






'''simple docstring'''


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


lowerCamelCase_				: Union[str, Any]    	=							logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase_				: List[Any]    	=							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''







def 	__magic_name__( _A						,      _A						,      _A=8					):
						'''simple docstring'''





						UpperCamelCase__         = height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						UpperCamelCase__         = width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor







def 	__magic_name__( _A						,      _A=512						,      _A=512					):
						'''simple docstring'''





						UpperCamelCase__         = pil_image.resize((w, h)						,      resample=Image.BICUBIC						,      reducing_gap=1					)
						UpperCamelCase__         = np.array(pil_image.convert("""RGB"""					)					)
						UpperCamelCase__         = arr.astype(np.floataa					) / 127.5 - 1
						UpperCamelCase__         = np.transpose(_A						,      [2, 0, 1]					)
						UpperCamelCase__         = torch.from_numpy(_A					).unsqueeze(0					)
						return image







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						UNetaDConditionModel ,      lowercase							:						DDPMScheduler ,      lowercase							:						VQModel ,      )							->    Dict:

									'''simple docstring'''







									super().__init__()

									self.register_modules(
									    unet=lowercase ,      scheduler=lowercase ,      movq=lowercase ,      )
									UpperCamelCase__         = 2 ** (len(self.movq.config.block_out_channels			) - 1)






			def 				A      (   self							:						List[Any] ,      lowercase							:						List[Any] ,      lowercase							:						List[str] ,      lowercase							:						Dict			)							->    Any:

									'''simple docstring'''







									UpperCamelCase__         = min(int(num_inference_steps * strength			) ,      lowercase			)

									UpperCamelCase__         = max(num_inference_steps - init_timestep ,      0			)
									UpperCamelCase__         = self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






			def 				A      (   self							:						List[str] ,      lowercase							:						List[Any] ,      lowercase							:						List[str] ,      lowercase							:						Dict ,      lowercase							:						Union[str, Any] ,      lowercase							:						str ,      lowercase							:						Optional[int] ,      lowercase							:						Tuple=None			)							->    List[str]:

									'''simple docstring'''







									if not isinstance(lowercase ,      (torch.Tensor, PIL.Image.Image, list)			):
															raise ValueError(
															    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase			)}"			)

									UpperCamelCase__         = image.to(device=lowercase ,      dtype=lowercase			)

									UpperCamelCase__         = batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															UpperCamelCase__         = image

									else:
															if isinstance(lowercase ,      lowercase			) and len(lowercase			) != batch_size:
																					raise ValueError(
																					    f"You have passed a list of generators of length {len(lowercase			)}, but requested an effective batch"
																					    f" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

															elif isinstance(lowercase ,      lowercase			):
																					UpperCamelCase__         = [
																					    self.movq.encode(image[i : i + 1]			).latent_dist.sample(generator[i]			) for i in range(lowercase			)
																					]
																					UpperCamelCase__         = torch.cat(lowercase ,      dim=0			)
															else:
																					UpperCamelCase__         = self.movq.encode(lowercase			).latent_dist.sample(lowercase			)

															UpperCamelCase__         = self.movq.config.scaling_factor * init_latents

									UpperCamelCase__         = torch.cat([init_latents] ,      dim=0			)

									UpperCamelCase__         = init_latents.shape
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=lowercase ,      dtype=lowercase			)

									# get latents
									UpperCamelCase__         = self.scheduler.add_noise(lowercase ,      lowercase ,      lowercase			)

									UpperCamelCase__         = init_latents

									return latents






			def 				A      (   self							:						Tuple ,      lowercase							:						Union[str, Any]=0			)							->    Union[str, Any]:

									'''simple docstring'''







									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("""Please install accelerate via `pip install accelerate`"""			)

									UpperCamelCase__         = torch.device(f"cuda:{gpu_id}"			)

