




import argparse
import json
import re
import string
import sys

import numpy as np


__SCREAMING_SNAKE_CASE			:      Any      			=0.5

def 	lowerCAmelCase_	(     lowerCamelCase:				List[str]   , lowerCamelCase:				Tuple  )    ->	List[str]:


      '''simple docstring'''




      lowerCamelCase__					     = ['.', ',', ';', ':']

      for token in remove_tokens:
            lowerCamelCase__					     = ground_truth.replace(lowerCamelCase   , ''  )
            lowerCamelCase__					     = prediction.replace(lowerCamelCase   , ''  )

      lowerCamelCase__     ,						lowerCamelCase__					     = ground_truth.lower(), prediction.lower()
      lowerCamelCase__     ,						lowerCamelCase__					     = ground_truth.replace('/'   , ' '  ), prediction.replace('/'   , ' '  )
      lowerCamelCase__     ,						lowerCamelCase__					     = set(ground_truth.split(' '  )  ), set(prediction.split(' '  )  )

      lowerCamelCase__					     = ground_truth.intersection(lowerCamelCase  )
      lowerCamelCase__					     = ground_truth.union(lowerCamelCase  )
      lowerCamelCase__					     = len(lowerCamelCase  ) / len(lowerCamelCase  )
      return jaccard

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict  )    ->	List[Any]:


      '''simple docstring'''




      def remove_articles(lowerCamelCase:				Union[str, Any]  ):
            return re.sub(r'\b(a|an|the)\b'   , ' '   , lowerCamelCase  )

      def white_space_fix(lowerCamelCase:				Optional[Any]  ):
            return " ".join(text.split()  )

      def remove_punc(lowerCamelCase:				Dict  ):
            lowerCamelCase__					     = set(string.punctuation  )
            return "".join(ch for ch in text if ch not in exclude  )

      def lower(lowerCamelCase:				List[Any]  ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase  )  )  )  )

def 	lowerCAmelCase_	(     lowerCamelCase:				List[str]   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				Optional[Any]  )    ->	Tuple:


      '''simple docstring'''




      lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = 0, 0, 0

      lowerCamelCase__					     = 'Parties' in qa_id

      # first check if ground truth is empty
      if len(lowerCamelCase  ) == 0:
            if len(lowerCamelCase  ) > 0:
                  fp += len(lowerCamelCase  )  # false positive for each one
      else:
            for ground_truth in ground_truths:
                  assert len(lowerCamelCase  ) > 0
                  # check if there is a match
                  lowerCamelCase__					     = False
                  for pred in predictions:
                        if substr_ok:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
                        else:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH
                        if is_match:
                              lowerCamelCase__					     = True

                  if match_found:
                        tp += 1
                  else:
                        fn += 1

        # now also get any fps by looping through preds
            for pred in predictions:
                  # Check if there's a match. if so, don't count (don't want to double count based on the above)
                  # but if there's no match, then this is a false positive.
                  # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
                  # multiple predictions that are matched with the same answer.)
                  lowerCamelCase__					     = False
                  for ground_truth in ground_truths:
                        assert len(lowerCamelCase  ) > 0
                        if substr_ok:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
                        else:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH
                        if is_match:
                              lowerCamelCase__					     = True

                  if not match_found:
                        fp += 1

      lowerCamelCase__					     = tp / (tp + fp) if tp + fp > 0 else np.nan
      lowerCamelCase__					     = tp / (tp + fn) if tp + fn > 0 else np.nan

      return precision, recall

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict  )    ->	Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__					     = precisions[::-1]
      for i in range(1   , len(lowerCamelCase  )  ):
            lowerCamelCase__					     = max(precision_best[i - 1]   , precision_best[i]  )
      lowerCamelCase__					     = precision_best[::-1]
      return precisions

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[Any]   , lowerCamelCase:				Any  )    ->	List[Any]:


