

"""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)

UpperCAmelCase													=logging.getLogger()



def   _A					(	):



      """simple docstring"""






      A				  =		argparse.ArgumentParser()
      parser.add_argument("""-f"""				)
      A				  =		parser.parse_args()
      return args.f



def   _A					(	_a	:						List[Any]				):



      """simple docstring"""






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



def   _A					(	):



      """simple docstring"""






      A				  =		torch.cuda.is_available() and torch_device == """cuda"""
      return is_using_cuda and is_apex_available()


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





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




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




    @classmethod
    def UpperCamelCase__		( cls    )    ->      Dict:
          shutil.rmtree(cls.tmpdir    )




    @mock.patch.dict(os.environ ,{"""WANDB_MODE""": """offline"""}    )
    def UpperCamelCase__		( self    )    ->      Dict:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		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 UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		get_results(lowerCamelCase_    )
          self.assertLess(result["""perplexity"""] ,1_0_0    )
          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 UpperCamelCase__		( self    )    ->      List[str]:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		get_results(lowerCamelCase_    )
          self.assertLess(result["""perplexity"""] ,4_2    )
          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 UpperCamelCase__		( self    )    ->      Optional[int]:
          # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
          A				  =		7 if get_gpu_count() > 1 else 2

          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		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 UpperCamelCase__		( self    )    ->      int:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		get_results(lowerCamelCase_    )
          # 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(lowerCamelCase_ ,"""epoch_0"""    )    )    )
          self.assertTrue(os.path.exists(os.path.join(lowerCamelCase_ ,"""qa_no_trainer"""    )    )    )




    @mock.patch.dict(os.environ ,{"""WANDB_MODE""": """offline"""}    )
    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		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 UpperCamelCase__		( self    )    ->      Union[str, Any]:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		get_results(lowerCamelCase_    )
          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(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 UpperCamelCase__		( self    )    ->      List[str]:
          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		get_results(lowerCamelCase_    )
          self.assertGreaterEqual(result["""eval_bleu"""] ,3_0    )
          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 UpperCamelCase__		( self    )    ->      Dict:
          A				  =		logging.StreamHandler(sys.stdout    )
          logger.addHandler(lowerCamelCase_    )

          A				  =		self.get_auto_remove_tmp_dir()
          A				  =		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    )
          A				  =		get_results(lowerCamelCase_    )
          self.assertGreaterEqual(result["""eval_overall_accuracy"""] ,0.10    )




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


from statistics import mean, stdev



def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		min(_a				)
      A				  =		max(_a				)
      # normalize data
      return [round((x - x_min) / (x_max - x_min)     ,   _a				) for x in data]





def   _A					(	_a	:						list     ,   _a	:						int = 3				):



      """simple docstring"""






      A				  =		mean(_a				)
      A				  =		stdev(_a				)
      # standardize data
      return [round((x - mu) / (sigma)     ,   _a				) for x in data]




