




"""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


lowerCAmelCase							:    Union[str, Any]  							=							"""scheduler_config.json"""






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		1
				__UpperCamelCase    						=		2
				__UpperCamelCase    						=		3
				__UpperCamelCase    						=		4
				__UpperCamelCase    						=		5






@dataclass
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		42






class 					__magic_name__							:






				'''simple docstring'''
				__UpperCamelCase    						=		SCHEDULER_CONFIG_NAME
				__UpperCamelCase    						=		["dtype"]
				__UpperCamelCase    						=		[]
				__UpperCamelCase    						=		True





				@classmethod
				def      _lowerCAmelCase       ( cls	,				_a = None	,				_a = None	,				_a=False	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase   ,     lowerCamelCase       =      cls.load_config(
							    pretrained_model_name_or_path=_a	,				subfolder=_a	,				return_unused_kwargs=_a	,				**_a	,				)
							lowerCamelCase   ,     lowerCamelCase       =      cls.from_config(_a	,				return_unused_kwargs=_a	,				**_a				)

							if hasattr(_a	,				"""create_state"""				) and getattr(_a	,				"""has_state"""	,				_a				):
										lowerCamelCase       =      scheduler.create_state()

							if return_unused_kwargs:
										return scheduler, state, unused_kwargs

							return scheduler, state





				def      _lowerCAmelCase       ( self	,				_a	,				_a = False	,				**_a				):





							"""simple docstring"""
							self.save_config(save_directory=_a	,				push_to_hub=_a	,				**_a				)





				@property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return self._get_compatibles()





				@classmethod
				def      _lowerCAmelCase       ( cls				):





							"""simple docstring"""
							lowerCamelCase       =      list(set([cls.__name__] + cls._compatibles				)				)
							lowerCamelCase       =      importlib.import_module(__name__.split("""."""				)[0]				)
							lowerCamelCase       =      [
							    getattr(_a	,				_a				) for c in compatible_classes_str if hasattr(_a	,				_a				)
							]
							return compatible_classes




def 					a__				(       snake_case__ ,				snake_case__       )		->      jnp.ndarray:
			assert len(snake_case__       ) >= x.ndim
			return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case__       ) - x.ndim)       ) ,				snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__=0.999 ,				snake_case__=jnp.floataa       )		->      jnp.ndarray:

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

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






@flax.struct.dataclass
class 					__magic_name__							:






				'''simple docstring'''
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42
				__UpperCamelCase    						=		42





				@classmethod
				def      _lowerCAmelCase       ( cls	,				_a				):





							"""simple docstring"""
							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(_a	,				axis=0				)

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




def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[str]:
			lowerCamelCase       =      state.alphas_cumprod

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

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

			return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      int:
			lowerCamelCase   ,     lowerCamelCase       =      get_sqrt_alpha_prod(snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )
			lowerCamelCase       =      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[Any]:
			lowerCamelCase   ,     lowerCamelCase       =      get_sqrt_alpha_prod(snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )
			lowerCamelCase       =      sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
			return velocity









"""simple docstring"""




import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


lowerCAmelCase							:    Optional[Any]  							=							"""sshleifer/bart-tiny-random"""
lowerCAmelCase							:    List[Any]  							=							"""patrickvonplaten/t5-tiny-random"""






@require_torch
class 					__magic_name__							( unittest.TestCase       ):






				'''simple docstring'''





				@cached_property
				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							return AutoConfig.from_pretrained(_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=1				)
							self.assertEqual(student.config.num_hidden_layers	,				1				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=_a				)
							self.assertEqual(student.config.encoder_layers	,				1				)
							self.assertEqual(student.config.decoder_layers	,				self.teacher_config.encoder_layers				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase   ,     *lowerCamelCase       =      create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=1	,				d=1				)
							self.assertEqual(student.config.encoder_layers	,				1				)
							self.assertEqual(student.config.decoder_layers	,				1				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							with self.assertRaises(_a				):
										create_student_by_copying_alternating_layers(_a	,				tempfile.mkdtemp()	,				e=_a	,				d=_a				)



