





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)

SCREAMING_SNAKE_CASE_   :      str			=   logging.getLogger()



def  SCREAMING_SNAKE_CASE				(      )		->    List[str]:
		__lowercase											=     argparse.ArgumentParser()
		parser.add_argument('-f'							)
		__lowercase											=     parser.parse_args()
		return args.f



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    List[Any]:
		__lowercase											=     {}
		__lowercase											=     os.path.join(snake_case						,   'all_results.json'							)
		if os.path.exists(snake_case							):
				with open(snake_case						,   'r'							) as f:
						__lowercase											=     json.load(snake_case							)
		else:
				raise ValueError(F"can't find {path}"							)
		return results



def  SCREAMING_SNAKE_CASE				(      )		->    Any:
		__lowercase											=     torch.cuda.is_available() and torch_device == 'cuda'
		return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE_   :      Optional[int]			=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	@classmethod
	def    UpperCAmelCase			(						cls							:     Any   )       -> List[str]:

			'''simple docstring'''




			__lowercase											=     tempfile.mkdtemp()
			__lowercase											=     os.path.join(cls.tmpdir					,      'default_config.yml'   )
			write_basic_config(save_location=cls.configPath   )
			__lowercase											=     ['accelerate', 'launch', '--config_file', cls.configPath]




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

			'''simple docstring'''




			shutil.rmtree(cls.tmpdir   )




	@mock.patch.dict(os.environ					,      {'WANDB_MODE': 'offline'}   )
	def    UpperCAmelCase			(						self							:     List[Any]   )       -> str:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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							:     Any   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Tuple   )       -> int:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Dict   )       -> int:

			'''simple docstring'''




			__lowercase											=     7 if get_gpu_count() > 1 else 2

			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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							:     Optional[Any]   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Any   )       -> Tuple:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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]   )       -> Union[str, Any]:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Optional[int]   )       -> Dict:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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    UpperCAmelCase			(						self							:     Tuple   )       -> Optional[int]:

			'''simple docstring'''




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

			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     get_results(__lowerCamelCase   )
			self.assertGreaterEqual(result['eval_overall_accuracy']					,      0.10   )






	@mock.patch.dict(os.environ					,      {'WANDB_MODE': 'offline'}   )
	def    UpperCAmelCase			(						self							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.get_auto_remove_tmp_dir()
			__lowercase											=     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											=     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'   )   )   )











SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




