
"""simple docstring"""





import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
				from transformers.trainer import DEFAULT_CALLBACKS

				from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel






class    _UpperCAmelCase(   lowerCamelCase     ):



		def __init__( self)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  []



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	int:






							'''simple docstring'''
							self.events.append('''on_init_end''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	Optional[int]:






							'''simple docstring'''
							self.events.append('''on_train_begin''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	Dict:






							'''simple docstring'''
							self.events.append('''on_train_end''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	str:






							'''simple docstring'''
							self.events.append('''on_epoch_begin''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	Dict:






							'''simple docstring'''
							self.events.append('''on_epoch_end''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	Any:






							'''simple docstring'''
							self.events.append('''on_step_begin''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	List[Any]:






							'''simple docstring'''
							self.events.append('''on_step_end''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	Optional[int]:






							'''simple docstring'''
							self.events.append('''on_evaluate''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	str:






							'''simple docstring'''
							self.events.append('''on_predict''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	str:






							'''simple docstring'''
							self.events.append('''on_save''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	int:






							'''simple docstring'''
							self.events.append('''on_log''')




		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a    ,		**__a)	->	Dict:






							'''simple docstring'''
							self.events.append('''on_prediction_step''')






@require_torch
class    _UpperCAmelCase(   unittest.TestCase     ):



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  tempfile.mkdtemp()



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							shutil.rmtree(self.output_dir)



		def 						UpperCAmelCase							( self    ,		__a=0    ,		__a=0    ,		__a=64    ,		__a=64    ,		__a=None    ,		__a=False    ,		**__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  RegressionDataset(length=__a)
							_UpperCamelCase										=  RegressionDataset(length=__a)
							_UpperCamelCase										=  RegressionModelConfig(a=__a    ,		b=__a)
							_UpperCamelCase										=  RegressionPreTrainedModel(__a)

							_UpperCamelCase										=  TrainingArguments(self.output_dir    ,		disable_tqdm=__a    ,		report_to=[]    ,		**__a)
							return Trainer(
							    __a    ,		__a    ,		train_dataset=__a    ,		eval_dataset=__a    ,		callbacks=__a    ,		)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							self.assertEqual(len(__a)    ,		len(__a))

							# Order doesn't matter
							_UpperCamelCase										=  sorted(__a    ,		key=lambda __a: cb.__name__ if isinstance(__a    ,		__a) else cb.__class__.__name__)
							_UpperCamelCase										=  sorted(__a    ,		key=lambda __a: cb.__name__ if isinstance(__a    ,		__a) else cb.__class__.__name__)

							for cba, cba in zip(__a    ,		__a):
												if isinstance(__a    ,		__a) and isinstance(__a    ,		__a):
																	self.assertEqual(__a    ,		__a)
												elif isinstance(__a    ,		__a) and not isinstance(__a    ,		__a):
																	self.assertEqual(__a    ,		cba.__class__)
												elif not isinstance(__a    ,		__a) and isinstance(__a    ,		__a):
																	self.assertEqual(cba.__class__    ,		__a)
												else:
																	self.assertEqual(__a    ,		__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  ['''on_init_end''', '''on_train_begin''']
							_UpperCamelCase										=  0
							_UpperCamelCase										=  len(trainer.get_eval_dataloader())
							_UpperCamelCase										=  ['''on_prediction_step'''] * len(trainer.get_eval_dataloader()) + ['''on_log''', '''on_evaluate''']
							for _ in range(trainer.state.num_train_epochs):
												expected_events.append('''on_epoch_begin''')
												for _ in range(__a):
																	step += 1
																	expected_events += ["on_step_begin", "on_step_end"]
																	if step % trainer.args.logging_steps == 0:
																						expected_events.append('''on_log''')
																	if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
																						expected_events += evaluation_events.copy()
																	if step % trainer.args.save_steps == 0:
																						expected_events.append('''on_save''')
												expected_events.append('''on_epoch_end''')
												if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
																	expected_events += evaluation_events.copy()
							expected_events += ["on_log", "on_train_end"]
							return expected_events



		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  self.get_trainer()
							_UpperCamelCase										=  DEFAULT_CALLBACKS.copy() + [ProgressCallback]
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							# Callbacks passed at init are added to the default callbacks
							_UpperCamelCase										=  self.get_trainer(callbacks=[MyTestTrainerCallback])
							expected_callbacks.append(__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
							_UpperCamelCase										=  self.get_trainer(disable_tqdm=__a)
							_UpperCamelCase										=  DEFAULT_CALLBACKS.copy() + [PrinterCallback]
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)



		def 						UpperCAmelCase							( self)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  DEFAULT_CALLBACKS.copy() + [ProgressCallback]
							_UpperCamelCase										=  self.get_trainer()

