


'''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 		_A			()				->					Dict:




     '''simple docstring'''



     _a      =							argparse.ArgumentParser()
     parser.add_argument('-f'						)
     _a      =							parser.parse_args()
     return args.f






def 		_A			(lowerCAmelCase__					:str						)				->					Optional[Any]:




     '''simple docstring'''



     _a      =							{}
     _a      =							os.path.join(lowerCAmelCase__			,  'all_results.json'						)
     if os.path.exists(lowerCAmelCase__						):
          with open(lowerCAmelCase__			,  'r'						) as f:
               _a      =							json.load(lowerCAmelCase__						)
     else:
          raise ValueError(f'can\'t find {path}'						)
     return results






def 		_A			()				->					int:




     '''simple docstring'''



     _a      =							torch.cuda.is_available() and torch_device == 'cuda'
     return is_using_cuda and is_apex_available()


a_   :      Tuple				    =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 	a       (       _SCREAMING_SNAKE_CASE    ):
 @classmethod
 def 							__UpperCAmelCase						(	cls  )   ->							List[Any]:
      # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
      _a      =							tempfile.mkdtemp()
      _a      =							os.path.join(cls.tmpdir ,			'default_config.yml'  )
      write_basic_config(save_location=cls.configPath  )
      _a      =							['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:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      self.assertGreaterEqual(result['eval_accuracy'] ,			0.7_5  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'glue_no_trainer'  )  )  )
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      self.assertLess(result['perplexity'] ,			1_00  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'clm_no_trainer'  )  )  )
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      self.assertLess(result['perplexity'] ,			42  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'mlm_no_trainer'  )  )  )
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      _a      =							7 if get_gpu_count() > 1 else 2

      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      self.assertGreaterEqual(result['eval_accuracy'] ,			0.7_5  )
      self.assertLess(result['train_loss'] ,			0.5  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'ner_no_trainer'  )  )  )
 @unittest.skip(reason='Fix me @muellerzr'  )
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      # 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(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'qa_no_trainer'  )  )  )
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      self.assertGreaterEqual(result['eval_accuracy'] ,			0.8  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'swag_no_trainer'  )  )  )
 @slow
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      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(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'summarization_no_trainer'  )  )  )
 @slow
 @mock.patch.dict(os.environ ,			{'WANDB_MODE': 'offline'}  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.get_auto_remove_tmp_dir()
      _a      =							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  )
      _a      =							get_results(__magic_name__  )
      self.assertGreaterEqual(result['eval_bleu'] ,			30  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'epoch_0'  )  )  )
      self.assertTrue(os.path.exists(os.path.join(__magic_name__ ,			'translation_no_trainer'  )  )  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							logging.StreamHandler(sys.stdout  )
      logger.addHandler(__magic_name__  )

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




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








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_   :      Optional[int]				    =      {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Dict				    =      ["DeiTFeatureExtractor"]
 a_   :      List[Any]				    =      ["DeiTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Tuple				    =      [
     "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "DeiTForImageClassification",
     "DeiTForImageClassificationWithTeacher",
     "DeiTForMaskedImageModeling",
     "DeiTModel",
     "DeiTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      Optional[Any]				    =      [
     "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFDeiTForImageClassification",
     "TFDeiTForImageClassificationWithTeacher",
     "TFDeiTForMaskedImageModeling",
     "TFDeiTModel",
     "TFDeiTPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_deit import DeiTFeatureExtractor
  from .image_processing_deit import DeiTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deit import (
      DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DeiTForImageClassification,
      DeiTForImageClassificationWithTeacher,
      DeiTForMaskedImageModeling,
      DeiTModel,
      DeiTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deit import (
      TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDeiTForImageClassification,
      TFDeiTForImageClassificationWithTeacher,
      TFDeiTForMaskedImageModeling,
      TFDeiTModel,
      TFDeiTPreTrainedModel,
  )


else:
 import sys

 a_   :      List[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

