





'''simple docstring'''






import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


A__				:   Dict      ='''scheduler_config.json'''




class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			List[Any]						     =   1
      _lowercase:			List[str]						     =   2
      _lowercase:			str						     =   3
      _lowercase:			Union[str, Any]						     =   4
      _lowercase:			Dict						     =   5




@dataclass
class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			jnp.ndarray




class  UpperCAmelCase     :
      _lowercase:			Any						     =   SCHEDULER_CONFIG_NAME
      _lowercase:			Any						     =   ['''dtype''']
      _lowercase:			Optional[int]						     =   []
      _lowercase:			Union[str, Any]						     =   True




      @classmethod
      def 				lowercase__    (			cls			: List[str]			,			__snake_case			: Dict[str, Any] = None			,			__snake_case			: Optional[str] = None			,			__snake_case			: List[Any]=False			,			**__snake_case			: List[Any]			,			)				->      int:
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							cls.load_config(
                pretrained_model_name_or_path=__snake_case			,			subfolder=__snake_case			,			return_unused_kwargs=__snake_case			,			**__snake_case			,			)
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							cls.from_config(__snake_case			,			return_unused_kwargs=__snake_case			,			**__snake_case  )

            if hasattr(__snake_case			,			"""create_state"""  ) and getattr(__snake_case			,			"""has_state"""			,			__snake_case  ):
                  _lowerCAmelCase							     =							scheduler.create_state()

            if return_unused_kwargs:
                  return scheduler, state, unused_kwargs

            return scheduler, state




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: Union[str, os.PathLike]			,			__snake_case			: bool = False			,			**__snake_case			: Dict  )				->      int:
            self.save_config(save_directory=__snake_case			,			push_to_hub=__snake_case			,			**__snake_case  )




      @property
      def 				lowercase__    (			self			: Optional[int]  )				->      Dict:
            return self._get_compatibles()




      @classmethod
      def 				lowercase__    (			cls			: List[str]  )				->      Optional[int]:
            _lowerCAmelCase							     =							list(set([cls.__name__] + cls._compatibles  )  )
            _lowerCAmelCase							     =							importlib.import_module(__name__.split("""."""  )[0]  )
            _lowerCAmelCase							     =							[
                getattr(__snake_case			,			__snake_case  ) for c in compatible_classes_str if hasattr(__snake_case			,			__snake_case  )
            ]
            return compatible_classes




def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      assert len(lowerCAmelCase  ) >= x.ndim
      return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowerCAmelCase  ) - x.ndim)  )							,	lowerCAmelCase  )
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase=0.999							,	lowerCAmelCase=jnp.floataa  ):





      """simple docstring"""


      def alpha_bar(lowerCAmelCase  ):
            return math.cos((time_step + 0.008) / 1.008 * math.pi / 2  ) ** 2

      _lowerCAmelCase							     =							[]
      for i in range(lowerCAmelCase  ):
            _lowerCAmelCase							     =							i / num_diffusion_timesteps
            _lowerCAmelCase							     =							(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar(lowerCAmelCase  ) / alpha_bar(lowerCAmelCase  )							,	lowerCAmelCase  )  )
      return jnp.array(lowerCAmelCase							,	dtype=lowerCAmelCase  )




@flax.struct.dataclass
class  UpperCAmelCase     :
      _lowercase:			jnp.ndarray
      _lowercase:			jnp.ndarray
      _lowercase:			jnp.ndarray




      @classmethod
      def 				lowercase__    (			cls			: int			,			__snake_case			: List[Any]  )				->      int:
            _lowerCAmelCase							     =							scheduler.config

            if config.trained_betas is not None:
                  _lowerCAmelCase							     =							jnp.asarray(config.trained_betas			,			dtype=scheduler.dtype  )
            elif config.beta_schedule == "linear":
                  _lowerCAmelCase							     =							jnp.linspace(config.beta_start			,			config.beta_end			,			config.num_train_timesteps			,			dtype=scheduler.dtype  )
            elif config.beta_schedule == "scaled_linear":
                  # this schedule is very specific to the latent diffusion model.
                  _lowerCAmelCase							     =							(
                      jnp.linspace(
                          config.beta_start**0.5			,			config.beta_end**0.5			,			config.num_train_timesteps			,			dtype=scheduler.dtype  )
                      ** 2
                  )
            elif config.beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  _lowerCAmelCase							     =							betas_for_alpha_bar(config.num_train_timesteps			,			dtype=scheduler.dtype  )
            else:
                  raise NotImplementedError(
                      f"beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"  )