      '''simple docstring'''




      lowerCamelCase__					     = process_precisions(lowerCamelCase  )
      lowerCamelCase__					     = np.trapz(lowerCamelCase   , lowerCamelCase  )
      if np.isnan(lowerCamelCase  ):
            return 0
      return aupr

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[Any]   , lowerCamelCase:				int   , lowerCamelCase:				str  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = process_precisions(lowerCamelCase  )
      lowerCamelCase__					     = 0
      for prec, recall in zip(lowerCamelCase   , lowerCamelCase  ):
            if recall >= recall_thresh:
                  lowerCamelCase__					     = prec
                  break
      return prec_at_recall

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Tuple  )    ->	Tuple:


      '''simple docstring'''




      return normalize_answer(lowerCamelCase  ) == normalize_answer(lowerCamelCase  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[int]   , lowerCamelCase:				Optional[Any]   , lowerCamelCase:				Dict  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = 0
      for pred in predictions:
            for ground_truth in ground_truths:
                  lowerCamelCase__					     = metric_fn(lowerCamelCase   , lowerCamelCase  )
                  if score == 1:  # break the loop when one prediction matches the ground truth
                        break
            if score == 1:
                  break
      return score

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Optional[Any]  )    ->	Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__					     = lowerCamelCase__					     = lowerCamelCase__					     = 0
      lowerCamelCase__					     = []
      lowerCamelCase__					     = []
      for article in dataset:
            for paragraph in article["paragraphs"]:
                  for qa in paragraph["qas"]:
                        total += 1
                        if qa["id"] not in predictions:
                              lowerCamelCase__					     = 'Unanswered question ' + qa['id'] + ' will receive score 0.'
                              print(lowerCamelCase   , file=sys.stderr  )
                              continue
                        lowerCamelCase__					     = [x['text'] for x in qa['answers']]
                        lowerCamelCase__					     = predictions[qa['id']]
                        lowerCamelCase__     ,						lowerCamelCase__					     = compute_precision_recall(lowerCamelCase   , lowerCamelCase   , qa['id']  )

                        precisions.append(lowerCamelCase  )
                        recalls.append(lowerCamelCase  )

                        if precision == 0 and recall == 0:
                              fa += 0
                        else:
                              fa += 2 * (precision * recall) / (precision + recall)

                        exact_match += metric_max_over_ground_truths(lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )

      lowerCamelCase__					     = [x for _, x in sorted(zip(lowerCamelCase   , lowerCamelCase  )  )]
      recalls.sort()

      lowerCamelCase__					     = 100.0 * fa / total
      lowerCamelCase__					     = 100.0 * exact_match / total
      lowerCamelCase__					     = get_aupr(lowerCamelCase   , lowerCamelCase  )

      lowerCamelCase__					     = get_prec_at_recall(lowerCamelCase   , lowerCamelCase   , recall_thresh=0.9  )
      lowerCamelCase__					     = get_prec_at_recall(lowerCamelCase   , lowerCamelCase   , recall_thresh=0.8  )

      return {
          "exact_match": exact_match,
          "f1": fa,
          "aupr": aupr,
          "prec_at_80_recall": prec_at_aa_recall,
          "prec_at_90_recall": prec_at_aa_recall,
      }


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE			:      Optional[Any]      			=argparse.ArgumentParser(description="""Evaluation for CUAD""")
      parser.add_argument("""dataset_file""", help="""Dataset file""")
      parser.add_argument("""prediction_file""", help="""Prediction File""")
      __SCREAMING_SNAKE_CASE			:      Optional[int]      			=parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            __SCREAMING_SNAKE_CASE			:      Tuple      			=json.load(dataset_file)
            __SCREAMING_SNAKE_CASE			:      str      			=dataset_json["""data"""]
      with open(args.prediction_file) as prediction_file:
            __SCREAMING_SNAKE_CASE			:      int      			=json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))

from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


_A  					= "\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"