									UpperCamelCase__         = [
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(lowercase ,      lowercase			)






			def 				A      (   self							:						Any ,      lowercase							:						Optional[Any]=0			)							->    List[str]:

									'''simple docstring'''







									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."""			)

									UpperCamelCase__         = torch.device(f"cuda:{gpu_id}"			)

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

									UpperCamelCase__         = None
									for cpu_offloaded_model in [self.unet, self.movq]:
															UpperCamelCase__    ,   UpperCamelCase__         = cpu_offload_with_hook(lowercase ,      lowercase ,      prev_module_hook=lowercase			)

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






			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 				A      (   self							:						str			)							->    Optional[int]:

									'''simple docstring'''







									if not hasattr(self.unet ,      """_hf_hook"""			):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(lowercase ,      """_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()
			@replace_example_docstring(lowercase			)
			def __call__(   self							:						int ,      lowercase							:						Union[torch.FloatTensor, List[torch.FloatTensor]] ,      lowercase							:						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]] ,      lowercase							:						Union[torch.FloatTensor, List[torch.FloatTensor]] ,      lowercase							:						int = 5_1_2 ,      lowercase							:						int = 5_1_2 ,      lowercase							:						int = 1_0_0 ,      lowercase							:						float = 4.0 ,      lowercase							:						float = 0.3 ,      lowercase							:						int = 1 ,      lowercase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowercase							:						Optional[str] = "pil" ,      lowercase							:						bool = True ,      )							->    str:

									'''simple docstring'''







									UpperCamelCase__         = self._execution_device

									UpperCamelCase__         = guidance_scale > 1.0

									if isinstance(lowercase ,      lowercase			):
															UpperCamelCase__         = torch.cat(lowercase ,      dim=0			)
									UpperCamelCase__         = image_embeds.shape[0]
									if isinstance(lowercase ,      lowercase			):
															UpperCamelCase__         = torch.cat(lowercase ,      dim=0			)

									if do_classifier_free_guidance:
															UpperCamelCase__         = image_embeds.repeat_interleave(lowercase ,      dim=0			)
															UpperCamelCase__         = negative_image_embeds.repeat_interleave(lowercase ,      dim=0			)

									UpperCamelCase__         = torch.cat([negative_image_embeds, image_embeds] ,      dim=0			).to(dtype=self.unet.dtype ,      device=lowercase			)

									if not isinstance(lowercase ,      lowercase			):
															UpperCamelCase__         = [image]
									if not all(isinstance(lowercase ,      (PIL.Image.Image, torch.Tensor)			) for i in image			):
															raise ValueError(
															    f"Input is in incorrect format: {[type(lowercase			) for i in image]}. Currently, we only support  PIL image and pytorch tensor"			)

									UpperCamelCase__         = torch.cat([prepare_image(lowercase ,      lowercase ,      lowercase			) for i in image] ,      dim=0			)
									UpperCamelCase__         = image.to(dtype=image_embeds.dtype ,      device=lowercase			)

									UpperCamelCase__         = self.movq.encode(lowercase			)["""latents"""]
									UpperCamelCase__         = latents.repeat_interleave(lowercase ,      dim=0			)
									self.scheduler.set_timesteps(lowercase ,      device=lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.get_timesteps(lowercase ,      lowercase ,      lowercase			)
									UpperCamelCase__         = timesteps[:1].repeat(batch_size * num_images_per_prompt			)
									UpperCamelCase__    ,   UpperCamelCase__         = downscale_height_and_width(lowercase ,      lowercase ,      self.movq_scale_factor			)
									UpperCamelCase__         = self.prepare_latents(
									    lowercase ,      lowercase ,      lowercase ,      lowercase ,      image_embeds.dtype ,      lowercase ,      lowercase			)
									for i, t in enumerate(self.progress_bar(lowercase			)			):
															# expand the latents if we are doing classifier free guidance
															UpperCamelCase__         = torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

