






import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def     a     (		lowerCamelCase_						,  lowerCamelCase_=0.9_99						,  lowerCamelCase_="cosine"						,  ):






				'''simple docstring'''
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase_							):
												return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase_							):
												return math.exp(t * -12.0							)

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

				lowercase__  					=     []
				for i in range(lowerCamelCase_							):
								lowercase__  					=     i / num_diffusion_timesteps
								lowercase__  					=     (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase_							) / alpha_bar_fn(lowerCamelCase_							)						,  lowerCamelCase_							)							)
				return torch.tensor(lowerCamelCase_						,  dtype=torch.floataa							)




class  _UpperCAmelCase   (			A__						,A__      ):

				"""simple docstring"""




				lowercase__											=			[e.name for e in KarrasDiffusionSchedulers]
				lowercase__											=			2




				@register_to_config
				def __init__(	self		:       Optional[Any],    lowerCamelCase		:       int = 1_000,    lowerCamelCase		:       float = 0.00085,    lowerCamelCase		:       float = 0.012,    lowerCamelCase		:       str = "linear",    lowerCamelCase		:       Optional[Union[np.ndarray, List[float]]] = None,    lowerCamelCase		:       str = "epsilon",    lowerCamelCase		:       str = "linspace",    lowerCamelCase		:       int = 0,    ):

								'''simple docstring'''
								if trained_betas is not None:
												lowercase__  					=     torch.tensor(lowerCamelCase,    dtype=torch.floataa    )
								elif beta_schedule == "linear":
												lowercase__  					=     torch.linspace(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    dtype=torch.floataa    )
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												lowercase__  					=     (
												    torch.linspace(beta_start**0.5,    beta_end**0.5,    lowerCamelCase,    dtype=torch.floataa    ) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												lowercase__  					=     betas_for_alpha_bar(lowerCamelCase    )
								else:
												raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

								lowercase__  					=     1.0 - self.betas
								lowercase__  					=     torch.cumprod(self.alphas,    dim=0    )

								#  set all values
								self.set_timesteps(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       Tuple,    lowerCamelCase		:       Optional[int]=None    ):

								'''simple docstring'''
								if schedule_timesteps is None:
												lowercase__  					=     self.timesteps

								lowercase__  					=     (schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter    ) == 0:
												lowercase__  					=     1 if len(lowerCamelCase    ) > 1 else 0
								else:
												lowercase__  					=     timestep.cpu().item() if torch.is_tensor(lowerCamelCase    ) else timestep
												lowercase__  					=     self._index_counter[timestep_int]

								return indices[pos].item()




				@property
				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       Union[float, torch.FloatTensor],    ):

								'''simple docstring'''
								lowercase__  					=     self.index_for_timestep(lowerCamelCase    )

								if self.state_in_first_order:
												lowercase__  					=     self.sigmas[step_index]
								else:
												lowercase__  					=     self.sigmas_interpol[step_index]

								lowercase__  					=     sample / ((sigma**2 + 1) ** 0.5)
								return sample




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       int,    lowerCamelCase		:       Union[str, torch.device] = None,    lowerCamelCase		:       Optional[int] = None,    ):

								'''simple docstring'''
								lowercase__  					=     num_inference_steps

								lowercase__  					=     num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												lowercase__  					=     np.linspace(0,    num_train_timesteps - 1,    lowerCamelCase,    dtype=lowerCamelCase    )[::-1].copy()
								elif self.config.timestep_spacing == "leading":
												lowercase__  					=     num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												lowercase__  					=     (np.arange(0,    lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase    )
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												lowercase__  					=     num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												lowercase__  					=     (np.arange(lowerCamelCase,    0,    -step_ratio    )).round().copy().astype(lowerCamelCase    )
												timesteps -= 1
								else:
												raise ValueError(
												    F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

								lowercase__  					=     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
								lowercase__  					=     torch.from_numpy(np.log(lowerCamelCase    )    ).to(lowerCamelCase    )

								lowercase__  					=     np.interp(lowerCamelCase,    np.arange(0,    len(lowerCamelCase    )    ),    lowerCamelCase    )
								lowercase__  					=     np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
								lowercase__  					=     torch.from_numpy(lowerCamelCase    ).to(device=lowerCamelCase    )

								# interpolate sigmas
								lowercase__  					=     sigmas.log().lerp(sigmas.roll(1    ).log(),    0.5    ).exp()

								lowercase__  					=     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
								lowercase__  					=     torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

								if str(lowerCamelCase    ).startswith('''mps'''    ):
												# mps does not support float64
												lowercase__  					=     torch.from_numpy(lowerCamelCase    ).to(lowerCamelCase,    dtype=torch.floataa    )
								else:
												lowercase__  					=     torch.from_numpy(lowerCamelCase    ).to(lowerCamelCase    )