							# We can add, pop, or remove by class name
							trainer.remove_callback(__a)
							expected_callbacks.remove(__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							_UpperCamelCase										=  self.get_trainer()
							_UpperCamelCase										=  trainer.pop_callback(__a)
							self.assertEqual(cb.__class__    ,		__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							trainer.add_callback(__a)
							expected_callbacks.insert(0    ,		__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							# We can also add, pop, or remove by instance
							_UpperCamelCase										=  self.get_trainer()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[0]
							trainer.remove_callback(__a)
							expected_callbacks.remove(__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							_UpperCamelCase										=  self.get_trainer()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[0]
							_UpperCamelCase										=  trainer.pop_callback(__a)
							self.assertEqual(__a    ,		__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)

							trainer.add_callback(__a)
							expected_callbacks.insert(0    ,		__a)
							self.check_callbacks_equality(trainer.callback_handler.callbacks    ,		__a)




		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							import warnings

							# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
							warnings.simplefilter(action='''ignore'''    ,		category=__a)

							_UpperCamelCase										=  self.get_trainer(callbacks=[MyTestTrainerCallback])
							trainer.train()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[-2].events
							self.assertEqual(__a    ,		self.get_expected_events(__a))

							# Independent log/save/eval
							_UpperCamelCase										=  self.get_trainer(callbacks=[MyTestTrainerCallback]    ,		logging_steps=5)
							trainer.train()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[-2].events
							self.assertEqual(__a    ,		self.get_expected_events(__a))

							_UpperCamelCase										=  self.get_trainer(callbacks=[MyTestTrainerCallback]    ,		save_steps=5)
							trainer.train()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[-2].events
							self.assertEqual(__a    ,		self.get_expected_events(__a))

							_UpperCamelCase										=  self.get_trainer(callbacks=[MyTestTrainerCallback]    ,		eval_steps=5    ,		evaluation_strategy='''steps''')
							trainer.train()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[-2].events
							self.assertEqual(__a    ,		self.get_expected_events(__a))

							_UpperCamelCase										=  self.get_trainer(callbacks=[MyTestTrainerCallback]    ,		evaluation_strategy='''epoch''')
							trainer.train()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[-2].events
							self.assertEqual(__a    ,		self.get_expected_events(__a))

							# A bit of everything
							_UpperCamelCase										=  self.get_trainer(
							    callbacks=[MyTestTrainerCallback]    ,		logging_steps=3    ,		save_steps=10    ,		eval_steps=5    ,		evaluation_strategy='''steps'''    ,		)
							trainer.train()
							_UpperCamelCase										=  trainer.callback_handler.callbacks[-2].events
							self.assertEqual(__a    ,		self.get_expected_events(__a))

							# warning should be emitted for duplicated callbacks
							with patch('''transformers.trainer_callback.logger.warning''') as warn_mock:
												_UpperCamelCase										=  self.get_trainer(
												    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]    ,		)
												assert str(__a) in warn_mock.call_args[0][0]








"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 				lowerCamelCase__		(      __snake_case     )							->		None:


					"""simple docstring"""





					_UpperCamelCase			,  _UpperCamelCase										=  analyze_text(__snake_case     )
					_UpperCamelCase										=  list(''' ''' + ascii_lowercase     )
					# what is our total sum of probabilities.
					_UpperCamelCase										=  sum(single_char_strings.values()     )

					# one length string
					_UpperCamelCase										=  0
					# for each alpha we go in our dict and if it is in it we calculate entropy
					for ch in my_alphas:
										if ch in single_char_strings:
															_UpperCamelCase										=  single_char_strings[ch]
															_UpperCamelCase										=  my_str / all_sum
															my_fir_sum += prob * math.loga(__snake_case     )  # entropy formula.

    # print entropy
					print(F'''{round(-1 * my_fir_sum     ):.1f}'''     )

					# two len string
					_UpperCamelCase										=  sum(two_char_strings.values()     )
					_UpperCamelCase										=  0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															_UpperCamelCase										=  cha + cha
															if sequence in two_char_strings:
																				_UpperCamelCase										=  two_char_strings[sequence]
																				_UpperCamelCase										=  int(__snake_case     ) / all_sum
																				my_sec_sum += prob * math.loga(__snake_case     )

    # print second entropy
					print(F'''{round(-1 * my_sec_sum     ):.1f}'''     )

					# print the difference between them
					print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)     ):.1f}'''     )



def 				lowerCamelCase__		(      __snake_case     )							->		tuple[dict, dict]:


					"""simple docstring"""





					_UpperCamelCase										=  Counter()  # type: ignore
					_UpperCamelCase										=  Counter()  # type: ignore
					single_char_strings[text[-1]] += 1

					# first case when we have space at start.
					two_char_strings[" " + text[0]] += 1
					for i in range(0,					len(__snake_case     ) - 1     ):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings



def 				lowerCamelCase__		(      )							->		Dict:


					"""simple docstring"""





					import doctest

					doctest.testmod()
					# text = (
					#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
					#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
					#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
					#     "beloved evident through no service elderly is. Blind there if every no so "
					#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
					#     "of message cottage windows do besides against uncivil.  Delightful "
					#     "unreserved impossible few estimating men favourable see entreaties. She "
					#     "propriety immediate was improving. He or entrance humoured likewise "
					#     "moderate. Much nor game son say feel. Fat make met can must form into "
					#     "gate. Me we offending prevailed discovery. "
					# )

					# calculate_prob(text)


if __name__ == "__main__":
				main()






