


"""simple docstring"""







import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase					:					Any								=							image.size
  lowerCAmelCase						,  lowerCAmelCase					:					str								=							(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
  lowerCAmelCase					:					int								=							image.resize((w, h)    ,				resample=PIL_INTERPOLATION["lanczos"]    )
  lowerCAmelCase					:					str								=							np.array(SCREAMING_SNAKE_CASE    ).astype(np.floataa    ) / 255.0
  lowerCAmelCase					:					Any								=							image[None].transpose(0    ,				3    ,				1    ,				2    )
  lowerCAmelCase					:					List[Any]								=							torch.from_numpy(SCREAMING_SNAKE_CASE    )
  return 2.0 * image - 1.0





class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					):






         """simple docstring"""





         super().__init__()
         self.register_modules(vqvae=snake_case__		,					unet=snake_case__		,					scheduler=snake_case__						)







       @torch.no_grad()
       def __call__(						self		,					snake_case__ = None		,					snake_case__ = 1		,					snake_case__ = 100		,					snake_case__ = 0.0		,					snake_case__ = None		,					snake_case__ = "pil"		,					snake_case__ = True		,					):






         """simple docstring"""





         if isinstance(snake_case__		,					PIL.Image.Image						):
           lowerCAmelCase					:					int								=							1
         elif isinstance(snake_case__		,					torch.Tensor						):
           lowerCAmelCase					:					Optional[int]								=							image.shape[0]
         else:
           raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case__						)}"""						)

         if isinstance(snake_case__		,					PIL.Image.Image						):
           lowerCAmelCase					:					Optional[int]								=							preprocess(snake_case__						)

         lowerCAmelCase						,  lowerCAmelCase					:					List[str]								=							image.shape[-2:]

         # in_channels should be 6: 3 for latents, 3 for low resolution image
         lowerCAmelCase					:					Any								=							(batch_size, self.unet.config.in_channels // 2, height, width)
         lowerCAmelCase					:					Union[str, Any]								=							next(self.unet.parameters()						).dtype

         lowerCAmelCase					:					Dict								=							randn_tensor(snake_case__		,					generator=snake_case__		,					device=self.device		,					dtype=snake_case__						)

         lowerCAmelCase					:					Dict								=							image.to(device=self.device		,					dtype=snake_case__						)

         # set timesteps and move to the correct device
         self.scheduler.set_timesteps(snake_case__		,					device=self.device						)
         lowerCAmelCase					:					Tuple								=							self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         lowerCAmelCase					:					Union[str, Any]								=							latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         lowerCAmelCase					:					Optional[int]								=							"eta" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
         lowerCAmelCase					:					str								=							{}
         if accepts_eta:
           lowerCAmelCase					:					Tuple								=							eta

         for t in self.progress_bar(snake_case__						):
           # concat latents and low resolution image in the channel dimension.
           lowerCAmelCase					:					List[Any]								=							torch.cat([latents, image]		,					dim=1						)
           lowerCAmelCase					:					int								=							self.scheduler.scale_model_input(snake_case__		,					snake_case__						)
           # predict the noise residual
           lowerCAmelCase					:					Any								=							self.unet(snake_case__		,					snake_case__						).sample
           # compute the previous noisy sample x_t -> x_t-1
           lowerCAmelCase					:					Any								=							self.scheduler.step(snake_case__		,					snake_case__		,					snake_case__		,					**snake_case__						).prev_sample

         # decode the image latents with the VQVAE
         lowerCAmelCase					:					int								=							self.vqvae.decode(snake_case__						).sample
         lowerCAmelCase					:					int								=							torch.clamp(snake_case__		,					-1.0		,					1.0						)
         lowerCAmelCase					:					Union[str, Any]								=							image / 2 + 0.5
         lowerCAmelCase					:					int								=							image.cpu().permute(0		,					2		,					3		,					1						).numpy()

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

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=snake_case__						)









"""simple docstring"""







from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
     import importlib_metadata
else:
     import importlib.metadata as importlib_metadata


lowerCAmelCase__				    =       ''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):


     class 				SCREAMING_SNAKE_CASE__    (		tr.AbstractTransform    ):
            """simple docstring"""


            def __init__(						self		,					snake_case__ = " "						):






              """simple docstring"""





              lowerCAmelCase					:					List[Any]								=							sentence_delimiter

            def 	lowercase__							(						self		,					snake_case__						):






              """simple docstring"""





              return list(snake_case__						)







            def 	lowercase__							(						self		,					snake_case__						):






              """simple docstring"""





              lowerCAmelCase					:					Any								=							[]
              for sent_idx, sentence in enumerate(snake_case__						):
                chars.extend(self.process_string(snake_case__						)						)
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(snake_case__						) - 1:
                  chars.append(self.sentence_delimiter						)
              return chars

     lowerCAmelCase__				    =       tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     lowerCAmelCase__				    =       tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


lowerCAmelCase__				    =       '''\
@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.}
}
'''

lowerCAmelCase__				    =       '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (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 characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

lowerCAmelCase__				    =       '''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__    (		datasets.Metric    ):
       """simple docstring"""


       def 	lowercase__							(						self						):






         """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",
                 "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
             ]		,					)







       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__=False						):






         """simple docstring"""





         if concatenate_texts:
           return jiwer.compute_measures(
               snake_case__		,					snake_case__		,					truth_transform=snake_case__		,					hypothesis_transform=snake_case__		,					)["wer"]

         lowerCAmelCase					:					List[Any]								=							0
         lowerCAmelCase					:					Optional[Any]								=							0
         for prediction, reference in zip(snake_case__		,					snake_case__						):
           lowerCAmelCase					:					Optional[int]								=							jiwer.compute_measures(
               snake_case__		,					snake_case__		,					truth_transform=snake_case__		,					hypothesis_transform=snake_case__		,					)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

         return incorrect / total





