






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						:		Optional[int]				      = logging.getLogger()







def       A__				(   ):





 '''simple docstring'''







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







def       A__				(   _a	:       Any						):





 '''simple docstring'''







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







def       A__				(   ):





 '''simple docstring'''







 snake_case__	:     Tuple											=torch.cuda.is_available() and torch_device == """cuda"""
 return is_using_cuda and is_apex_available()


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







class 			_lowercase ( __lowercase    ):
      @classmethod
      def     lowercase__       (  cls ):
       snake_case__	:     int											=tempfile.mkdtemp()
       snake_case__	:     int											=os.path.join(cls.tmpdir					,      """default_config.yml""" )
       write_basic_config(save_location=cls.configPath )
       snake_case__	:     Any											=["""accelerate""", """launch""", """--config_file""", cls.configPath]
      @classmethod
      def     lowercase__       (  cls ):
       shutil.rmtree(cls.tmpdir )
      @mock.patch.dict(os.environ					,      {"""WANDB_MODE""": """offline"""} )
      def     lowercase__       (  self ):
       snake_case__	:     Optional[Any]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     Union[str, Any]											=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 )
       snake_case__	:     List[Any]											=get_results(a )
       self.assertGreaterEqual(result["""eval_accuracy"""]					,      0.75 )
       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     lowercase__       (  self ):
       snake_case__	:     List[str]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     List[str]											=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 )
       snake_case__	:     int											=get_results(a )
       self.assertLess(result["""perplexity"""]					,      1_0_0 )
       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     lowercase__       (  self ):
       snake_case__	:     Optional[int]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     int											=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 )
       snake_case__	:     List[Any]											=get_results(a )
       self.assertLess(result["""perplexity"""]					,      4_2 )
       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     lowercase__       (  self ):
       snake_case__	:     Dict											=7 if get_gpu_count() > 1 else 2

       snake_case__	:     Dict											=self.get_auto_remove_tmp_dir()
       snake_case__	:     Optional[Any]											=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 )
       snake_case__	:     int											=get_results(a )
       self.assertGreaterEqual(result["""eval_accuracy"""]					,      0.75 )
       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     lowercase__       (  self ):
       snake_case__	:     Tuple											=self.get_auto_remove_tmp_dir()
       snake_case__	:     Any											=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 )
       snake_case__	:     Optional[int]											=get_results(a )
       # 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(a					,      """epoch_0""" ) ) )
       self.assertTrue(os.path.exists(os.path.join(a					,      """qa_no_trainer""" ) ) )
      @mock.patch.dict(os.environ					,      {"""WANDB_MODE""": """offline"""} )
      def     lowercase__       (  self ):
       snake_case__	:     Union[str, Any]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     Union[str, Any]											=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 )
       snake_case__	:     Any											=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     lowercase__       (  self ):
       snake_case__	:     Optional[int]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     Any											=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 )
       snake_case__	:     Any											=get_results(a )
       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(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     lowercase__       (  self ):
       snake_case__	:     List[Any]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     Union[str, Any]											=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 )
       snake_case__	:     str											=get_results(a )
       self.assertGreaterEqual(result["""eval_bleu"""]					,      3_0 )
       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     lowercase__       (  self ):
       snake_case__	:     Tuple											=logging.StreamHandler(sys.stdout )
       logger.addHandler(a )

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



      @mock.patch.dict(os.environ					,      {"""WANDB_MODE""": """offline"""} )
      def     lowercase__       (  self ):
       snake_case__	:     Optional[int]											=self.get_auto_remove_tmp_dir()
       snake_case__	:     List[str]											=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 )
       snake_case__	:     Optional[int]											=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""" ) ) )







from abc import ABC, abstractmethod
from argparse import ArgumentParser







class 							lowerCAmelCase__   ( __lowercase						):

						@staticmethod
						@abstractmethod
						def 			A_					(      a					)       ->   Union[str, Any]:


								'''simple docstring'''

								raise NotImplementedError()







						@abstractmethod
						def 			A_					(      self					)       ->   List[Any]:


								'''simple docstring'''

								raise NotImplementedError()



