


"""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)

_lowerCAmelCase							:    Tuple					   =	logging.getLogger()







def        lowerCamelCase_(		)    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     argparse.ArgumentParser()
   parser.add_argument("-f"       )
   _lowerCamelCase   :				Tuple							=     parser.parse_args()
   return args.f







def        lowerCamelCase_(		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





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







def        lowerCamelCase_(		)    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[int]							=     torch.cuda.is_available() and torch_device == "cuda"
   return is_using_cuda and is_apex_available()


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




class 							A_      (       _a ):


  @classmethod
  def 							_lowercase					(			cls:						List[str]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     tempfile.mkdtemp()
     _lowerCamelCase   :				List[Any]							=     os.path.join(cls.tmpdir							,"default_config.yml"    )
     write_basic_config(save_location=cls.configPath    )
     _lowerCamelCase   :				Optional[Any]							=     ["accelerate", "launch", "--config_file", cls.configPath]


  @classmethod
  def 							_lowercase					(			cls:						Union[str, Any]    ):







     '''simple docstring'''







     shutil.rmtree(cls.tmpdir    )


  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						List[str]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Union[str, 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   :				Tuple							=     get_results(__lowerCAmelCase    )
     self.assertGreaterEqual(result["eval_accuracy"]							,0.75    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"glue_no_trainer"    )    )    )


  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Optional[Any]							=     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(__lowerCAmelCase    )
     self.assertLess(result["perplexity"]							,100    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"clm_no_trainer"    )    )    )


  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     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   :				Optional[int]							=     get_results(__lowerCAmelCase    )
     self.assertLess(result["perplexity"]							,42    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"mlm_no_trainer"    )    )    )


  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[Any]							=     7 if get_gpu_count() > 1 else 2

     _lowerCamelCase   :				Any							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Union[str, Any]							=     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   :				Optional[int]							=     get_results(__lowerCAmelCase    )
     self.assertGreaterEqual(result["eval_accuracy"]							,0.75    )
     self.assertLess(result["train_loss"]							,0.5    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"ner_no_trainer"    )    )    )


  @unittest.skip(reason="Fix me @muellerzr"    )
  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Optional[int]							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Any							=     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   :				int							=     get_results(__lowerCAmelCase    )
     # 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(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"qa_no_trainer"    )    )    )


  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Any							=     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   :				str							=     get_results(__lowerCAmelCase    )
     self.assertGreaterEqual(result["eval_accuracy"]							,0.8    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"swag_no_trainer"    )    )    )


  @slow
  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						Union[str, Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Optional[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(__lowerCAmelCase    )
     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(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"summarization_no_trainer"    )    )    )


  @slow
  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				Dict							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Union[str, Any]							=     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   :				Dict							=     get_results(__lowerCAmelCase    )
     self.assertGreaterEqual(result["eval_bleu"]							,30    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"epoch_0"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"translation_no_trainer"    )    )    )


  @slow
  def 							_lowercase					(			self:						Optional[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     logging.StreamHandler(sys.stdout    )
     logger.addHandler(__lowerCAmelCase    )

     _lowerCamelCase   :				List[Any]							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Optional[int]							=     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   :				Any							=     get_results(__lowerCAmelCase    )
     self.assertGreaterEqual(result["eval_overall_accuracy"]							,0.10    )






  @mock.patch.dict(os.environ							,{"WANDB_MODE": "offline"}    )
  def 							_lowercase					(			self:						Dict    ):







     '''simple docstring'''







     _lowerCamelCase   :				Union[str, Any]							=     self.get_auto_remove_tmp_dir()
     _lowerCamelCase   :				Union[str, 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   :				Union[str, Any]							=     get_results(__lowerCAmelCase    )
     # The base model scores a 25%
     self.assertGreaterEqual(result["eval_accuracy"]							,0.6    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"step_1"    )    )    )
     self.assertTrue(os.path.exists(os.path.join(__lowerCAmelCase							,"image_classification_no_trainer"    )    )    )



'''simple docstring'''






import warnings
from functools import wraps
from typing import Callable





def lowerCamelCase__     (		__lowerCamelCase			:				Callable				):







   '''simple docstring'''




   @wraps(__lowerCamelCase				)
   def _inner_fn(*__lowerCamelCase			:				int			,       **__lowerCamelCase			:				Optional[Any]				):
      warnings.warn(
          (f"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")			,       __lowerCamelCase			,       )
      return fn(*__lowerCamelCase			,       **__lowerCamelCase				)

   return _inner_fn

