

from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase_   							=  8
def 	a (     A__      :  Any ,  A__      :  str=BITS	)       -> Optional[int]:





      """simple docstring"""



      _lowercase      			=x.device

      _lowercase      			=(x * 255).int().clamp(0 ,  255	)

      _lowercase      			=2 ** torch.arange(bits - 1 ,  -1 ,  -1 ,  device=A__	)
      _lowercase      			=rearrange(A__ ,  'd -> d 1 1'	)
      _lowercase      			=rearrange(A__ ,  'b c h w -> b c 1 h w'	)

      _lowercase      			=((x & mask) != 0).float()
      _lowercase      			=rearrange(A__ ,  'b c d h w -> b (c d) h w'	)
      _lowercase      			=bits * 2 - 1
      return bits
def 	a (     A__      :  List[Any] ,  A__      :  List[str]=BITS	)       -> Optional[int]:





      """simple docstring"""



      _lowercase      			=x.device

      _lowercase      			=(x > 0).int()
      _lowercase      			=2 ** torch.arange(bits - 1 ,  -1 ,  -1 ,  device=A__ ,  dtype=torch.intaa	)

      _lowercase      			=rearrange(A__ ,  'd -> d 1 1'	)
      _lowercase      			=rearrange(A__ ,  'b (c d) h w -> b c d h w' ,  d=8	)
      _lowercase      			=reduce(x * mask ,  'b c d h w -> b c h w' ,  'sum'	)
      return (dec / 255).clamp(0.0 ,  1.0	)
def 	a (     self      :  Union[str, Any] ,  A__      :  torch.FloatTensor ,  A__      :  int ,  A__      :  torch.FloatTensor ,  A__      :  float = 0.0 ,  A__      :  bool = True ,  A__      :  Optional[int]=None ,  A__      :  bool = True ,  )       -> Union[DDIMSchedulerOutput, Tuple]:





      """simple docstring"""



      if self.num_inference_steps is None:
            raise ValueError(
                'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'	)

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      _lowercase      			=timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      _lowercase      			=self.alphas_cumprod[timestep]
      _lowercase      			=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      _lowercase      			=1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _lowercase      			=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      _lowercase      			=self.bit_scale
      if self.config.clip_sample:
            _lowercase      			=torch.clamp(A__ ,  -scale ,  A__	)

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      _lowercase      			=self._get_variance(A__ ,  A__	)
      _lowercase      			=eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            _lowercase      			=(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _lowercase      			=(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _lowercase      			=alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            _lowercase      			=model_output.device if torch.is_tensor(A__	) else 'cpu'
            _lowercase      			=torch.randn(model_output.shape ,  dtype=model_output.dtype ,  generator=A__	).to(A__	)
            _lowercase      			=self._get_variance(A__ ,  A__	) ** 0.5 * eta * noise

            _lowercase      			=prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=A__ ,  pred_original_sample=A__	)
def 	a (     self      :  List[Any] ,  A__      :  torch.FloatTensor ,  A__      :  int ,  A__      :  torch.FloatTensor ,  A__      :  List[Any]="epsilon" ,  A__      :  Tuple=None ,  A__      :  bool = True ,  )       -> Union[DDPMSchedulerOutput, Tuple]:





      """simple docstring"""



      _lowercase      			=timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            _lowercase			,			_lowercase      			=torch.split(A__ ,  sample.shape[1] ,  dim=1	)
      else:
            _lowercase      			=None

      # 1. compute alphas, betas
      _lowercase      			=self.alphas_cumprod[t]
      _lowercase      			=self.alphas_cumprod[t - 1] if t > 0 else self.one
      _lowercase      			=1 - alpha_prod_t
      _lowercase      			=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 prediction_type == "epsilon":
            _lowercase      			=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            _lowercase      			=model_output
      else:
            raise ValueError(F'''Unsupported prediction_type {prediction_type}.'''	)

