



'''simple docstring'''





from jiwer import compute_measures

import datasets


_UpperCamelCase											=		'\\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'

_UpperCamelCase											=		'\\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'

_UpperCamelCase											=		'\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	):
  """simple docstring"""




  def 		_lowercase	(					self :      str  )   ->   int:
    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 :      Tuple     ,      _a :      int=None     ,      _a :      List[str]=None     ,      _a :      Any=False  )   ->   Dict:
    if concatenate_texts:
      return compute_measures(_a     ,      _a  )["wer"]
    else:
      __lowerCamelCase			:			Optional[Any]       			=					0
      __lowerCamelCase			:			List[str]       			=					0
      for prediction, reference in zip(_a     ,      _a  ):
        __lowerCamelCase			:			List[str]       			=					compute_measures(_a     ,      _a  )
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]
      return incorrect / total











'''simple docstring'''





from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_UpperCamelCase											=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
          f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
          'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
          ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
          ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
          ' file'
      )
      deprecate('steps_offset!=1'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          f'The configuration file of this scheduler: {scheduler} has not set the configuration'
          ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
          ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
          ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
          ' Hub, it would be very nice if you could open a Pull request for the'
          ' `scheduler/scheduler_config.json` file'
      )
      deprecate('skip_prk_steps not set'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    if safety_checker is None:
      logger.warning(
          f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
          ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
          ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
          ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
          ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
          ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'  )

    self.register_modules(
        segmentation_model=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'  )

    __lowerCamelCase			:			Dict       			=					torch.device('cuda'  )

    for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
      if cpu_offloaded_model is not None:
        cpu_offload(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_hf_hook'  )
          and hasattr(module._hf_hook     ,      'execution_device'  )
          and module._hf_hook.execution_device is not None
      ):
        return torch.device(module._hf_hook.execution_device  )
    return self.device




  @torch.no_grad()
  def __call__(					self :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					StableDiffusionInpaintPipeline(
        vae=self.vae     ,      text_encoder=self.text_encoder     ,      tokenizer=self.tokenizer     ,      unet=self.unet     ,      scheduler=self.scheduler     ,      safety_checker=self.safety_checker     ,      feature_extractor=self.feature_extractor     ,      )
    return inpainting_pipeline(
        prompt=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






