


'''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)

UpperCAmelCase_		:   str							   =		logging.getLogger()







def 			snake_case_   (  ):







				"""simple docstring"""





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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  ):







				"""simple docstring"""





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


UpperCAmelCase_		:   List[str]							   =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							@classmethod
							def UpperCAmelCase_	(    cls  ):
											# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
											_SCREAMING_SNAKE_CASE :       int     		=      tempfile.mkdtemp()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      os.path.join(cls.tmpdir    ,					"""default_config.yml"""  )
											write_basic_config(save_location=cls.configPath  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]






							@classmethod
							def UpperCAmelCase_	(    cls  ):
											shutil.rmtree(cls.tmpdir  )






							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      f"""
            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --seed=42
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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

											run_command(self._launch_args + testargs  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      get_results(__snake_case  )
											self.assertGreaterEqual(result["""eval_accuracy"""]    ,					0.75  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""epoch_0"""  )  )  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""glue_no_trainer"""  )  )  )






							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      f"""
            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --block_size 128
            --per_device_train_batch_size 5
            --per_device_eval_batch_size 5
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --checkpointing_steps epoch
            --with_tracking
        """.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  )
											_SCREAMING_SNAKE_CASE :       int     		=      get_results(__snake_case  )
											self.assertLess(result["""perplexity"""]    ,					100  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""epoch_0"""  )  )  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""clm_no_trainer"""  )  )  )






							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Any     		=      f"""
            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --num_train_epochs=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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






							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
											_SCREAMING_SNAKE_CASE :       List[str]     		=      7 if get_gpu_count() > 1 else 2

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       List[str]     		=      f"""
            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.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}
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
            --checkpointing_steps epoch
            --with_tracking
        """.split()

											run_command(self._launch_args + testargs  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      get_results(__snake_case  )
											self.assertGreaterEqual(result["""eval_accuracy"""]    ,					0.75  )
											self.assertLess(result["""train_loss"""]    ,					0.5  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""epoch_0"""  )  )  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""ner_no_trainer"""  )  )  )






							@unittest.skip(reason="""Fix me @muellerzr"""  )
							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       int     		=      f"""
            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.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}
            --seed=42
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

											run_command(self._launch_args + testargs  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      get_results(__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(__snake_case    ,					"""epoch_0"""  )  )  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""qa_no_trainer"""  )  )  )






							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      f"""
            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.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}
            --max_train_steps=20
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --with_tracking
        """.split()

											run_command(self._launch_args + testargs  )
											_SCREAMING_SNAKE_CASE :       int     		=      get_results(__snake_case  )
											self.assertGreaterEqual(result["""eval_accuracy"""]    ,					0.8  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""swag_no_trainer"""  )  )  )






							@slow
							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Any     		=      f"""
            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.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}
            --max_train_steps=50
            --num_warmup_steps=8
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
            --with_tracking
        """.split()

											run_command(self._launch_args + testargs  )
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      get_results(__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(__snake_case    ,					"""epoch_0"""  )  )  )
											self.assertTrue(os.path.exists(os.path.join(__snake_case    ,					"""summarization_no_trainer"""  )  )  )






							@slow
							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      f"""
            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py
            --model_name_or_path sshleifer/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}
            --max_train_steps=50
            --num_warmup_steps=8
            --num_beams=6
            --learning_rate=3e-3
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
            --checkpointing_steps epoch
            --with_tracking
        """.split()

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






							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      logging.StreamHandler(sys.stdout  )
											logger.addHandler(__snake_case  )

											_SCREAMING_SNAKE_CASE :       Any     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      f"""
            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py
            --dataset_name huggingface/semantic-segmentation-test-sample
            --output_dir {tmp_dir}
            --max_train_steps=10
            --num_warmup_steps=2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --checkpointing_steps epoch
        """.split()

											run_command(self._launch_args + testargs  )
											_SCREAMING_SNAKE_CASE :       int     		=      get_results(__snake_case  )
											self.assertGreaterEqual(result["""eval_overall_accuracy"""]    ,					0.10  )






							@mock.patch.dict(os.environ    ,					{"""WANDB_MODE""": """offline"""}  )
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.get_auto_remove_tmp_dir()
											_SCREAMING_SNAKE_CASE :       int     		=      f"""
            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py
            --model_name_or_path google/vit-base-patch16-224-in21k
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --max_train_steps 2
            --train_val_split 0.1
            --seed 42
            --output_dir {tmp_dir}
            --with_tracking
            --checkpointing_steps 1
        """.split()

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

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








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				return int((input_a, input_a).count(0     ) != 0     )







def 			snake_case_   (  ):







				"""simple docstring"""





				assert nand_gate(0							,      0     ) == 1
				assert nand_gate(0							,      1     ) == 1
				assert nand_gate(1							,      0     ) == 1
				assert nand_gate(1							,      1     ) == 0


if __name__ == "__main__":
				print(nand_gate(0, 0))
				print(nand_gate(0, 1))
				print(nand_gate(1, 0))
				print(nand_gate(1, 1))




