






"""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)

A__	:    Any			 = logging.getLogger()

def 			_snake_case    (					)    ->     Tuple:
							lowerCamelCase_	:							List[Any]									=argparse.ArgumentParser()
							parser.add_argument("-f"  )
							lowerCamelCase_	:							Optional[int]									=parser.parse_args()
							return args.f

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]  )    ->     Union[str, Any]:
							lowerCamelCase_	:							List[Any]									={}
							lowerCamelCase_	:							Dict									=os.path.join(lowerCamelCase__    ,						"all_results.json"  )
							if os.path.exists(lowerCamelCase__  ):
														with open(lowerCamelCase__    ,						"r"  ) as f:
																					lowerCamelCase_	:							Optional[int]									=json.load(lowerCamelCase__  )
							else:
														raise ValueError(F"""can't find {path}"""  )
							return results

def 			_snake_case    (					)    ->     Any:
							lowerCamelCase_	:							Tuple									=torch.cuda.is_available() and torch_device == "cuda"
							return is_using_cuda and is_apex_available()


A__	:    Dict			 = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        lowercase__      (							snake_case__       ):


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


			@classmethod
			def      UpperCAmelCase__						(				cls					:  List[str] ):
										shutil.rmtree(cls.tmpdir )


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

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

										lowerCamelCase_	:							Optional[Any]									=self.get_auto_remove_tmp_dir()
										lowerCamelCase_	:							str									=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 )
										lowerCamelCase_	:							Optional[int]									=get_results(snake_case__ )
										self.assertGreaterEqual(result["eval_overall_accuracy"]       ,	0.10 )


			@mock.patch.dict(os.environ       ,	{"WANDB_MODE": "offline"} )
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Tuple									=self.get_auto_remove_tmp_dir()
										lowerCamelCase_	:							List[Any]									=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 )
										lowerCamelCase_	:							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    (					lowerCamelCase__     :   int  )    ->     int:

							if not isinstance(lowerCamelCase__    ,						lowerCamelCase__  ):
														raise TypeError("only integers accepted as input"  )
							else:
														lowerCamelCase_	:							str									=str(abs(lowerCamelCase__  )  )
														lowerCamelCase_	:							Tuple									=[list(lowerCamelCase__  ) for char in range(len(lowerCamelCase__  )  )]
														for index in range(len(lowerCamelCase__  )  ):
																					num_transpositions[index].pop(lowerCamelCase__  )
														return max(
														    int("".join(list(lowerCamelCase__  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
						__import__('doctest').testmod()