            _lowerCAmelCase							     =							1.0 - betas

            _lowerCAmelCase							     =							jnp.cumprod(__snake_case			,			axis=0  )

            return cls(
                alphas=__snake_case			,			betas=__snake_case			,			alphas_cumprod=__snake_case			,			)




def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							state.alphas_cumprod

      _lowerCAmelCase							     =							alphas_cumprod[timesteps] ** 0.5
      _lowerCAmelCase							     =							sqrt_alpha_prod.flatten()
      _lowerCAmelCase							     =							broadcast_to_shape_from_left(lowerCAmelCase							,	original_samples.shape  )

      _lowerCAmelCase							     =							(1 - alphas_cumprod[timesteps]) ** 0.5
      _lowerCAmelCase							     =							sqrt_one_minus_alpha_prod.flatten()
      _lowerCAmelCase							     =							broadcast_to_shape_from_left(lowerCAmelCase							,	original_samples.shape  )

      return sqrt_alpha_prod, sqrt_one_minus_alpha_prod
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase     ,							_lowerCAmelCase							     =							get_sqrt_alpha_prod(lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  )
      _lowerCAmelCase							     =							sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples





def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase     ,							_lowerCAmelCase							     =							get_sqrt_alpha_prod(lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  )
      _lowerCAmelCase							     =							sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
      return velocity











"""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		:       Optional[int]				):
     snake_case_ :     str				=	[]

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Any  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[str]  ,     **lowercase_		:       Tuple				):
     self.events.append('''on_init_end'''				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       str  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]  ,     **lowercase_		:       List[str]				):
     self.events.append('''on_train_begin'''				)

 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       List[str]  ,     lowercase_		:       Tuple  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       Optional[int]				):
     self.events.append('''on_train_end'''				)

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int  ,     lowercase_		:       Optional[Any]  ,     **lowercase_		:       List[Any]				):
     self.events.append('''on_epoch_begin'''				)

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       List[str]  ,     lowercase_		:       Dict  ,     lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Optional[Any]				):
     self.events.append('''on_epoch_end'''				)

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       int  ,     **lowercase_		:       Optional[Any]				):
     self.events.append('''on_step_begin'''				)

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       int  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       List[str]				):
     self.events.append('''on_step_end'''				)

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       int  ,     lowercase_		:       Dict  ,     lowercase_		:       List[str]  ,     **lowercase_		:       List[str]				):
     self.events.append('''on_evaluate'''				)

 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       Any  ,     lowercase_		:       List[Any]  ,     **lowercase_		:       str				):
     self.events.append('''on_predict'''				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       int  ,     **lowercase_		:       Union[str, Any]				):
     self.events.append('''on_save'''				)

 def 						_snake_case					(     self		:       str  ,     lowercase_		:       Tuple  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]  ,     **lowercase_		:       Any				):
     self.events.append('''on_log'''				)






 def 						_snake_case					(     self		:       Dict  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]  ,     lowercase_		:       Union[str, Any]  ,     **lowercase_		:       Optional[int]				):
     self.events.append('''on_prediction_step'''				)





