






'''simple docstring'''







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)




@flax.struct.dataclass
class 		UpperCamelCase__     :




		'''simple docstring'''





		_UpperCamelCase						=       42

		# setable values
		_UpperCamelCase						=       42
		_UpperCamelCase						=       42
		_UpperCamelCase						=       None






		@classmethod
		def       UpperCamelCase_       (   cls ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								return cls(common=_lowerCAmelCase ,init_noise_sigma=_lowerCAmelCase ,timesteps=_lowerCAmelCase							)




@dataclass
class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       42




class 		UpperCamelCase__     (a ,a      ):




		'''simple docstring'''





		_UpperCamelCase						=       [e.name for e in FlaxKarrasDiffusionSchedulers]

		_UpperCamelCase						=       42






		@property
		def       UpperCamelCase_       (   self							):
								return True






		@register_to_config
		def __init__(   self ,_lowerCAmelCase = 10_00 ,_lowerCAmelCase = 0.0001 ,_lowerCAmelCase = 0.02 ,_lowerCAmelCase = "linear" ,_lowerCAmelCase = None ,_lowerCAmelCase = "fixed_small" ,_lowerCAmelCase = True ,_lowerCAmelCase = "epsilon" ,_lowerCAmelCase = jnp.floataa ,):
								lowerCamelCase__								=	dtype






		def       UpperCamelCase_       (   self ,_lowerCAmelCase = None							):
								if common is None:
														lowerCamelCase__								=	CommonSchedulerState.create(self							)

								# standard deviation of the initial noise distribution
								lowerCamelCase__								=	jnp.array(1.0 ,dtype=self.dtype							)

								lowerCamelCase__								=	jnp.arange(0 ,self.config.num_train_timesteps							).round()[::-1]

								return DDPMSchedulerState.create(
								    common=_lowerCAmelCase ,init_noise_sigma=_lowerCAmelCase ,timesteps=_lowerCAmelCase ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None							):
								return sample






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = ()							):
								lowerCamelCase__								=	self.config.num_train_timesteps // num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# rounding to avoid issues when num_inference_step is power of 3
								lowerCamelCase__								=	(jnp.arange(0 ,_lowerCAmelCase							) * step_ratio).round()[::-1]

								return state.replace(
								    num_inference_steps=_lowerCAmelCase ,timesteps=_lowerCAmelCase ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase=None							):
								lowerCamelCase__								=	state.common.alphas_cumprod[t]
								lowerCamelCase__								=	jnp.where(t > 0 ,state.common.alphas_cumprod[t - 1] ,jnp.array(1.0 ,dtype=self.dtype							)							)

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								lowerCamelCase__								=	(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
														lowerCamelCase__								=	self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small":
														lowerCamelCase__								=	jnp.clip(_lowerCAmelCase ,a_min=1E-20							)
								# for rl-diffuser https://arxiv.org/abs/2205.09991
								elif variance_type == "fixed_small_log":
														lowerCamelCase__								=	jnp.log(jnp.clip(_lowerCAmelCase ,a_min=1E-20							)							)
								elif variance_type == "fixed_large":
														lowerCamelCase__								=	state.common.betas[t]
								elif variance_type == "fixed_large_log":
														# Glide max_log
														lowerCamelCase__								=	jnp.log(state.common.betas[t]							)
								elif variance_type == "learned":
														return predicted_variance
								elif variance_type == "learned_range":
														lowerCamelCase__								=	variance
														lowerCamelCase__								=	state.common.betas[t]
														lowerCamelCase__								=	(predicted_variance + 1) / 2
														lowerCamelCase__								=	frac * max_log + (1 - frac) * min_log

								return variance






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase = None ,_lowerCAmelCase = True ,):
								lowerCamelCase__								=	timestep

								if key is None:
														lowerCamelCase__								=	jax.random.PRNGKey(0							)

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
														lowerCamelCase__							,      lowerCamelCase__								=	jnp.split(_lowerCAmelCase ,sample.shape[1] ,axis=1							)
								else:
														lowerCamelCase__								=	None

