


"""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)

__lowercase    =				logging.getLogger()


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







  a						:		List[Any]											=      argparse.ArgumentParser()
  parser.add_argument("-f"	)
  a						:		Dict											=      parser.parse_args()
  return args.f


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







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


def 					lowercase      (    )->				Optional[int]:
  '''simple docstring'''







  a						:		Any											=      torch.cuda.is_available() and torch_device == "cuda"
  return is_using_cuda and is_apex_available()


__lowercase    =				logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 						_A  (  _a  ):







    """simple docstring"""




    @classmethod
    def      __snake_case							( cls    :   Any):
      # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
      a						:		Tuple											=      tempfile.mkdtemp()
      a						:		int											=      os.path.join(cls.tmpdir    ,			"default_config.yml")
      write_basic_config(save_location=cls.configPath)
      a						:		List[str]											=      ["accelerate", "launch", "--config_file", cls.configPath]






    @classmethod
    def      __snake_case							( cls    :   Optional[Any]):
      shutil.rmtree(cls.tmpdir)






    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   Dict):
      a						:		str											=      self.get_auto_remove_tmp_dir()
      a						:		str											=      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)
      a						:		Optional[Any]											=      get_results(__UpperCAmelCase)
      self.assertGreaterEqual(result["eval_accuracy"]    ,			0.75)
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"glue_no_trainer")))






    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   Optional[Any]):
      a						:		Optional[Any]											=      self.get_auto_remove_tmp_dir()
      a						:		Tuple											=      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)
      a						:		Any											=      get_results(__UpperCAmelCase)
      self.assertLess(result["perplexity"]    ,			100)
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"clm_no_trainer")))






    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   List[Any]):
      a						:		List[Any]											=      self.get_auto_remove_tmp_dir()
      a						:		Dict											=      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)
      a						:		str											=      get_results(__UpperCAmelCase)
      self.assertLess(result["perplexity"]    ,			42)
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"mlm_no_trainer")))






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

      a						:		Dict											=      self.get_auto_remove_tmp_dir()
      a						:		List[str]											=      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)
      a						:		Any											=      get_results(__UpperCAmelCase)
      self.assertGreaterEqual(result["eval_accuracy"]    ,			0.75)
      self.assertLess(result["train_loss"]    ,			0.5)
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"ner_no_trainer")))






    @unittest.skip(reason="Fix me @muellerzr")
    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   Optional[Any]):
      a						:		Union[str, Any]											=      self.get_auto_remove_tmp_dir()
      a						:		List[str]											=      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)
      a						:		List[str]											=      get_results(__UpperCAmelCase)
      # 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(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"qa_no_trainer")))






    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[str]											=      self.get_auto_remove_tmp_dir()
      a						:		int											=      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)
      a						:		Any											=      get_results(__UpperCAmelCase)
      self.assertGreaterEqual(result["eval_accuracy"]    ,			0.8)
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"swag_no_trainer")))






    @slow
    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   Optional[int]):
      a						:		Dict											=      self.get_auto_remove_tmp_dir()
      a						:		List[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)
      a						:		List[str]											=      get_results(__UpperCAmelCase)
      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(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"summarization_no_trainer")))






    @slow
    @mock.patch.dict(os.environ    ,			{"WANDB_MODE": "offline"})
    def      __snake_case							( self    :   Optional[Any]):
      a						:		List[Any]											=      self.get_auto_remove_tmp_dir()
      a						:		Dict											=      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)
      a						:		Union[str, Any]											=      get_results(__UpperCAmelCase)
      self.assertGreaterEqual(result["eval_bleu"]    ,			30)
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"epoch_0")))
      self.assertTrue(os.path.exists(os.path.join(__UpperCAmelCase    ,			"translation_no_trainer")))






    @slow
    def      __snake_case							( self    :   Dict):
      a						:		List[Any]											=      logging.StreamHandler(sys.stdout)
      logger.addHandler(__UpperCAmelCase)

      a						:		Dict											=      self.get_auto_remove_tmp_dir()
      a						:		List[str]											=      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)
      a						:		List[str]											=      get_results(__UpperCAmelCase)
      self.assertGreaterEqual(result["eval_overall_accuracy"]    ,			0.10)






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










"""simple docstring"""







from __future__ import annotations






class 						_A  :







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   List[Any]=None):
      a						:		int											=      data
      a						:		Dict											=      None






    def __repr__( self    :   Dict):
      a						:		List[Any]											=      []
      a						:		str											=      self
      while temp:
        string_rep.append(f'''{temp.data}''')
        a						:		Tuple											=      temp.next
      return "->".join(__UpperCAmelCase)






def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  if not elements_list:
    raise Exception("The Elements List is empty"	)

  a						:		Any											=      Node(elements_list[0]	)
  for i in range(1  ,       len(A_	)	):
    a						:		int											=      Node(elements_list[i]	)
    a						:		Optional[Any]											=      current.next
  return head


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if head_node is not None and isinstance(A_  ,       A_	):
    print_reverse(head_node.next	)
    print(head_node.data	)


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







  from doctest import testmod

  testmod()

  a						:		Union[str, Any]											=      make_linked_list([14, 52, 14, 12, 43]	)
  print("Linked List:"	)
  print(A_	)
  print("Elements in Reverse:"	)
  print_reverse(A_	)


if __name__ == "__main__":
 main()






