


'''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'''






import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


a_   :      Tuple				    =      get_tests_dir("fixtures")




class 	a       (       unittest.TestCase    ):
 def 							__UpperCAmelCase						(	self  )   ->							str:
      # A mock response for an HTTP head request to emulate server down
      _a      =							mock.Mock()
      _a      =							5_00
      _a      =							{}
      _a      =							HTTPError
      _a      =							{}

      # Download this model to make sure it's in the cache.
      _a      =							ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch('requests.Session.request' ,			return_value=__magic_name__  ) as mock_head:
           _a      =							ViTImageProcessor.from_pretrained('hf-internal-testing/tiny-random-vit'  )
           # This check we did call the fake head request
           mock_head.assert_called()
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      # This test is for deprecated behavior and can be removed in v5
      _a      =							ViTImageProcessor.from_pretrained(
          'https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json'  )




 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      with self.assertRaises(__magic_name__  ):
           # config is in subfolder, the following should not work without specifying the subfolder
           _a      =							AutoImageProcessor.from_pretrained('hf-internal-testing/stable-diffusion-all-variants'  )

      _a      =							AutoImageProcessor.from_pretrained(
          'hf-internal-testing/stable-diffusion-all-variants' ,			subfolder='feature_extractor'  )

      self.assertIsNotNone(__magic_name__  )





@is_staging_test
class 	a       (       unittest.TestCase    ):
 @classmethod
 def 							__UpperCAmelCase						(	cls  )   ->							Dict:
      _a      =							TOKEN
      HfFolder.save_token(__magic_name__  )
 @classmethod
 def 							__UpperCAmelCase						(	cls  )   ->							List[Any]:
      try:
           delete_repo(token=cls._token ,			repo_id='test-image-processor'  )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token ,			repo_id='valid_org/test-image-processor-org'  )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token ,			repo_id='test-dynamic-image-processor'  )
      except HTTPError:
           pass
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							ViTImageProcessor.from_pretrained(__magic_name__  )
      image_processor.push_to_hub('test-image-processor' ,			use_auth_token=self._token  )

      _a      =							ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor'  )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(__magic_name__ ,			getattr(__magic_name__ ,			__magic_name__  )  )

      # Reset repo
      delete_repo(token=self._token ,			repo_id='test-image-processor'  )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               __magic_name__ ,			repo_id='test-image-processor' ,			push_to_hub=__magic_name__ ,			use_auth_token=self._token  )

      _a      =							ViTImageProcessor.from_pretrained(f'{USER}/test-image-processor'  )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(__magic_name__ ,			getattr(__magic_name__ ,			__magic_name__  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							ViTImageProcessor.from_pretrained(__magic_name__  )
      image_processor.push_to_hub('valid_org/test-image-processor' ,			use_auth_token=self._token  )

      _a      =							ViTImageProcessor.from_pretrained('valid_org/test-image-processor'  )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(__magic_name__ ,			getattr(__magic_name__ ,			__magic_name__  )  )

      # Reset repo
      delete_repo(token=self._token ,			repo_id='valid_org/test-image-processor'  )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               __magic_name__ ,			repo_id='valid_org/test-image-processor-org' ,			push_to_hub=__magic_name__ ,			use_auth_token=self._token  )

      _a      =							ViTImageProcessor.from_pretrained('valid_org/test-image-processor-org'  )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(__magic_name__ ,			getattr(__magic_name__ ,			__magic_name__  )  )




 def 							__UpperCAmelCase						(	self  )   ->							Any:
      CustomImageProcessor.register_for_auto_class()
      _a      =							CustomImageProcessor.from_pretrained(__magic_name__  )

      image_processor.push_to_hub('test-dynamic-image-processor' ,			use_auth_token=self._token  )

      # This has added the proper auto_map field to the config
      self.assertDictEqual(
          image_processor.auto_map ,			{'AutoImageProcessor': 'custom_image_processing.CustomImageProcessor'} ,			)

      _a      =							AutoImageProcessor.from_pretrained(
          f'{USER}/test-dynamic-image-processor' ,			trust_remote_code=__magic_name__  )
      # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
      self.assertEqual(new_image_processor.__class__.__name__ ,			'CustomImageProcessor'  )

