





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 :    int  							= logging.getLogger()




def _a (  )	->    Optional[Any]:

       '''simple docstring'''






       SCREAMING_SNAKE_CASE__       :      List[Any]     =						argparse.ArgumentParser()
       parser.add_argument("-f"       )
       SCREAMING_SNAKE_CASE__       :      int     =						parser.parse_args()
       return args.f




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    Dict:

       '''simple docstring'''






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




def _a (  )	->    Optional[int]:

       '''simple docstring'''






       SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.cuda.is_available() and torch_device == """cuda"""
       return is_using_cuda and is_apex_available()


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







class 						lowerCamelCase       (lowercase__      ):







       """simple docstring"""




       @classmethod
       def  A_     ( cls     :					Dict						)				->			Dict:





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






       @classmethod
       def  A_     ( cls     :					int						)				->			Any:





              """simple docstring"""
              shutil.rmtree(cls.tmpdir						)






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[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(_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						)				->			Union[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      Tuple     =						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__       :      Optional[int]     =						get_results(_UpperCamelCase						)
              self.assertLess(result["perplexity"],			1_0_0						)
              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"""
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      List[str]     =						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__       :      str     =						get_results(_UpperCamelCase						)
              self.assertLess(result["perplexity"],			4_2						)
              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]						)				->			Dict:





              """simple docstring"""
              # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
              SCREAMING_SNAKE_CASE__       :      Dict     =						7 if get_gpu_count() > 1 else 2

              SCREAMING_SNAKE_CASE__       :      str     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      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[int]     =						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]						)				->			Union[str, Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      Optional[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__       :      str     =						get_results(_UpperCamelCase						)
              # 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(_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]						)				->			Any:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      str     =						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__       :      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  A_     ( self     :					int						)				->			Optional[Any]:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      List[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__       :      int     =						get_results(_UpperCamelCase						)
              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(_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						)				->			Dict:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      Optional[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__       :      Any     =						get_results(_UpperCamelCase						)
              self.assertGreaterEqual(result["eval_bleu"],			3_0						)
              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]						)				->			int:





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      List[str]     =						logging.StreamHandler(sys.stdout						)
              logger.addHandler(_UpperCamelCase						)

              SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      Tuple     =						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__       :      str     =						get_results(_UpperCamelCase						)
              self.assertGreaterEqual(result["eval_overall_accuracy"],			0.10						)






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





              """simple docstring"""
              SCREAMING_SNAKE_CASE__       :      Dict     =						self.get_auto_remove_tmp_dir()
              SCREAMING_SNAKE_CASE__       :      Tuple     =						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__       :      str     =						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 _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    str:

							'''simple docstring'''






							if not all(char in "01" for char in bin_string       ):
														raise ValueError("Non-binary value was passed to the function"       )
							if not bin_string:
														raise ValueError("Empty string was passed to the function"       )
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						""
							while len(SCREAMING_SNAKE_CASE__       ) % 3 != 0:
														SCREAMING_SNAKE_CASE__       :      str     =						"0" + bin_string
							SCREAMING_SNAKE_CASE__       :      List[Any]     =						[
							    bin_string[index : index + 3]
							    for index in range(len(SCREAMING_SNAKE_CASE__       )       )
							    if index % 3 == 0
							]
							for bin_group in bin_string_in_3_list:
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						0
														for index, val in enumerate(SCREAMING_SNAKE_CASE__       ):
																					oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE__       )       )
														oct_string += str(SCREAMING_SNAKE_CASE__       )
							return oct_string


if __name__ == "__main__":
		from doctest import testmod

		testmod()






