)
								logger.addHandler(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[str] 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    str 	=			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    :    Any 	=			get_results(SCREAMING_SNAKE_CASE__)
								self.assertGreaterEqual(result['eval_overall_accuracy']       ,0.10)


							@mock.patch.dict(os.environ       ,{'WANDB_MODE': 'offline'})
							def    lowerCAmelCase					(		self		:      str):
								__lowerCamelCase    :    Optional[int] 	=			self.get_auto_remove_tmp_dir()
								__lowerCamelCase    :    List[str] 	=			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    :    Optional[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')))







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)  ->					None:
	__lowerCamelCase    :    int 	=			len(lowerCamelCase__      )

	# If row is equal to the size of the board it means there are a queen in each row in
	# the current board (possible_board)
	if row == n:
		# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
		# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
		boards.append(['. ' * i + 'Q ' + '. ' * (n - 1 - i) for i in possible_board]      )
		return

	# We iterate each column in the row to find all possible results in each row
	for col in range(lowerCamelCase__      ):
		# We apply that we learned previously. First we check that in the current board
		# (possible_board) there are not other same value because if there is it means
		# that there are a collision in vertical. Then we apply the two formulas we
		# learned before:
		#
		# 45º: y - x = b or 45: row - col = b
		# 135º: y + x = b or row + col = b.
		#
		# And we verify if the results of this two formulas not exist in their variables
		# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
		#
		# If any or these are True it means there is a collision so we continue to the
		# next value in the for loop.
		if (
		    col in possible_board
		    or row - col in diagonal_right_collisions
		    or row + col in diagonal_left_collisions
		):
			continue

		# If it is False we call dfs function again and we update the inputs
		depth_first_search(
		    [*possible_board, col]     ,			[*diagonal_right_collisions, row - col]     ,			[*diagonal_left_collisions, row + col]     ,			lowerCamelCase__     ,			lowerCamelCase__     ,			)






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					None:
	__lowerCamelCase    :    list[list[str]] 	=			[]
	depth_first_search([]     ,			[]     ,			[]     ,			lowerCamelCase__     ,			lowerCamelCase__      )

	# Print all the boards
	for board in boards:
		for column in board:
			print(lowerCamelCase__      )
		print(''      )

	print(len(lowerCamelCase__      )     ,			'solutions were found.'      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					n_queens_solution(4)




