"""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       a_      (     __UpperCamelCase			):




					def __init__(	self						:     str     ):
							lowerCAmelCase__        =   []




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]  ,						**snake_case__						:     Any     ):
							self.events.append("""on_init_end"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     str  ,						snake_case__						:     List[Any]  ,						**snake_case__						:     str     ):
							self.events.append("""on_train_begin"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Tuple  ,						snake_case__						:     str  ,						snake_case__						:     str  ,						**snake_case__						:     Any     ):
							self.events.append("""on_train_end"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     str  ,						snake_case__						:     List[str]  ,						snake_case__						:     str  ,						**snake_case__						:     Dict     ):
							self.events.append("""on_epoch_begin"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     List[str]  ,						snake_case__						:     List[str]  ,						**snake_case__						:     str     ):
							self.events.append("""on_epoch_end"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Optional[int]  ,						**snake_case__						:     str     ):
							self.events.append("""on_step_begin"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     List[Any]  ,						snake_case__						:     Dict  ,						**snake_case__						:     int     ):
							self.events.append("""on_step_end"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     List[str]  ,						snake_case__						:     Dict  ,						snake_case__						:     Optional[Any]  ,						**snake_case__						:     Any     ):
							self.events.append("""on_evaluate"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Tuple  ,						snake_case__						:     Any  ,						snake_case__						:     int  ,						**snake_case__						:     Optional[int]     ):
							self.events.append("""on_predict"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     Any  ,						snake_case__						:     Union[str, Any]  ,						**snake_case__						:     List[str]     ):
							self.events.append("""on_save"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     str  ,						snake_case__						:     Union[str, Any]  ,						snake_case__						:     int  ,						**snake_case__						:     List[Any]     ):
							self.events.append("""on_log"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     Optional[Any]  ,						snake_case__						:     Optional[int]  ,						snake_case__						:     Any  ,						**snake_case__						:     List[Any]     ):
							self.events.append("""on_prediction_step"""     )

@require_torch
class       a_      (     unittest.TestCase			):




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							lowerCAmelCase__        =   tempfile.mkdtemp()




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							shutil.rmtree(self.output_dir     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						snake_case__						:     Dict=0  ,						snake_case__						:     List[str]=0  ,						snake_case__						:     Any=64  ,						snake_case__						:     Tuple=64  ,						snake_case__						:     str=None  ,						snake_case__						:     int=False  ,						**snake_case__						:     List[str]     ):
							# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
							# its set to False since the tests later on depend on its value.
							lowerCAmelCase__        =   RegressionDataset(length=snake_case__     )
							lowerCAmelCase__        =   RegressionDataset(length=snake_case__     )
							lowerCAmelCase__        =   RegressionModelConfig(a=snake_case__  ,						b=snake_case__     )
							lowerCAmelCase__        =   RegressionPreTrainedModel(snake_case__     )

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						snake_case__						:     Tuple  ,						snake_case__						:     Union[str, Any]     ):
							self.assertEqual(len(snake_case__     )  ,						len(snake_case__     )     )

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]  ,						snake_case__						:     List[str]     ):
							lowerCAmelCase__        =   ["""on_init_end""", """on_train_begin"""]
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   len(trainer.get_eval_dataloader()     )
							lowerCAmelCase__        =   ["""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(snake_case__     ):
											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 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							lowerCAmelCase__        =   self.get_trainer()
							lowerCAmelCase__        =   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
							self.check_callbacks_equality(trainer.callback_handler.callbacks  ,						snake_case__     )

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
							lowerCAmelCase__        =   self.get_trainer()

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

							lowerCAmelCase__        =   self.get_trainer()
							lowerCAmelCase__        =   trainer.pop_callback(snake_case__     )
							self.assertEqual(cb.__class__  ,						snake_case__     )
							self.check_callbacks_equality(trainer.callback_handler.callbacks  ,						snake_case__     )

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

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

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

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							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=snake_case__     )

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

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

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

							lowerCAmelCase__        =   self.get_trainer(callbacks=[MyTestTrainerCallback]  ,						eval_steps=5  ,						evaluation_strategy="""steps"""     )
							trainer.train()
							lowerCAmelCase__        =   trainer.callback_handler.callbacks[-2].events
							self.assertEqual(snake_case__  ,						self.get_expected_events(snake_case__     )     )

							lowerCAmelCase__        =   self.get_trainer(callbacks=[MyTestTrainerCallback]  ,						evaluation_strategy="""epoch"""     )
							trainer.train()
							lowerCAmelCase__        =   trainer.callback_handler.callbacks[-2].events
							self.assertEqual(snake_case__  ,						self.get_expected_events(snake_case__     )     )

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

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





"""simple docstring"""




import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class       a_      :




					def __init__(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   256
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   cva.imread(snake_case__  ,						0     )
							lowerCAmelCase__        =   copy.deepcopy(self.img     )
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   plt.hist(self.img.ravel()  ,						256  ,						[0, 256]  ,						label="""x"""     )
							lowerCAmelCase__        =   np.sum(snake_case__     )
							for i in range(len(snake_case__     )     ):
									lowerCAmelCase__        =   x[i] / self.k
									self.sk += prk
									lowerCAmelCase__        =   (self.L - 1) * self.sk
									if self.rem != 0:
											lowerCAmelCase__        =   int(last % last     )
									lowerCAmelCase__        =   int(last + 1 if self.rem >= 0.5 else last     )
									self.last_list.append(snake_case__     )
									lowerCAmelCase__        =   int(np.ma.count(self.img     ) / self.img[1].size     )
									lowerCAmelCase__        =   self.img[1].size
							for i in range(self.number_of_cols     ):
									for j in range(self.number_of_rows     ):
											lowerCAmelCase__        =   self.img[j][i]
											if num != self.last_list[num]:
													lowerCAmelCase__        =   self.last_list[num]
							cva.imwrite("""output_data/output.jpg"""  ,						self.img     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							plt.hist(self.img.ravel()  ,						256  ,						[0, 256]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							cva.imshow("""Output-Image"""  ,						self.img     )
							cva.imshow("""Input-Image"""  ,						self.original_image     )
							cva.waitKey(5000     )
							cva.destroyAllWindows()


if __name__ == "__main__":
			__lowerCAmelCase    :							Dict													=						os.path.join(os.path.basename(__file__), "image_data/input.jpg")
			__lowerCAmelCase    :							Optional[int]													=						ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()




