





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







def _lowerCAmelCase     (      )->     Tuple:
 __UpperCAmelCase					=   argparse.ArgumentParser()
 parser.add_argument('-f'    )
 __UpperCAmelCase					=   parser.parse_args()
 return args.f







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[Any]:
 __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     (      )->     Optional[int]:
 __UpperCAmelCase					=   torch.cuda.is_available() and torch_device == 'cuda'
 return is_using_cuda and is_apex_available()


_A:					Union[str, Any]							=				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class UpperCAmelCase							(		UpperCAmelCase_							):



    @classmethod
    def 					__lowerCamelCase						(  cls					):
     # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
     __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 					__lowerCamelCase						(  cls					):
     shutil.rmtree(cls.tmpdir					)



    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertGreaterEqual(result['eval_accuracy']     ,			0.7_5					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'glue_no_trainer'					)					)					)



    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertLess(result['perplexity']     ,			100					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'clm_no_trainer'					)					)					)



    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertLess(result['perplexity']     ,			42					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'mlm_no_trainer'					)					)					)



    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     __UpperCAmelCase					=   7 if get_gpu_count() > 1 else 2

     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertGreaterEqual(result['eval_accuracy']     ,			0.7_5					)
     self.assertLess(result['train_loss']     ,			0.5					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'ner_no_trainer'					)					)					)



    @unittest.skip(reason='Fix me @muellerzr'					)
    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     # 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(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'qa_no_trainer'					)					)					)



    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertGreaterEqual(result['eval_accuracy']     ,			0.8					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'swag_no_trainer'					)					)					)



    @slow
    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     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(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'summarization_no_trainer'					)					)					)



    @slow
    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertGreaterEqual(result['eval_bleu']     ,			30					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'epoch_0'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'translation_no_trainer'					)					)					)



    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   logging.StreamHandler(sys.stdout					)
     logger.addHandler(__A					)

     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     self.assertGreaterEqual(result['eval_overall_accuracy']     ,			0.1_0					)




    @mock.patch.dict(os.environ     ,			{'WANDB_MODE': 'offline'}					)
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.get_auto_remove_tmp_dir()
     __UpperCAmelCase					=   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					)
     __UpperCAmelCase					=   get_results(__A					)
     # The base model scores a 25%
     self.assertGreaterEqual(result['eval_accuracy']     ,			0.6					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'step_1'					)					)					)
     self.assertTrue(os.path.exists(os.path.join(__A     ,			'image_classification_no_trainer'					)					)					)












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A:					Tuple							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			List[Any]						   =       """timm_backbone"""



    def __init__(  self     ,			__A=None     ,			__A=3     ,			__A=True     ,			__A=True     ,			__A=None     ,			**__A     ,			):
     super().__init__(**__A					)
     __UpperCAmelCase					=   backbone
     __UpperCAmelCase					=   num_channels
     __UpperCAmelCase					=   features_only
     __UpperCAmelCase					=   use_pretrained_backbone
     __UpperCAmelCase					=   True
     __UpperCAmelCase					=   out_indices if out_indices is not None else (-1,)