class      lowerCamelCase		(  A_  ):





     def __init__(self      :    Tuple				,    _A      :    UNetaDModel				,    _A      :    CMStochasticIterativeScheduler   )						->						None:
         super().__init__()

         self.register_modules(
             unet=_A				,    scheduler=_A				,    )

         snake_case      =					None





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]=0   )						->						Any:
         if is_accelerate_available() and is_accelerate_version(">="				,    "0.14.0"   ):
             from accelerate import cpu_offload
         else:
             raise ImportError("`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher"   )

         snake_case      =					torch.device(f'cuda:{gpu_id}'   )

         if self.device.type != "cpu":
             self.to("cpu"				,    silence_dtype_warnings=_A   )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         for cpu_offloaded_model in [self.unet]:
             cpu_offload(_A				,    _A   )

         if self.safety_checker is not None:
             cpu_offload(self.safety_checker				,    execution_device=_A				,    offload_buffers=_A   )





     def 		UpperCAmelCase(self      :    str				,    _A      :    Optional[Any]=0   )						->						Union[str, Any]:
         if is_accelerate_available() and is_accelerate_version(">="				,    "0.17.0.dev0"   ):
             from accelerate import cpu_offload_with_hook
         else:
             raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."   )

         snake_case      =					torch.device(f'cuda:{gpu_id}'   )

         if self.device.type != "cpu":
             self.to("cpu"				,    silence_dtype_warnings=_A   )
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         snake_case      =					None
         for cpu_offloaded_model in [self.unet]:
             snake_case     ,							snake_case      =					cpu_offload_with_hook(_A				,    _A				,    prev_module_hook=_A   )

         if self.safety_checker is not None:
             snake_case     ,							snake_case      =					cpu_offload_with_hook(self.safety_checker				,    _A				,    prev_module_hook=_A   )

         # We'll offload the last model manually.
         snake_case      =					hook





     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 		UpperCAmelCase(self      :    Dict   )						->						Any:
         if 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





     def 		UpperCAmelCase(self      :    Dict				,    _A      :    List[str]				,    _A      :    Tuple				,    _A      :    List[str]				,    _A      :    List[Any]				,    _A      :    Dict				,    _A      :    List[str]				,    _A      :    List[str]				,    _A      :    Optional[Any]=None   )						->						Tuple:
         snake_case      =					(batch_size, num_channels, height, width)
         if isinstance(_A				,    _A   ) and len(_A   ) != batch_size:
             raise ValueError(
                 f'You have passed a list of generators of length {len(_A   )}, but requested an effective batch'
                 f' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

         if latents is None:
             snake_case      =					randn_tensor(_A				,    generator=_A				,    device=_A				,    dtype=_A   )
         else:
             snake_case      =					latents.to(device=_A				,    dtype=_A   )

         # scale the initial noise by the standard deviation required by the scheduler
         snake_case      =					latents * self.scheduler.init_noise_sigma
         return latents





     def 		UpperCAmelCase(self      :    List[Any]				,    _A      :    torch.FloatTensor				,    _A      :    str = "pil"   )						->						str:
         if output_type not in ["pt", "np", "pil"]:
             raise ValueError(
                 f'output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'   )

         # Equivalent to diffusers.VaeImageProcessor.denormalize
         snake_case      =					(sample / 2 + 0.5).clamp(0				,    1   )
         if output_type == "pt":
             return sample

         # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
         snake_case      =					sample.cpu().permute(0				,    2				,    3				,    1   ).numpy()
         if output_type == "np":
             return sample

