



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__ =						logging.getLogger()






def  UpperCAmelCase__    (    ):
						__lowercase					=				argparse.ArgumentParser()
						parser.add_argument("""-f"""   )
						__lowercase					=				parser.parse_args()
						return args.f






def  UpperCAmelCase__    (    lowercase__   ):
						__lowercase					=				{}
						__lowercase					=				os.path.join(lowercase__						,       """all_results.json"""   )
						if os.path.exists(lowercase__   ):
												with open(lowercase__						,       """r"""   ) as f:
																		__lowercase					=				json.load(lowercase__   )
						else:
												raise ValueError(F"can't find {path}"   )
						return results






def  UpperCAmelCase__    (    ):
						__lowercase					=				torch.cuda.is_available() and torch_device == """cuda"""
						return is_using_cuda and is_apex_available()


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







class 			_lowerCAmelCase   ( __snake_case			):






			"""simple docstring"""





			@classmethod
			def 		snake_case__			(				cls	:       List[Any]   )						->					Union[str, Any]:
									"""simple docstring"""







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





			@classmethod
			def 		snake_case__			(				cls	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







									shutil.rmtree(cls.tmpdir   )





			@mock.patch.dict(os.environ			,     {"""WANDB_MODE""": """offline"""}   )
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       Optional[Any]   )						->					List[str]:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       str   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       Tuple   )						->					Optional[Any]:
									"""simple docstring"""







									__lowercase					=				7 if get_gpu_count() > 1 else 2

									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       Dict   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       Optional[int]   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       int   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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 		snake_case__			(				self	:       str   )						->					Any:
									"""simple docstring"""







									__lowercase					=				logging.StreamHandler(sys.stdout   )
									logger.addHandler(__UpperCamelCase   )

									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				get_results(__UpperCamelCase   )
									self.assertGreaterEqual(result["""eval_overall_accuracy"""]			,     0.10   )







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







									__lowercase					=				self.get_auto_remove_tmp_dir()
									__lowercase					=				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   )
									__lowercase					=				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"""   )   )   )










def  UpperCAmelCase__    (    lowercase__						,       lowercase__   ) ->      bool:
						__lowercase					=				len(lowercase__   )
						__lowercase					=				[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

						# for each arr value, a sum of zero(0) can be formed by not taking any element
						# hence True/1
						for i in range(arr_len + 1   ):
												__lowercase					=				True

						# sum is not zero and set is empty then false
						for i in range(1						,       required_sum + 1   ):
												__lowercase					=				False

						for i in range(1						,       arr_len + 1   ):
												for j in range(1						,       required_sum + 1   ):
																		if arr[i - 1] > j:
																								__lowercase					=				subset[i - 1][j]
																		if arr[i - 1] <= j:
																								__lowercase					=				subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

						return subset[arr_len][required_sum]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





