


"""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)

__snake_case   			=    logging.getLogger()







def   __lowerCAmelCase						(						)	->						str:


    """simple docstring"""





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







def   __lowerCAmelCase						(						lowercase  :  Optional[int] )	->						Dict:


    """simple docstring"""





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







def   __lowerCAmelCase						(						)	->						Tuple:


    """simple docstring"""





    snake_case    :      Union[str, Any]      					=    torch.cuda.is_available() and torch_device == "cuda"
    return is_using_cuda and is_apex_available()


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







class _lowerCAmelCase				(      snake_case_	):







   @classmethod
   def  lowerCamelCase		(	cls						)	->		Optional[Any]:

       '''simple docstring'''




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







   @classmethod
   def  lowerCamelCase		(	cls						)	->		Optional[Any]:

       '''simple docstring'''




       shutil.rmtree(cls.tmpdir						)







   @mock.patch.dict(os.environ						,	{"WANDB_MODE": "offline"}						)
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      str      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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    :      Union[str, 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  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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    :      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  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      str      					=    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    :      Dict      					=    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  lowerCamelCase		(	self						)	->		Optional[Any]:

       '''simple docstring'''




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

       snake_case    :      Optional[int]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      List[str]      					=    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    :      str      					=    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  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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    :      Dict      					=    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  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      List[str]      					=    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    :      Optional[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  lowerCamelCase		(	self						)	->		str:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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(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  lowerCamelCase		(	self						)	->		List[Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      int      					=    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    :      Optional[int]      					=    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  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      str      					=    logging.StreamHandler(sys.stdout						)
       logger.addHandler(UpperCamelCase__						)

       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      Dict      					=    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    :      Optional[Any]      					=    get_results(UpperCamelCase__						)
       self.assertGreaterEqual(result["eval_overall_accuracy"]						,	0.10						)







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

       '''simple docstring'''




       snake_case    :      Dict      					=    self.get_auto_remove_tmp_dir()
       snake_case    :      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    :      str      					=    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"""



def   __lowerCAmelCase						(						lowercase  :  list[int] )	->						float:


    """simple docstring"""





    if not nums:  # Makes sure that the list is not empty
        raise ValueError("List is empty" )

    snake_case    :      List[str]      					=    sum(lowercase ) / len(lowercase )  # Calculate the average
    return sum(abs(x - average ) for x in nums ) / len(lowercase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