         # Output_type must be 'pil'
         snake_case      =					self.numpy_to_pil(_A   )
         return sample





     def 		UpperCAmelCase(self      :    str				,    _A      :    List[Any]				,    _A      :    Optional[Any]				,    _A      :    List[Any]=None   )						->						List[str]:
         if self.unet.config.num_class_embeds is not None:
             if isinstance(_A				,    _A   ):
                 snake_case      =					torch.tensor(_A				,    dtype=torch.int   )
             elif isinstance(_A				,    _A   ):
                 assert batch_size == 1, "Batch size must be 1 if classes is an int"
                 snake_case      =					torch.tensor([class_labels]				,    dtype=torch.int   )
             elif class_labels is None:
                 # Randomly generate batch_size class labels
                 # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
                 snake_case      =					torch.randint(0				,    self.unet.config.num_class_embeds				,    size=(batch_size,)   )
             snake_case      =					class_labels.to(_A   )
         else:
             snake_case      =					None
         return class_labels





     def 		UpperCAmelCase(self      :    Optional[Any]				,    _A      :    Tuple				,    _A      :    Union[str, Any]				,    _A      :    Dict				,    _A      :    Union[str, Any]				,    _A      :    Any				,    _A      :    str   )						->						Optional[Any]:
         if num_inference_steps is None and timesteps is None:
             raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."   )

         if num_inference_steps is not None and timesteps is not None:
             logger.warning(
                 f'Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'
                 " `timesteps` will be used over `num_inference_steps`."   )

         if latents is not None:
             snake_case      =					(batch_size, 3, img_size, img_size)
             if latents.shape != expected_shape:
                 raise ValueError(f'The shape of latents is {latents.shape} but is expected to be {expected_shape}.'   )

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(_A				,    _A   ) or callback_steps <= 0)
         ):
             raise ValueError(
                 f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                 f' {type(_A   )}.'   )





     @torch.no_grad()
     @replace_example_docstring(_A   )
     def __call__(self      :    Union[str, Any]				,    _A      :    int = 1				,    _A      :    Optional[Union[torch.Tensor, List[int], int]] = None				,    _A      :    int = 1				,    _A      :    List[int] = None				,    _A      :    Optional[Union[torch.Generator, List[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				,    )						->						List[str]:
         snake_case      =					self.unet.config.sample_size
         snake_case      =					self._execution_device

         # 1. Check inputs
         self.check_inputs(_A				,    _A				,    _A				,    _A				,    _A				,    _A   )

         # 2. Prepare image latents
         # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
         snake_case      =					self.prepare_latents(
             batch_size=_A				,    num_channels=self.unet.config.in_channels				,    height=_A				,    width=_A				,    dtype=self.unet.dtype				,    device=_A				,    generator=_A				,    latents=_A				,    )

         # 3. Handle class_labels for class-conditional models
         snake_case      =					self.prepare_class_labels(_A				,    _A				,    class_labels=_A   )

         # 4. Prepare timesteps
         if timesteps is not None:
             self.scheduler.set_timesteps(timesteps=_A				,    device=_A   )
             snake_case      =					self.scheduler.timesteps
             snake_case      =					len(_A   )
         else:
             self.scheduler.set_timesteps(_A   )
             snake_case      =					self.scheduler.timesteps

         # 5. Denoising loop
         # Multistep sampling: implements Algorithm 1 in the paper
         with self.progress_bar(total=_A   ) as progress_bar:
             for i, t in enumerate(_A   ):
                 snake_case      =					self.scheduler.scale_model_input(_A				,    _A   )
                 snake_case      =					self.unet(_A				,    _A				,    class_labels=_A				,    return_dict=_A   )[0]

                 snake_case      =					self.scheduler.step(_A				,    _A				,    _A				,    generator=_A   )[0]

                 # call the callback, if provided
                 progress_bar.update()
                 if callback is not None and i % callback_steps == 0:
                     callback(_A				,    _A				,    _A   )

        # 6. Post-process image sample
         snake_case      =					self.postprocess_image(_A				,    output_type=_A   )

         # Offload last model to CPU
         if hasattr(self				,    "final_offload_hook"   ) and self.final_offload_hook is not None:
             self.final_offload_hook.offload()

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=_A   )

