



'''simple docstring'''





import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__a          =		5_0000
__a          =		5000

__a       ,						__a          =		os.path.split(__file__)
__a          =		os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[Any]:
      for i in range(_lowerCAmelCase		):
            snake_case__  :    str    						=      dataset[i]





@get_duration
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      for i in range(0     ,   len(_lowerCAmelCase		)     ,   _lowerCAmelCase		):
            snake_case__  :    Optional[Any]    						=      dataset[i : i + batch_size]





@get_duration
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      with dataset.formatted_as(type=_lowerCAmelCase		):
            for i in range(_lowerCAmelCase		):
                  snake_case__  :    Tuple    						=      dataset[i]





@get_duration
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      with dataset.formatted_as(type=_lowerCAmelCase		):
            for i in range(0     ,   _lowerCAmelCase     ,   _lowerCAmelCase		):
                  snake_case__  :    Tuple    						=      dataset[i : i + batch_size]





def 						__snake_case(				)		-> str:
      snake_case__  :    List[str]    						=      {"""num examples""": SPEED_TEST_N_EXAMPLES}
      snake_case__  :    str    						=      [
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_000}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
          (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_000}),
      ]

      snake_case__  :    Union[str, Any]    						=      [
          (read, {"""length""": SMALL_TEST}),
          (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
          (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1_000}),
          (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
          (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1_000}),
      ]
      with tempfile.TemporaryDirectory() as tmp_dir:
            print("""generating dataset"""		)
            snake_case__  :    Tuple    						=      datasets.Features(
                {"""list""": datasets.Sequence(datasets.Value("""float32"""		)		), """numbers""": datasets.Value("""float32"""		)}		)
            snake_case__  :    Optional[int]    						=      generate_example_dataset(
                os.path.join(_lowerCAmelCase     ,   """dataset.arrow"""		)     ,   _lowerCAmelCase     ,   num_examples=_lowerCAmelCase     ,   seq_shapes={"""list""": (100,)}     ,   )
            print("""first set of iterations"""		)
            for func, kwargs in functions:
                  print(func.__name__     ,   str(_lowerCAmelCase		)		)
                  snake_case__  :    int    						=      func(_lowerCAmelCase     ,   **_lowerCAmelCase		)

            print("""shuffling dataset"""		)
            snake_case__  :    Optional[Any]    						=      dataset.shuffle()
            print("""Second set of iterations (after shuffling"""		)
            for func, kwargs in functions_shuffled:
                  print("""shuffled """     ,   func.__name__     ,   str(_lowerCAmelCase		)		)
                  snake_case__  :    Dict    						=      func(
                      _lowerCAmelCase     ,   **_lowerCAmelCase		)

      with open(_lowerCAmelCase     ,   """wb"""		) as f:
            f.write(json.dumps(_lowerCAmelCase		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
       benchmark_iterating()





'''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 __A			:



 '''simple docstring'''







 __lowerCamelCase    :       CommonSchedulerState

 # setable values
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       jnp.ndarray
 __lowerCamelCase    :       Optional[int]          =       None



 @classmethod
 def 					a__  (cls ,   A ,   A ,   A       )    ->     str:


      """simple docstring"""



      return cls(common=A ,   init_noise_sigma=A ,   timesteps=A       )






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       DDPMSchedulerState






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Dict          =       [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowerCamelCase    :       jnp.dtype



 @property
 def 					a__  (self       )    ->     List[str]:


      """simple docstring"""



      return True



 @register_to_config
 def __init__(self ,   A = 1_000 ,   A = 0.0001 ,   A = 0.02 ,   A = "linear" ,   A = None ,   A = "fixed_small" ,   A = True ,   A = "epsilon" ,   A = jnp.floataa ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  dtype



 def 					a__  (self ,   A = None       )    ->     DDPMSchedulerState:


      """simple docstring"""



      if common is None:
           _a      =  CommonSchedulerState.create(self       )

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

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

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



 def 					a__  (self ,   A ,   A ,   A = None       )    ->     jnp.ndarray:


      """simple docstring"""



      return sample



 def 					a__  (self ,   A ,   A ,   A = ()       )    ->     DDPMSchedulerState:


      """simple docstring"""



      _a      =  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
      _a      =  (jnp.arange(0 ,   A       ) * step_ratio).round()[::-1]

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



 def 					a__  (self ,   A ,   A ,   A=None ,   A=None       )    ->     int:


      """simple docstring"""



      _a      =  state.common.alphas_cumprod[t]
      _a      =  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
      _a      =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

      return variance



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A = None ,   A = True ,   )    ->     Union[FlaxDDPMSchedulerOutput, Tuple]:


      """simple docstring"""



      _a      =  timestep

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

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

      # 1. compute alphas, betas
      _a      =  state.common.alphas_cumprod[t]
      _a      =  jnp.where(t > 0 ,   state.common.alphas_cumprod[t - 1] ,   jnp.array(1.0 ,   dtype=self.dtype       )       )
      _a      =  1 - alpha_prod_t
      _a      =  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":
           _a      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
           _a      =  model_output
      elif self.config.prediction_type == "v_prediction":
           _a      =  (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:
           _a      =  jnp.clip(A ,   -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
      _a      =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
      _a      =  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
      _a      =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

      _a      =  pred_prev_sample + variance

      if not return_dict:
           return (pred_prev_sample, state)

      return FlaxDDPMSchedulerOutput(prev_sample=A ,   state=A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return add_noise_common(state.common ,   A ,   A ,   A       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   )    ->     jnp.ndarray:


      """simple docstring"""



      return get_velocity_common(state.common ,   A ,   A ,   A       )



 def __len__(self       )    ->     Tuple:


      """simple docstring"""



      return self.config.num_train_timesteps