															UpperCamelCase__         = {"""image_embeds""": image_embeds}
															UpperCamelCase__         = self.unet(
															    sample=lowercase ,      timestep=lowercase ,      encoder_hidden_states=lowercase ,      added_cond_kwargs=lowercase ,      return_dict=lowercase ,      )[0]

															if do_classifier_free_guidance:
																					UpperCamelCase__    ,   UpperCamelCase__         = noise_pred.split(latents.shape[1] ,      dim=1			)
																					UpperCamelCase__    ,   UpperCamelCase__         = noise_pred.chunk(2			)
																					UpperCamelCase__    ,   UpperCamelCase__         = variance_pred.chunk(2			)
																					UpperCamelCase__         = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					UpperCamelCase__         = torch.cat([noise_pred, variance_pred_text] ,      dim=1			)

															if not (
															    hasattr(self.scheduler.config ,      """variance_type"""			)
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																					UpperCamelCase__    ,   UpperCamelCase__         = noise_pred.split(latents.shape[1] ,      dim=1			)

															# compute the previous noisy sample x_t -> x_t-1
															UpperCamelCase__         = self.scheduler.step(
															    lowercase ,      lowercase ,      lowercase ,      generator=lowercase ,      )[0]

									# post-processing
									UpperCamelCase__         = self.movq.decode(lowercase ,      force_not_quantize=lowercase			)["""sample"""]

									if output_type not in ["pt", "np", "pil"]:
															raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"			)

									if output_type in ["np", "pil"]:
															UpperCamelCase__         = image * 0.5 + 0.5
															UpperCamelCase__         = image.clamp(0 ,      1			)
															UpperCamelCase__         = image.cpu().permute(0 ,      2 ,      3 ,      1			).float().numpy()

									if output_type == "pil":
															UpperCamelCase__         = self.numpy_to_pil(lowercase			)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowercase			)














'''simple docstring'''


import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


lowerCamelCase_				: Union[str, Any]    	=							re.compile(r'''\b(a|an|the)\b''', re.UNICODE)

lowerCamelCase_				: int    	=							None







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""					)
						parser.add_argument("""data_file"""						,      metavar="""data.json"""						,      help="""Input data JSON file."""					)
						parser.add_argument("""pred_file"""						,      metavar="""pred.json"""						,      help="""Model predictions."""					)
						parser.add_argument(
						    """--out-file"""						,      """-o"""						,      metavar="""eval.json"""						,      help="""Write accuracy metrics to file (default is stdout)."""					)
						parser.add_argument(
						    """--na-prob-file"""						,      """-n"""						,      metavar="""na_prob.json"""						,      help="""Model estimates of probability of no answer."""					)
						parser.add_argument(
						    """--na-prob-thresh"""						,      """-t"""						,      type=_A						,      default=1.0						,      help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""						,      )
						parser.add_argument(
						    """--out-image-dir"""						,      """-p"""						,      metavar="""out_images"""						,      default=_A						,      help="""Save precision-recall curves to directory."""					)
						parser.add_argument("""--verbose"""						,      """-v"""						,      action="""store_true"""					)
						if len(sys.argv					) == 1:
												parser.print_help()
												sys.exit(1					)
						return parser.parse_args()







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = bool(qa["""answers"""]["""text"""]					)
						return qid_to_has_ans







def 	__magic_name__( _A					):
						'''simple docstring'''





						def remove_articles(_A					):
												return ARTICLES_REGEX.sub(""" """						,      _A					)

						def white_space_fix(_A					):
												return " ".join(text.split()					)

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

						def lower(_A					):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_A					)					)					)					)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if not s:
												return []
						return normalize_answer(_A					).split()







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						return int(normalize_answer(_A					) == normalize_answer(_A					)					)







