'''simple docstring'''





from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE_:     Optional[int]  =3_00  # TEMPERATURE (unit = K)





def 	lowerCAmelCase_				(					snake_case_       : float					,    snake_case_       : float					,    snake_case_       : float					,    )    ->      float:


     '''simple docstring'''




     if donor_conc <= 0:
          raise ValueError("Donor concentration should be positive"			)
     elif acceptor_conc <= 0:
          raise ValueError("Acceptor concentration should be positive"			)
     elif intrinsic_conc <= 0:
          raise ValueError("Intrinsic concentration should be positive"			)
     elif donor_conc <= intrinsic_conc:
          raise ValueError(
              "Donor concentration should be greater than intrinsic concentration"			)
     elif acceptor_conc <= intrinsic_conc:
          raise ValueError(
              "Acceptor concentration should be greater than intrinsic concentration"			)
     else:
          return (
              Boltzmann
              * T
              * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
              / physical_constants["electron volt"][0]
          )


if __name__ == "__main__":
    import doctest

    doctest.testmod()








"""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"						)						)						)