@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Tuple				=	tempfile.mkdtemp()

 def 						_snake_case					(     self		:       Tuple				):
     shutil.rmtree(self.output_dir				)

 def 						_snake_case					(     self		:       int  ,     lowercase_		:       Union[str, Any]=0  ,     lowercase_		:       Dict=0  ,     lowercase_		:       List[str]=64  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       Union[str, Any]=None  ,     lowercase_		:       Any=False  ,     **lowercase_		:       List[Any]				):
     # 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.
     snake_case_ :     int				=	RegressionDataset(length=lowercase_				)
     snake_case_ :     Any				=	RegressionDataset(length=lowercase_				)
     snake_case_ :     int				=	RegressionModelConfig(a=lowercase_  ,     b=lowercase_				)
     snake_case_ :     Tuple				=	RegressionPreTrainedModel(lowercase_				)

     snake_case_ :     Any				=	TrainingArguments(self.output_dir  ,     disable_tqdm=lowercase_  ,     report_to=[]  ,     **lowercase_				)
     return Trainer(
         lowercase_  ,     lowercase_  ,     train_dataset=lowercase_  ,     eval_dataset=lowercase_  ,     callbacks=lowercase_  ,     )

 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       Any  ,     lowercase_		:       List[Any]				):
     self.assertEqual(len(lowercase_				)  ,     len(lowercase_				)				)

     # Order doesn't matter
     snake_case_ :     Any				=	sorted(lowercase_  ,     key=lambda lowercase_				: cb.__name__ if isinstance(lowercase_  ,     lowercase_				) else cb.__class__.__name__				)
     snake_case_ :     List[str]				=	sorted(lowercase_  ,     key=lambda lowercase_				: 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 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       Tuple				):
     snake_case_ :     Tuple				=	['''on_init_end''', '''on_train_begin''']
     snake_case_ :     List[Any]				=	0
     snake_case_ :     Union[str, Any]				=	len(trainer.get_eval_dataloader()				)
     snake_case_ :     List[Any]				=	['''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 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Union[str, Any]				=	self.get_trainer()
     snake_case_ :     Dict				=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     # Callbacks passed at init are added to the default callbacks
     snake_case_ :     Optional[Any]				=	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
     snake_case_ :     Optional[int]				=	self.get_trainer(disable_tqdm=lowercase_				)
     snake_case_ :     List[Any]				=	DEFAULT_CALLBACKS.copy() + [PrinterCallback]
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     int				=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
     snake_case_ :     List[Any]				=	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_				)

     snake_case_ :     Dict				=	self.get_trainer()
     snake_case_ :     Optional[int]				=	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
     snake_case_ :     Optional[int]				=	self.get_trainer()
     snake_case_ :     List[Any]				=	trainer.callback_handler.callbacks[0]
     trainer.remove_callback(lowercase_				)
     expected_callbacks.remove(lowercase_				)
     self.check_callbacks_equality(trainer.callback_handler.callbacks  ,     lowercase_				)

     snake_case_ :     List[Any]				=	self.get_trainer()
     snake_case_ :     Optional[int]				=	trainer.callback_handler.callbacks[0]
     snake_case_ :     Optional[Any]				=	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 						_snake_case					(     self		:       List[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=lowercase_				)

     snake_case_ :     int				=	self.get_trainer(callbacks=[MyTestTrainerCallback]				)
     trainer.train()
     snake_case_ :     Union[str, Any]				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     # Independent log/save/eval
     snake_case_ :     int				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     logging_steps=5				)
     trainer.train()
     snake_case_ :     str				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     snake_case_ :     List[Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     save_steps=5				)
     trainer.train()
     snake_case_ :     int				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     snake_case_ :     List[Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     eval_steps=5  ,     evaluation_strategy='''steps'''				)
     trainer.train()
     snake_case_ :     Union[str, Any]				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     snake_case_ :     Union[str, Any]				=	self.get_trainer(callbacks=[MyTestTrainerCallback]  ,     evaluation_strategy='''epoch'''				)
     trainer.train()
     snake_case_ :     Dict				=	trainer.callback_handler.callbacks[-2].events
     self.assertEqual(lowercase_  ,     self.get_expected_events(lowercase_				)				)

     # A bit of everything
     snake_case_ :     str				=	self.get_trainer(
         callbacks=[MyTestTrainerCallback]  ,     logging_steps=3  ,     save_steps=10  ,     eval_steps=5  ,     evaluation_strategy='''steps'''  ,     )
     trainer.train()
     snake_case_ :     str				=	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:
         snake_case_ :     Dict				=	self.get_trainer(
             callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]  ,     )
         assert str(lowercase_				) in warn_mock.call_args[0][0]





