


'''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      :      int            =					logging.getLogger()


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     argparse.ArgumentParser()
						parser.add_argument('''-f'''					)
						UpperCAmelCase					    =     parser.parse_args()
						return args.f


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     {}
						UpperCAmelCase					    =     os.path.join(lowerCamelCase_				,       '''all_results.json'''					)
						if os.path.exists(lowerCamelCase_					):
												with open(lowerCamelCase_				,       '''r'''					) as f:
																		UpperCAmelCase					    =     json.load(lowerCamelCase_					)
						else:
												raise ValueError(f"""can\'t find {path}"""					)
						return results


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     torch.cuda.is_available() and torch_device == """cuda"""
						return is_using_cuda and is_apex_available()


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







class 	UpperCamelCase__(       lowercase__	):




				@classmethod
				def 					a__(		cls      :			Dict				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     tempfile.mkdtemp()
										UpperCAmelCase					    =     os.path.join(cls.tmpdir				,   '''default_config.yml'''				)
										write_basic_config(save_location=cls.configPath				)
										UpperCAmelCase					    =     ["""accelerate""", """launch""", """--config_file""", cls.configPath]




				@classmethod
				def 					a__(		cls      :			int				)->					Union[str, Any]:

										"""simple docstring"""




										shutil.rmtree(cls.tmpdir				)




				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			Optional[int]				)->					int:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										self.assertGreaterEqual(result['''eval_accuracy''']				,   0.75				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''epoch_0'''				)				)				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''glue_no_trainer'''				)				)				)




				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										self.assertLess(result['''perplexity''']				,   100				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''epoch_0'''				)				)				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''clm_no_trainer'''				)				)				)




				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										self.assertLess(result['''perplexity''']				,   42				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''epoch_0'''				)				)				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''mlm_no_trainer'''				)				)				)




				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     7 if get_gpu_count() > 1 else 2

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




				@unittest.skip(reason='''Fix me @muellerzr'''				)
				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			List[str]				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										# 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(_UpperCamelCase				,   '''epoch_0'''				)				)				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''qa_no_trainer'''				)				)				)




				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			List[Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										self.assertGreaterEqual(result['''eval_accuracy''']				,   0.8				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''swag_no_trainer'''				)				)				)




				@slow
				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			int				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										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(_UpperCamelCase				,   '''epoch_0'''				)				)				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''summarization_no_trainer'''				)				)				)




				@slow
				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										self.assertGreaterEqual(result['''eval_bleu''']				,   30				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''epoch_0'''				)				)				)
										self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,   '''translation_no_trainer'''				)				)				)




				@slow
				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     logging.StreamHandler(sys.stdout				)
										logger.addHandler(_UpperCamelCase				)

										UpperCAmelCase					    =     self.get_auto_remove_tmp_dir()
										UpperCAmelCase					    =     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				)
										UpperCAmelCase					    =     get_results(_UpperCamelCase				)
										self.assertGreaterEqual(result['''eval_overall_accuracy''']				,   0.10				)





				@mock.patch.dict(os.environ				,   {'''WANDB_MODE''': '''offline'''}				)
				def 					a__(		self      :			Any				)->					Tuple:

										"""simple docstring"""




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









'''simple docstring'''



def 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						return int(input_a == input_a == 0					)


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						print('''Truth Table of NOR Gate:'''					)
						print('''|   Input 1   |  Input 2  | Output |'''					)
						print(f"""|      0      |    0      |  {nor_gate(0				,       0					)}     |"""					)
						print(f"""|      0      |    1      |  {nor_gate(0				,       1					)}     |"""					)
						print(f"""|      1      |    0      |  {nor_gate(1				,       0					)}     |"""					)
						print(f"""|      1      |    1      |  {nor_gate(1				,       1					)}     |"""					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





