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 (     lowercase_				):






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





       '''simple docstring'''





       UpperCamelCase__					    =   []






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   Any:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   Tuple:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   str:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   str:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   int:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   Optional[int]:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   List[Any]:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   Optional[int]:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   str:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   int:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   List[str]:





       '''simple docstring'''





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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_ ,					**SCREAMING_SNAKE_CASE_						)		->   Tuple:





       '''simple docstring'''





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



@require_torch
class      _A (     unittest.TestCase				):






  def 		_a       (self						)		->   List[Any]:





       '''simple docstring'''





       UpperCamelCase__					    =   tempfile.mkdtemp()






  def 		_a       (self						)		->   Tuple:





       '''simple docstring'''





       shutil.rmtree(self.output_dir						)






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_=0 ,					SCREAMING_SNAKE_CASE_=0 ,					SCREAMING_SNAKE_CASE_=64 ,					SCREAMING_SNAKE_CASE_=64 ,					SCREAMING_SNAKE_CASE_=None ,					SCREAMING_SNAKE_CASE_=False ,					**SCREAMING_SNAKE_CASE_						)		->   str:





       '''simple docstring'''





       UpperCamelCase__					    =   RegressionDataset(length=_lowercase						)
       UpperCamelCase__					    =   RegressionDataset(length=_lowercase						)
       UpperCamelCase__					    =   RegressionModelConfig(a=_lowercase ,					b=_lowercase						)
       UpperCamelCase__					    =   RegressionPreTrainedModel(_lowercase						)

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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_ ,					SCREAMING_SNAKE_CASE_						)		->   List[Any]:





       '''simple docstring'''





       self.assertEqual(len(_lowercase						) ,					len(_lowercase						)						)

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

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






  def 		_a       (self ,					SCREAMING_SNAKE_CASE_						)		->   Dict:





       '''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(_lowercase						):
                 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 		_a       (self						)		->   int:





       '''simple docstring'''





       UpperCamelCase__					    =   self.get_trainer()
       UpperCamelCase__					    =   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
       self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_lowercase						)

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

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






  def 		_a       (self						)		->   Any:





       '''simple docstring'''





       UpperCamelCase__					    =   DEFAULT_CALLBACKS.copy() + [ProgressCallback]
       UpperCamelCase__					    =   self.get_trainer()

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

       UpperCamelCase__					    =   self.get_trainer()
       UpperCamelCase__					    =   trainer.pop_callback(_lowercase						)
       self.assertEqual(cb.__class__ ,					_lowercase						)
       self.check_callbacks_equality(trainer.callback_handler.callbacks ,					_lowercase						)

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

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

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

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






  def 		_a       (self						)		->   str:





       '''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=_lowercase						)

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

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

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

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

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

       # 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(_lowercase ,					self.get_expected_events(_lowercase						)						)

       # 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(_lowercase						) in warn_mock.call_args[0][0]






'''simple docstring'''

import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class    snake_case     (      lowercase_		):






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							List[Any]:
											super().__init__()
											self.register_modules(vqvae=_lowercase,     unet=_lowercase,     scheduler=_lowercase       )





				@torch.no_grad()
				def __call__(    self,     _lowercase = 1,     _lowercase = None,     _lowercase = 0.0,     _lowercase = 50,     _lowercase = "pil",     _lowercase = True,     **_lowercase,     )     ->							Union[Tuple, ImagePipelineOutput]:
											SCREAMING_SNAKE_CASE_				=				randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),     generator=_lowercase,     )
											SCREAMING_SNAKE_CASE_				=				latents.to(self.device       )

											# scale the initial noise by the standard deviation required by the scheduler
											SCREAMING_SNAKE_CASE_				=				latents * self.scheduler.init_noise_sigma

											self.scheduler.set_timesteps(_lowercase       )

											# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
											SCREAMING_SNAKE_CASE_				=				'eta' in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

											SCREAMING_SNAKE_CASE_				=				{}
											if accepts_eta:
																		SCREAMING_SNAKE_CASE_				=				eta

											for t in self.progress_bar(self.scheduler.timesteps       ):
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.scale_model_input(_lowercase,     _lowercase       )
																		# predict the noise residual
																		SCREAMING_SNAKE_CASE_				=				self.unet(_lowercase,     _lowercase       ).sample
																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE_				=				self.scheduler.step(_lowercase,     _lowercase,     _lowercase,     **_lowercase       ).prev_sample

											# decode the image latents with the VAE
											SCREAMING_SNAKE_CASE_				=				self.vqvae.decode(_lowercase       ).sample

											SCREAMING_SNAKE_CASE_				=				(image / 2 + 0.5).clamp(0,     1       )
											SCREAMING_SNAKE_CASE_				=				image.cpu().permute(0,     2,     3,     1       ).numpy()
											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_				=				self.numpy_to_pil(_lowercase       )

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowercase       )





