
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)

__lowerCAmelCase					:			List[Any]			      =		logging.getLogger()


def     UpperCAmelCase_  (						) ->						Any:
    __lowercase		:			Optional[int]	    =       argparse.ArgumentParser()
    parser.add_argument('''-f'''	)
    __lowercase		:			int	    =       parser.parse_args()
    return args.f


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Optional[int]:
    __lowercase		:			str	    =       {}
    __lowercase		:			List[str]	    =       os.path.join(lowercase__	,					'''all_results.json'''	)
    if os.path.exists(lowercase__	):
        with open(lowercase__	,					'''r'''	) as f:
            __lowercase		:			int	    =       json.load(lowercase__	)
    else:
        raise ValueError(F'can\'t find {path}'	)
    return results


def     UpperCAmelCase_  (						) ->						List[Any]:
    __lowercase		:			List[Any]	    =       torch.cuda.is_available() and torch_device == '''cuda'''
    return is_using_cuda and is_apex_available()


__lowerCAmelCase					:			Optional[int]			      =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 						__lowerCAmelCase	(	__snake_case      ):



      """simple docstring"""







      @classmethod
      def 		snake_case_			(      cls		:       Dict				):
          __lowercase		:			Dict	    =       tempfile.mkdtemp()
          __lowercase		:			List[Any]	    =       os.path.join(cls.tmpdir				,       '''default_config.yml'''				)
          write_basic_config(save_location=cls.configPath				)
          __lowercase		:			Dict	    =       ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]




      @classmethod
      def 		snake_case_			(      cls		:       Any				):
          shutil.rmtree(cls.tmpdir				)




      @mock.patch.dict(os.environ				,       {'''WANDB_MODE''': '''offline'''}				)
      def 		snake_case_			(      self		:       int				):
          __lowercase		:			str	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			List[Any]	    =       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		:			List[str]	    =       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[int]				):
          __lowercase		:			Dict	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			str	    =       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		:			int	    =       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		:       List[str]				):
          __lowercase		:			Dict	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			Optional[int]	    =       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		:			Tuple	    =       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		:       int				):
          __lowercase		:			Union[str, Any]	    =       7 if get_gpu_count() > 1 else 2

          __lowercase		:			List[Any]	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			Optional[int]	    =       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		:			str	    =       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		:       Optional[int]				):
          __lowercase		:			str	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			Tuple	    =       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		:			List[str]	    =       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		:       int				):
          __lowercase		:			Any	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			Optional[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				)
          __lowercase		:			Union[str, Any]	    =       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		:       List[str]				):
          __lowercase		:			Tuple	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			Tuple	    =       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		:			Optional[int]	    =       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		:       List[str]				):
          __lowercase		:			Dict	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			List[Any]	    =       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		:			Any	    =       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		:       Optional[Any]				):
          __lowercase		:			Optional[Any]	    =       logging.StreamHandler(sys.stdout				)
          logger.addHandler(__UpperCamelCase				)

          __lowercase		:			Dict	    =       self.get_auto_remove_tmp_dir()
          __lowercase		:			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				)
          __lowercase		:			Optional[int]	    =       get_results(__UpperCamelCase				)
          self.assertGreaterEqual(result['''eval_overall_accuracy''']				,       0.10				)




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




from math import factorial

__lowerCAmelCase					:			Dict			      =		{str(d): factorial(d) for d in range(10)}


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    return sum(DIGIT_FACTORIAL[d] for d in str(__lowerCAmelCase	)	)


def     UpperCAmelCase_  (						) ->						int:
    __lowercase		:			int	    =       7 * factorial(9	) + 1
    return sum(i for i in range(3	,					__lowerCAmelCase	) if sum_of_digit_factorial(__lowerCAmelCase	) == i	)


if __name__ == "__main__":
 print(F'{solution() = }')