      # 3. Clip "predicted x_0"
      _lowercase      			=self.bit_scale
      if self.config.clip_sample:
            _lowercase      			=torch.clamp(A__ ,  -scale ,  A__	)

      # 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
      _lowercase      			=(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      _lowercase      			=self.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
      _lowercase      			=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      _lowercase      			=0
      if t > 0:
            _lowercase      			=torch.randn(
                model_output.size() ,  dtype=model_output.dtype ,  layout=model_output.layout ,  generator=A__	).to(model_output.device	)
            _lowercase      			=(self._get_variance(A__ ,  predicted_variance=A__	) ** 0.5) * noise

      _lowercase      			=pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=A__ ,  pred_original_sample=A__	)







class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 1.0				,  )    ->					Union[str, Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=bit_scale
        _lowercase      			=(
            ddim_bit_scheduler_step if isinstance(lowerCAmelCase				,  lowerCAmelCase   ) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=lowerCAmelCase				,  scheduler=lowerCAmelCase   )




  @torch.no_grad()
  def __call__(							self				,  lowerCAmelCase = 256				,  lowerCAmelCase = 256				,  lowerCAmelCase = 50				,  lowerCAmelCase = None				,  lowerCAmelCase = 1				,  lowerCAmelCase = "pil"				,  lowerCAmelCase = True				,  **lowerCAmelCase				,  )    ->					Union[Tuple, ImagePipelineOutput]:
        '''simple docstring'''






        _lowercase      			=torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)				,  generator=lowerCAmelCase				,  )
        _lowercase      			=decimal_to_bits(lowerCAmelCase   ) * self.bit_scale
        _lowercase      			=latents.to(self.device   )

        self.scheduler.set_timesteps(lowerCAmelCase   )

        for t in self.progress_bar(self.scheduler.timesteps   ):
              # predict the noise residual
              _lowercase      			=self.unet(lowerCAmelCase				,  lowerCAmelCase   ).sample

              # compute the previous noisy sample x_t -> x_t-1
              _lowercase      			=self.scheduler.step(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   ).prev_sample

        _lowercase      			=bits_to_decimal(lowerCAmelCase   )

        if output_type == "pil":
              _lowercase      			=self.numpy_to_pil(lowerCAmelCase   )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase   )





import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase_   							=  5_0_0_0_0_0

lowercase_							,		lowercase_   							=  os.path.split(__file__)
lowercase_   							=  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[str]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=dataset.map(**A__	)
@get_duration
def 	a (     A__      :  datasets.Dataset ,  **A__      :  List[Any]	)       -> List[str]:





      """simple docstring"""



      _lowercase      			=dataset.filter(**A__	)
def 	a (     )       -> Union[str, Any]:





      """simple docstring"""



      _lowercase      			={'num examples': SPEED_TEST_N_EXAMPLES}
      with tempfile.TemporaryDirectory() as tmp_dir:
            _lowercase      			=datasets.Features({'text': datasets.Value('string'	), 'numbers': datasets.Value('float32'	)}	)
            _lowercase      			=generate_example_dataset(
                os.path.join(A__ ,  'dataset.arrow'	) ,  A__ ,  num_examples=A__	)

            _lowercase      			=transformers.AutoTokenizer.from_pretrained('bert-base-cased' ,  use_fast=A__	)

            def tokenize(A__      :  int	):
                  return tokenizer(examples['text']	)

            _lowercase      			=map(A__	)

            _lowercase      			=map(A__ ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='numpy'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='pandas'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='torch' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            with dataset.formatted_as(type='tensorflow' ,  columns='numbers'	):
                  _lowercase      			=map(A__ ,  function=lambda A__	: None ,  batched=A__	)

            _lowercase      			=map(A__ ,  function=A__ ,  batched=A__	)

            _lowercase      			=filter(A__	)

            # Activate later when tokenizer support batched inputs
            # with dataset.formatted_as(type='numpy'):
            #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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


