


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


SCREAMING_SNAKE_CASE :   Optional[int]							      =				8



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=BITS				)					->		List[str]:
    _lowercase :							Optional[int]   	=	x.device

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

    _lowercase :							Union[str, Any]   	=	2 ** torch.arange(bits - 1							,				-1							,				-1							,				device=lowerCamelCase_				)
    _lowercase :							int   	=	rearrange(lowerCamelCase_							,				'd -> d 1 1'				)
    _lowercase :							List[str]   	=	rearrange(lowerCamelCase_							,				'b c h w -> b c 1 h w'				)

    _lowercase :							Tuple   	=	((x & mask) != 0).float()
    _lowercase :							Optional[int]   	=	rearrange(lowerCamelCase_							,				'b c d h w -> b (c d) h w'				)
    _lowercase :							Tuple   	=	bits * 2 - 1
    return bits



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=BITS				)					->		List[Any]:
    _lowercase :							Tuple   	=	x.device

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

    _lowercase :							Optional[Any]   	=	rearrange(lowerCamelCase_							,				'd -> d 1 1'				)
    _lowercase :							Optional[int]   	=	rearrange(lowerCamelCase_							,				'b (c d) h w -> b c d h w'							,				d=8				)
    _lowercase :							Optional[Any]   	=	reduce(x * mask							,				'b c d h w -> b c h w'							,				'sum'				)
    return (dec / 255).clamp(0.0							,				1.0				)



def        UpperCamelCase_(   self							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = 0.0							,				lowerCamelCase_ = True							,				lowerCamelCase_=None							,				lowerCamelCase_ = True							,				)					->		Union[DDIMSchedulerOutput, Tuple]:
    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 :							List[Any]   	=	timestep - self.config.num_train_timesteps // self.num_inference_steps

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

    _lowercase :							List[Any]   	=	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 :							List[str]   	=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

    # 4. Clip "predicted x_0"
    _lowercase :							Optional[Any]   	=	self.bit_scale
    if self.config.clip_sample:
        _lowercase :							List[str]   	=	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)
    _lowercase :							List[Any]   	=	self._get_variance(lowerCamelCase_							,				lowerCamelCase_				)
    _lowercase :							List[Any]   	=	eta * variance ** 0.5

    if use_clipped_model_output:
        # the model_output is always re-derived from the clipped x_0 in Glide
        _lowercase :							str   	=	(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 :							Optional[int]   	=	(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 :							Optional[int]   	=	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 :							List[str]   	=	model_output.device if torch.is_tensor(lowerCamelCase_				) else 'cpu'
        _lowercase :							Union[str, Any]   	=	torch.randn(model_output.shape							,				dtype=model_output.dtype							,				generator=lowerCamelCase_				).to(lowerCamelCase_				)
        _lowercase :							Tuple   	=	self._get_variance(lowerCamelCase_							,				lowerCamelCase_				) ** 0.5 * eta * noise

        _lowercase :							str   	=	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]:
    _lowercase :							Dict   	=	timestep

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

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

    # 3. Clip "predicted x_0"
    _lowercase :							int   	=	self.bit_scale
    if self.config.clip_sample:
        _lowercase :							Tuple   	=	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
    _lowercase :							Tuple   	=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
    _lowercase :							Tuple   	=	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 :							Tuple   	=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    _lowercase :							Dict   	=	0
    if t > 0:
        _lowercase :							List[Any]   	=	torch.randn(
            model_output.size()							,				dtype=model_output.dtype							,				layout=model_output.layout							,				generator=lowerCamelCase_				).to(model_output.device				)
        _lowercase :							int   	=	(self._get_variance(lowerCamelCase_							,				predicted_variance=lowerCamelCase_				) ** 0.5) * noise

    _lowercase :							str   	=	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,   ) ->     Any:




           """simple docstring"""

           super().__init__()
           _lowercase :							Dict   	=	bit_scale
           _lowercase :							Tuple   	=	(
               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 = 2_56,   lowerCamelCase = 2_56,   lowerCamelCase = 50,   lowerCamelCase = None,   lowerCamelCase = 1,   lowerCamelCase = "pil",   lowerCamelCase = True,   **lowerCamelCase,   ) ->     Union[Tuple, ImagePipelineOutput]:




           """simple docstring"""

           _lowercase :							List[Any]   	=	torch.randn(
               (batch_size, self.unet.config.in_channels, height, width),   generator=lowerCamelCase,   )
           _lowercase :							List[Any]   	=	decimal_to_bits(lowerCamelCase) * self.bit_scale
           _lowercase :							Optional[Any]   	=	latents.to(self.device)

           self.scheduler.set_timesteps(lowerCamelCase)

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

               # compute the previous noisy sample x_t -> x_t-1
               _lowercase :							Optional[Any]   	=	self.scheduler.step(lowerCamelCase,   lowerCamelCase,   lowerCamelCase).prev_sample

           _lowercase :							Tuple   	=	bits_to_decimal(lowerCamelCase)

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

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=lowerCamelCase)






from jiwer import compute_measures

import datasets


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

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe 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.\n\nThis 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.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           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     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


