






'''simple docstring'''




import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test







class       __lowercase					(unittest.TestCase    ):

	@parameterized.expand([(None,), ('foo.json',)])
	def      __UpperCamelCase							(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict):
			UpperCamelCase__				:					int					=					GenerationConfig(
			    do_sample=__a  ,					temperature=0.7  ,					length_penalty=1.0  ,					bad_words_ids=[[1, 2, 3], [4, 5]]  ,					)
			with tempfile.TemporaryDirectory() as tmp_dir:
					config.save_pretrained(__a  ,					config_name=__a)
					UpperCamelCase__				:					Union[str, Any]					=					GenerationConfig.from_pretrained(__a  ,					config_name=__a)

			# Checks parameters that were specified
			self.assertEqual(loaded_config.do_sample  ,					__a)
			self.assertEqual(loaded_config.temperature  ,					0.7)
			self.assertEqual(loaded_config.length_penalty  ,					1.0)
			self.assertEqual(loaded_config.bad_words_ids  ,					[[1, 2, 3], [4, 5]])

			# Checks parameters that were not specified (defaults)
			self.assertEqual(loaded_config.top_k  ,					50)
			self.assertEqual(loaded_config.max_length  ,					20)
			self.assertEqual(loaded_config.max_time  ,					__a)

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					List[str]					=					AutoConfig.from_pretrained('gpt2')
			UpperCamelCase__				:					Optional[int]					=					GenerationConfig.from_model_config(__a)
			UpperCamelCase__				:					Dict					=					GenerationConfig()

			# The generation config has loaded a few non-default parameters from the model config
			self.assertNotEqual(__a  ,					__a)

			# One of those parameters is eos_token_id -- check if it matches
			self.assertNotEqual(generation_config_from_model.eos_token_id  ,					default_generation_config.eos_token_id)
			self.assertEqual(generation_config_from_model.eos_token_id  ,					model_config.eos_token_id)

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					str					=					GenerationConfig()
			UpperCamelCase__				:					Optional[int]					=					{
			    """max_new_tokens""": 1_024,
			    """foo""": """bar""",
			}
			UpperCamelCase__				:					Any					=					copy.deepcopy(__a)
			UpperCamelCase__				:					List[str]					=					generation_config.update(**__a)

			# update_kwargs was not modified (no side effects)
			self.assertEqual(__a  ,					__a)

			# update_kwargs was used to update the config on valid attributes
			self.assertEqual(generation_config.max_new_tokens  ,					1_024)

			# `.update()` returns a dictionary of unused kwargs
			self.assertEqual(__a  ,					{'foo': 'bar'})

	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Tuple					=					GenerationConfig()
			UpperCamelCase__				:					Any					=					"""bar"""

			with tempfile.TemporaryDirectory('test-generation-config') as tmp_dir:
					generation_config.save_pretrained(__a)

					UpperCamelCase__				:					List[str]					=					GenerationConfig.from_pretrained(__a)
			# update_kwargs was used to update the config on valid attributes
			self.assertEqual(new_config.foo  ,					'bar')

			UpperCamelCase__				:					Dict					=					GenerationConfig.from_model_config(__a)
			assert not hasattr(__a  ,					'foo')  # no new kwargs should be initialized if from config





	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Dict					=					GenerationConfig()
			self.assertEqual(default_config.temperature  ,					1.0)
			self.assertEqual(default_config.do_sample  ,					__a)
			self.assertEqual(default_config.num_beams  ,					1)

			UpperCamelCase__				:					int					=					GenerationConfig(
			    do_sample=__a  ,					temperature=0.7  ,					length_penalty=1.0  ,					bad_words_ids=[[1, 2, 3], [4, 5]]  ,					)
			self.assertEqual(config.temperature  ,					0.7)
			self.assertEqual(config.do_sample  ,					__a)
			self.assertEqual(config.num_beams  ,					1)

			with tempfile.TemporaryDirectory() as tmp_dir:
					config.save_pretrained(__a)
					UpperCamelCase__				:					Dict					=					GenerationConfig.from_pretrained(__a  ,					temperature=1.0)

			self.assertEqual(loaded_config.temperature  ,					1.0)
			self.assertEqual(loaded_config.do_sample  ,					__a)
			self.assertEqual(loaded_config.num_beams  ,					1)  # default value







@is_staging_test
class       __lowercase					(unittest.TestCase    ):

	@classmethod
	def      __UpperCamelCase							(      cls   :						Any):
			UpperCamelCase__				:					Dict					=					TOKEN
			HfFolder.save_token(__a)

	@classmethod
	def      __UpperCamelCase							(      cls   :						List[Any]):
			try:
					delete_repo(token=cls._token  ,					repo_id='test-generation-config')
			except HTTPError:
					pass

			try:
					delete_repo(token=cls._token  ,					repo_id='valid_org/test-generation-config-org')
			except HTTPError:
					pass

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					int					=					GenerationConfig(
			    do_sample=__a  ,					temperature=0.7  ,					length_penalty=1.0  ,					)
			config.push_to_hub('test-generation-config'  ,					use_auth_token=self._token)

			UpperCamelCase__				:					Union[str, Any]					=					GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
			for k, v in config.to_dict().items():
					if k != "transformers_version":
							self.assertEqual(__a  ,					getattr(__a  ,					__a))

        # Reset repo
			delete_repo(token=self._token  ,					repo_id='test-generation-config')

			# Push to hub via save_pretrained
			with tempfile.TemporaryDirectory() as tmp_dir:
					config.save_pretrained(
					    __a  ,					repo_id='test-generation-config'  ,					push_to_hub=__a  ,					use_auth_token=self._token)

			UpperCamelCase__				:					str					=					GenerationConfig.from_pretrained(F'{USER}/test-generation-config')
			for k, v in config.to_dict().items():
					if k != "transformers_version":
							self.assertEqual(__a  ,					getattr(__a  ,					__a))





	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Any					=					GenerationConfig(
			    do_sample=__a  ,					temperature=0.7  ,					length_penalty=1.0  ,					)
			config.push_to_hub('valid_org/test-generation-config-org'  ,					use_auth_token=self._token)

			UpperCamelCase__				:					Any					=					GenerationConfig.from_pretrained('valid_org/test-generation-config-org')
			for k, v in config.to_dict().items():
					if k != "transformers_version":
							self.assertEqual(__a  ,					getattr(__a  ,					__a))

        # Reset repo
			delete_repo(token=self._token  ,					repo_id='valid_org/test-generation-config-org')

			# Push to hub via save_pretrained
			with tempfile.TemporaryDirectory() as tmp_dir:
					config.save_pretrained(
					    __a  ,					repo_id='valid_org/test-generation-config-org'  ,					push_to_hub=__a  ,					use_auth_token=self._token)

			UpperCamelCase__				:					Optional[Any]					=					GenerationConfig.from_pretrained('valid_org/test-generation-config-org')
			for k, v in config.to_dict().items():
					if k != "transformers_version":
							self.assertEqual(__a  ,					getattr(__a  ,					__a))







'''simple docstring'''




from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase__          =						300  # TEMPERATURE (unit = K)







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      )							->     float:

		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()




