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


UpperCAmelCase_								=							8

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=BITS   )		->						List[str]:




       '''simple docstring'''






       _A=	x.device

       _A=	(x * 2_55).int().clamp(0       ,					2_55   )

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

       _A=	((x & mask) != 0).float()
       _A=	rearrange(lowerCAmelCase_       ,					'b c d h w -> b (c d) h w'   )
       _A=	bits * 2 - 1
       return bits

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=BITS   )		->						int:




       '''simple docstring'''






       _A=	x.device

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

       _A=	rearrange(lowerCAmelCase_       ,					'd -> d 1 1'   )
       _A=	rearrange(lowerCAmelCase_       ,					'b (c d) h w -> b c d h w'       ,					d=8   )
       _A=	reduce(x * mask       ,					'b c d h w -> b c h w'       ,					'sum'   )
       return (dec / 2_55).clamp(0.0       ,					1.0   )

def 							UpperCamelCase				(    self       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_ = 0.0       ,					lowerCAmelCase_ = True       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_ = 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)
       _A=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

       _A=	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
       _A=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

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

       # 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)
       _A=	self._get_variance(lowerCAmelCase_       ,					lowerCAmelCase_   )
       _A=	eta * variance ** 0.5

       if use_clipped_model_output:
              # the model_output is always re-derived from the clipped x_0 in Glide
              _A=	(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
       _A=	(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
       _A=	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
              _A=	model_output.device if torch.is_tensor(lowerCAmelCase_   ) else 'cpu'
              _A=	torch.randn(model_output.shape       ,					dtype=model_output.dtype       ,					generator=lowerCAmelCase_   ).to(lowerCAmelCase_   )
              _A=	self._get_variance(lowerCAmelCase_       ,					lowerCAmelCase_   ) ** 0.5 * eta * noise

              _A=	prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=lowerCAmelCase_       ,					pred_original_sample=lowerCAmelCase_   )

def 							UpperCamelCase				(    self       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_="epsilon"       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_ = True       ,					)		->						Union[DDPMSchedulerOutput, Tuple]:




       '''simple docstring'''






       _A=	timestep

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

       # 1. compute alphas, betas
       _A=	self.alphas_cumprod[t]
       _A=	self.alphas_cumprod[t - 1] if t > 0 else self.one
       _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 prediction_type == "epsilon":
              _A=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif prediction_type == "sample":
              _A=	model_output
       else:
              raise ValueError(F"Unsupported prediction_type {prediction_type}."   )

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

       # 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 * self.betas[t]) / beta_prod_t
       _A=	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
       _A=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

       _A=	pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=lowerCAmelCase_       ,					pred_original_sample=lowerCAmelCase_   )





class      lowerCAmelCase						(					_a			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__				,      lowerCAmelCase__ = 1.0				,      ):
            super().__init__()
            _A=	bit_scale
            _A=	(
                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__				,      ):
            _A=	torch.randn(
                (batch_size, self.unet.config.in_channels, height, width)				,      generator=lowerCAmelCase__				,      )
            _A=	decimal_to_bits(lowerCAmelCase__       ) * self.bit_scale
            _A=	latents.to(self.device       )

            self.scheduler.set_timesteps(lowerCAmelCase__       )

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

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

            _A=	bits_to_decimal(lowerCAmelCase__       )

            if output_type == "pil":
                   _A=	self.numpy_to_pil(lowerCAmelCase__       )

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=lowerCAmelCase__       )
from jiwer import compute_measures

import datasets


UpperCAmelCase_								=							'''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

UpperCAmelCase_								=							'''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.

The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.

This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.

Word error rate can then be computed as:

WER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).

This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''

UpperCAmelCase_								=							'''
Compute WER score of transcribed segments against references.

Args:
    references: List of references for each speech input.
    predictions: List of transcriptions to score.
    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.

Returns:
    (float): the word error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> wer = datasets.load_metric("wer")
    >>> wer_score = wer.compute(predictions=predictions, references=references)
    >>> print(wer_score)
    0.5
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,		_KWARGS_DESCRIPTION			)
class      lowerCAmelCase						(					datasets.Metric			):



     def 			a__  (					self       ):
            return datasets.MetricInfo(
                description=_DESCRIPTION				,      citation=_CITATION				,      inputs_description=_KWARGS_DESCRIPTION				,      features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'				,      id='sequence'       ),
                        'references': datasets.Value('string'				,      id='sequence'       ),
                    }       )				,      codebase_urls=['https://github.com/jitsi/jiwer/']				,      reference_urls=[
                    'https://en.wikipedia.org/wiki/Word_error_rate',
                ]				,      )





     def 			a__  (					self				,      lowerCAmelCase__=None				,      lowerCAmelCase__=None				,      lowerCAmelCase__=False       ):
            if concatenate_texts:
                   return compute_measures(lowerCAmelCase__				,      lowerCAmelCase__       )["wer"]
            else:
                   _A=	0
                   _A=	0
                   for prediction, reference in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                          _A=	compute_measures(lowerCAmelCase__				,      lowerCAmelCase__       )
                          incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                          total += measures["substitutions"] + measures["deletions"] + measures["hits"]
                   return incorrect / total