								# 1. compute alphas, betas
								lowerCamelCase__								=	state.common.alphas_cumprod[t]
								lowerCamelCase__								=	jnp.where(t > 0 ,state.common.alphas_cumprod[t - 1] ,jnp.array(1.0 ,dtype=self.dtype							)							)
								lowerCamelCase__								=	1 - alpha_prod_t
								lowerCamelCase__								=	1 - alpha_prod_t_prev

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								if self.config.prediction_type == "epsilon":
														lowerCamelCase__								=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
														lowerCamelCase__								=	model_output
								elif self.config.prediction_type == "v_prediction":
														lowerCamelCase__								=	(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								else:
														raise ValueError(
														    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
														    """ for the FlaxDDPMScheduler."""							)

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
														lowerCamelCase__								=	jnp.clip(_lowerCAmelCase ,-1 ,1							)

								# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								lowerCamelCase__								=	(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								lowerCamelCase__								=	state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

								# 5. Compute predicted previous sample µ_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								lowerCamelCase__								=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								def random_variance():
														lowerCamelCase__								=	jax.random.split(_lowerCAmelCase ,num=1							)
														lowerCamelCase__								=	jax.random.normal(_lowerCAmelCase ,shape=model_output.shape ,dtype=self.dtype							)
														return (self._get_variance(_lowerCAmelCase ,_lowerCAmelCase ,predicted_variance=_lowerCAmelCase							) ** 0.5) * noise

								lowerCamelCase__								=	jnp.where(t > 0 ,random_variance() ,jnp.zeros(model_output.shape ,dtype=self.dtype							)							)

								lowerCamelCase__								=	pred_prev_sample + variance

								if not return_dict:
														return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=_lowerCAmelCase ,state=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
								return add_noise_common(state.common ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,):
								return get_velocity_common(state.common ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def __len__(   self							):
								return self.config.num_train_timesteps








'''simple docstring'''

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


a   	=    logging.get_logger(__name__)

def     __magic_name__							(				__UpperCAmelCase=None				,						__UpperCAmelCase=None   )    ->       Dict:

   '''simple docstring'''







   return field(default_factory=lambda: default				,						metadata=__UpperCAmelCase   )







@dataclass
class __a			:
   __UpperCamelCase :			List[str]						   =						list_field(
       default=[], metadata={
           'help': (
               'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
               ' of all available models'
           )
       }, )

   __UpperCamelCase :			List[int]						   =						list_field(
       default=[8], metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}      )

   __UpperCamelCase :			List[int]						   =						list_field(
       default=[8, 32, 128, 512], metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}, )

   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Use FP16 to accelerate inference.'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Benchmark training of model'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Verbose memory tracing'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}, )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
       }, )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Trace memory line by line'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Save result to a CSV file'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Save all print statements in a log file'}      )
   __UpperCamelCase :			bool						   =						field(default=_snake_case, metadata={'help': 'Whether to print environment information'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': (
               'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
               ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
               ' for debugging / testing and on TPU.'
           )
       }, )
   __UpperCamelCase :			str						   =						field(
       default=F'inference_time_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving time results to csv.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'inference_memory_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving memory results to csv.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'train_time_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving time results to csv for training.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'train_memory_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving memory results to csv for training.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'env_info_{round(time()      )}.csv', metadata={'help': 'CSV filename used if saving environment information.'}, )
   __UpperCamelCase :			str						   =						field(
       default=F'log_{round(time()      )}.csv', metadata={'help': 'Log filename used if print statements are saved in log.'}, )
   __UpperCamelCase :			int						   =						field(default=3, metadata={'help': 'Times an experiment will be run.'}      )
   __UpperCamelCase :			bool						   =						field(
       default=_snake_case, metadata={
           'help': (
               'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
               ' model weights.'
           )
       }, )





   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''simple docstring'''

      warnings.warn(
          f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
          """ are deprecated in general and it is advised to use external Benchmarking libraries """
          """ to benchmark Transformer models."""					,lowerCamelCase					,)





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      return json.dumps(dataclasses.asdict(self     )					,indent=2     )





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if len(self.models     ) <= 0:
         raise ValueError(
             """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
             """ bert-base-cased` or `args.models = ['bert-base-cased']."""     )
      return self.models





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      if not self.multi_process:
         return False
      elif self.is_tpu:
         logger.info("""Multiprocessing is currently not possible on TPU."""     )
         return False
      else:
         return True





