
"""simple docstring"""




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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A__ :       List[str]				= logging.get_logger(__name__)  # pylint: disable=invalid-name




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  warnings.warn(
      '''The preprocess method is deprecated and will be removed in a future version. Please'''
      ''' use VaeImageProcessor.preprocess instead'''			,					SCREAMING_SNAKE_CASE_			,					)
  if isinstance(SCREAMING_SNAKE_CASE_			,					torch.Tensor    ):
    return image
  elif isinstance(SCREAMING_SNAKE_CASE_			,					PIL.Image.Image    ):
    _lowercase:      Optional[int]   			=  [image]

  if isinstance(image[0]			,					PIL.Image.Image    ):
    _lowercase			,					_lowercase:      str   			=  image[0].size
    _lowercase			,					_lowercase:      Tuple   			=  (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

    _lowercase:      Dict   			=  [np.array(i.resize((w, h)			,					resample=PIL_INTERPOLATION['''lanczos''']    )    )[None, :] for i in image]
    _lowercase:      Optional[Any]   			=  np.concatenate(SCREAMING_SNAKE_CASE_			,					axis=0    )
    _lowercase:      Union[str, Any]   			=  np.array(SCREAMING_SNAKE_CASE_    ).astype(np.floataa    ) / 255.0
    _lowercase:      Tuple   			=  image.transpose(0			,					3			,					1			,					2    )
    _lowercase:      Optional[Any]   			=  2.0 * image - 1.0
    _lowercase:      str   			=  torch.from_numpy(SCREAMING_SNAKE_CASE_    )
  elif isinstance(image[0]			,					torch.Tensor    ):
    _lowercase:      int   			=  torch.cat(SCREAMING_SNAKE_CASE_			,					dim=0    )
  return image




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  if isinstance(SCREAMING_SNAKE_CASE_			,					torch.Tensor    ):
    return mask
  elif isinstance(SCREAMING_SNAKE_CASE_			,					PIL.Image.Image    ):
    _lowercase:      Tuple   			=  [mask]

  if isinstance(mask[0]			,					PIL.Image.Image    ):
    _lowercase			,					_lowercase:      Optional[Any]   			=  mask[0].size
    _lowercase			,					_lowercase:      int   			=  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    _lowercase:      str   			=  [np.array(m.convert('''L'''    ).resize((w, h)			,					resample=PIL_INTERPOLATION['''nearest''']    )    )[None, :] for m in mask]
    _lowercase:      List[str]   			=  np.concatenate(SCREAMING_SNAKE_CASE_			,					axis=0    )
    _lowercase:      str   			=  mask.astype(np.floataa    ) / 255.0
    _lowercase:      Optional[Any]   			=  0
    _lowercase:      int   			=  1
    _lowercase:      int   			=  torch.from_numpy(SCREAMING_SNAKE_CASE_    )
  elif isinstance(mask[0]			,					torch.Tensor    ):
    _lowercase:      Optional[Any]   			=  torch.cat(SCREAMING_SNAKE_CASE_			,					dim=0    )
  return mask







class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		42
       UpperCamelCase_ =		42
       def __init__(  self			,					A_			,					A_							)  ->						Dict:





         """simple docstring"""




         super().__init__()
         self.register_modules(unet=_lowerCAmelCase			,					scheduler=_lowerCAmelCase							)



       @torch.no_grad()
       def __call__(  self			,					A_			,					A_			,					A_ = 250			,					A_ = 0.0			,					A_ = 10			,					A_ = 10			,					A_ = None			,					A_ = "pil"			,					A_ = True			,					)  ->						Union[ImagePipelineOutput, Tuple]:





         """simple docstring"""




         _lowercase:      List[Any]   			=  image

         _lowercase:      Any   			=  _preprocess_image(_lowerCAmelCase							)
         _lowercase:      List[str]   			=  original_image.to(device=self.device			,					dtype=self.unet.dtype							)
         _lowercase:      Tuple   			=  _preprocess_mask(_lowerCAmelCase							)
         _lowercase:      Tuple   			=  mask_image.to(device=self.device			,					dtype=self.unet.dtype							)

         _lowercase:      Optional[Any]   			=  original_image.shape[0]

         # sample gaussian noise to begin the loop
         if isinstance(_lowerCAmelCase			,					_lowerCAmelCase							) and len(_lowerCAmelCase							) != batch_size:
           raise ValueError(
               f'''You have passed a list of generators of length {len(_lowerCAmelCase							)}, but requested an effective batch'''
               f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

         _lowercase:      Tuple   			=  original_image.shape
         _lowercase:      Optional[Any]   			=  randn_tensor(_lowerCAmelCase			,					generator=_lowerCAmelCase			,					device=self.device			,					dtype=self.unet.dtype							)

         # set step values
         self.scheduler.set_timesteps(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					self.device							)
         _lowercase:      int   			=  eta

         _lowercase:      Any   			=  self.scheduler.timesteps[0] + 1
         _lowercase:      Tuple   			=  generator[0] if isinstance(_lowerCAmelCase			,					_lowerCAmelCase							) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
           if t < t_last:
             # predict the noise residual
             _lowercase:      List[Any]   			=  self.unet(_lowerCAmelCase			,					_lowerCAmelCase							).sample
             # compute previous image: x_t -> x_t-1
             _lowercase:      Optional[int]   			=  self.scheduler.step(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase							).prev_sample

           else:
             # compute the reverse: x_t-1 -> x_t
             _lowercase:      str   			=  self.scheduler.undo_step(_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase							)
           _lowercase:      Optional[int]   			=  t

         _lowercase:      List[str]   			=  (image / 2 + 0.5).clamp(0			,					1							)
         _lowercase:      Any   			=  image.cpu().permute(0			,					2			,					3			,					1							).numpy()
         if output_type == "pil":
           _lowercase:      Any   			=  self.numpy_to_pil(_lowerCAmelCase							)

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase							)







'''simple docstring'''


import re
import string

import numpy as np

import datasets


_SCREAMING_SNAKE_CASE									=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

_SCREAMING_SNAKE_CASE									=				"\n"

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




 def 				_snake_case (						self		)     ->				List[str]:
     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"		),
             }		)      ,  reference_urls=[]      ,  )





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  )     ->				str:
     if regexes_to_ignore is not None:
         for s in regexes_to_ignore:
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in predictions]		)
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in references]		)
     else:
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)

     if ignore_case:
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)

     if ignore_punctuation:
         _lowerCAmelCase						      =   string.punctuation.maketrans(""      ,  ""      ,  string.punctuation		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     if ignore_numbers:
         _lowerCAmelCase						      =   string.digits.maketrans(""      ,  ""      ,  string.digits		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     _lowerCAmelCase						      =   predictions == references

     return {"exact_match": np.mean(_lowerCAmelCase		) * 100}


