





'''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					:     str												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:List[str]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Tuple    			= parser.parse_args()
   return args.f
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Optional[int] ):
   lowercase_			:Any    			= {}
   lowercase_			:int    			= os.path.join(__lowerCamelCase	,"all_results.json" )
   if os.path.exists(__lowerCamelCase ):
      with open(__lowerCamelCase	,"r" ) as f:
         lowercase_			:Optional[int]    			= json.load(__lowerCamelCase )
   else:
      raise ValueError(F'can\'t find {path}' )
   return results
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= torch.cuda.is_available() and torch_device == "cuda"
   return is_using_cuda and is_apex_available()


lowerCAmelCase					:     Dict												=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class       a_   (				snake_case_							):





     @classmethod
     def 							lowercase__      ( cls		:					int						):





        """simple docstring"""




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





     @classmethod
     def 							lowercase__      ( cls		:					Tuple						):





        """simple docstring"""




        shutil.rmtree(cls.tmpdir						)





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





        """simple docstring"""




        lowercase_			:str    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					int						):





        """simple docstring"""




        lowercase_			:Dict    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




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

        lowercase_			:Optional[int]    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:Tuple    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					str						):





        """simple docstring"""




        lowercase_			:Tuple    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




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

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







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





        """simple docstring"""




        lowercase_			:Dict    			= self.get_auto_remove_tmp_dir()
        lowercase_			: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						)
        lowercase_			: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()