								# interpolate timesteps
								lowercase__  					=     self.sigma_to_t(lowerCamelCase    ).to(lowerCamelCase,    dtype=timesteps.dtype    )
								lowercase__  					=     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),    dim=-1    ).flatten()

								lowercase__  					=     torch.cat([timesteps[:1], interleaved_timesteps]    )

								lowercase__  					=     None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								lowercase__  					=     defaultdict(lowerCamelCase    )




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								# get log sigma
								lowercase__  					=     sigma.log()

								# get distribution
								lowercase__  					=     log_sigma - self.log_sigmas[:, None]

								# get sigmas range
								lowercase__  					=     dists.ge(0    ).cumsum(dim=0    ).argmax(dim=0    ).clamp(max=self.log_sigmas.shape[0] - 2    )
								lowercase__  					=     low_idx + 1

								lowercase__  					=     self.log_sigmas[low_idx]
								lowercase__  					=     self.log_sigmas[high_idx]

								# interpolate sigmas
								lowercase__  					=     (low - log_sigma) / (low - high)
								lowercase__  					=     w.clamp(0,    1    )

								# transform interpolation to time range
								lowercase__  					=     (1 - w) * low_idx + w * high_idx
								lowercase__  					=     t.view(sigma.shape    )
								return t




				@property
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								return self.sample is None




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Union[torch.FloatTensor, np.ndarray],    lowerCamelCase		:       Union[float, torch.FloatTensor],    lowerCamelCase		:       Union[torch.FloatTensor, np.ndarray],    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								lowercase__  					=     self.index_for_timestep(lowerCamelCase    )

								# advance index counter by 1
								lowercase__  					=     timestep.cpu().item() if torch.is_tensor(lowerCamelCase    ) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
												lowercase__  					=     self.sigmas[step_index]
												lowercase__  					=     self.sigmas_interpol[step_index + 1]
												lowercase__  					=     self.sigmas[step_index + 1]
								else:
												# 2nd order / KDPM2's method
												lowercase__  					=     self.sigmas[step_index - 1]
												lowercase__  					=     self.sigmas_interpol[step_index]
												lowercase__  					=     self.sigmas[step_index]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								lowercase__  					=     0
								lowercase__  					=     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												lowercase__  					=     sigma_hat if self.state_in_first_order else sigma_interpol
												lowercase__  					=     sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
												lowercase__  					=     sigma_hat if self.state_in_first_order else sigma_interpol
												lowercase__  					=     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
												    sample / (sigma_input**2 + 1)
												)
								elif self.config.prediction_type == "sample":
												raise NotImplementedError('''prediction_type not implemented yet: sample'''    )
								else:
												raise ValueError(
												    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

								if self.state_in_first_order:
												# 2. Convert to an ODE derivative for 1st order
												lowercase__  					=     (sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												lowercase__  					=     sigma_interpol - sigma_hat

												# store for 2nd order step
												lowercase__  					=     sample
								else:
												# DPM-Solver-2
												# 2. Convert to an ODE derivative for 2nd order
												lowercase__  					=     (sample - pred_original_sample) / sigma_interpol

												# 3. delta timestep
												lowercase__  					=     sigma_next - sigma_hat

												lowercase__  					=     self.sample
												lowercase__  					=     None

								lowercase__  					=     sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=lowerCamelCase    )




				def 							lowercase__  (	self		:       List[Any],    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       torch.FloatTensor,    lowerCamelCase		:       torch.FloatTensor,    ):

								'''simple docstring'''
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowercase__  					=     self.sigmas.to(device=original_samples.device,    dtype=original_samples.dtype    )
								if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase    ):
												# mps does not support float64
												lowercase__  					=     self.timesteps.to(original_samples.device,    dtype=torch.floataa    )
												lowercase__  					=     timesteps.to(original_samples.device,    dtype=torch.floataa    )
								else:
												lowercase__  					=     self.timesteps.to(original_samples.device    )
												lowercase__  					=     timesteps.to(original_samples.device    )

								lowercase__  					=     [self.index_for_timestep(lowerCamelCase,    lowerCamelCase    ) for t in timesteps]

								lowercase__  					=     sigmas[step_indices].flatten()
								while len(sigma.shape    ) < len(original_samples.shape    ):
												lowercase__  					=     sigma.unsqueeze(-1    )

								lowercase__  					=     original_samples + noise * sigma
								return noisy_samples




				def __len__(	self		:       str    ):

								'''simple docstring'''
								return self.config.num_train_timesteps












from jiwer import compute_measures

import datasets


A__      :     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'

A__      :     Optional[int]       =					'\\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'

A__      :     Dict       =					'\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  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, 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 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