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = get_tokens(_A					)
						UpperCamelCase__         = collections.Counter(_A					) & collections.Counter(_A					)
						UpperCamelCase__         = sum(common.values()					)
						if len(_A					) == 0 or len(_A					) == 0:
												# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
												return int(gold_toks == pred_toks					)
						if num_same == 0:
												return 0
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = 1.0 * num_same / len(_A					)
						UpperCamelCase__         = (2 * precision * recall) / (precision + recall)
						return fa







def 	__magic_name__( _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						UpperCamelCase__         = {}
						for article in dataset:
												for p in article["paragraphs"]:
																		for qa in p["qas"]:
																								UpperCamelCase__         = qa["""id"""]
																								UpperCamelCase__         = [t for t in qa["""answers"""]["""text"""] if normalize_answer(_A					)]
																								if not gold_answers:
																														# For unanswerable questions, only correct answer is empty string
																														UpperCamelCase__         = [""""""]
																								if qid not in preds:
																														print(f"Missing prediction for {qid}"					)
																														continue
																								UpperCamelCase__         = preds[qid]
																								# Take max over all gold answers
																								UpperCamelCase__         = max(compute_exact(_A						,      _A					) for a in gold_answers					)
																								UpperCamelCase__         = max(compute_fa(_A						,      _A					) for a in gold_answers					)
						return exact_scores, fa_scores







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = {}
						for qid, s in scores.items():
												UpperCamelCase__         = na_probs[qid] > na_prob_thresh
												if pred_na:
																		UpperCamelCase__         = float(not qid_to_has_ans[qid]					)
												else:
																		UpperCamelCase__         = s
						return new_scores







def 	__magic_name__( _A						,      _A						,      _A=None					):
						'''simple docstring'''





						if not qid_list:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores.values()					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores.values()					) / total),
												        ("""total""", total),
												    ]					)
						else:
												UpperCamelCase__         = len(_A					)
												return collections.OrderedDict(
												    [
												        ("""exact""", 1_0_0.0 * sum(exact_scores[k] for k in qid_list					) / total),
												        ("""f1""", 1_0_0.0 * sum(fa_scores[k] for k in qid_list					) / total),
												        ("""total""", total),
												    ]					)







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						for k in new_eval:
												UpperCamelCase__         = new_eval[k]







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						plt.step(_A						,      _A						,      color="""b"""						,      alpha=0.2						,      where="""post"""					)
						plt.fill_between(_A						,      _A						,      step="""post"""						,      alpha=0.2						,      color="""b"""					)
						plt.xlabel("""Recall"""					)
						plt.ylabel("""Precision"""					)
						plt.xlim([0.0, 1.0_5]					)
						plt.ylim([0.0, 1.0_5]					)
						plt.title(_A					)
						plt.savefig(_A					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A=None						,      _A=None					):
						'''simple docstring'''





						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = 1.0
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = [1.0]
						UpperCamelCase__         = [0.0]
						UpperCamelCase__         = 0.0
						for i, qid in enumerate(_A					):
												if qid_to_has_ans[qid]:
																		true_pos += scores[qid]
												UpperCamelCase__         = true_pos / float(i + 1					)
												UpperCamelCase__         = true_pos / float(_A					)
												if i == len(_A					) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
																		# i.e., if we can put a threshold after this point
																		avg_prec += cur_p * (cur_r - recalls[-1])
																		precisions.append(_A					)
																		recalls.append(_A					)
						if out_image:
												plot_pr_curve(_A						,      _A						,      _A						,      _A					)
						return {"ap": 1_0_0.0 * avg_prec}







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if out_image_dir and not os.path.exists(_A					):
												os.makedirs(_A					)
						UpperCamelCase__         = sum(1 for v in qid_to_has_ans.values() if v					)
						if num_true_pos == 0:
												return
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_exact.png"""					)						,      title="""Precision-Recall curve for Exact Match score"""						,      )
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_f1.png"""					)						,      title="""Precision-Recall curve for F1 score"""						,      )
						UpperCamelCase__         = {k: float(_A					) for k, v in qid_to_has_ans.items()}
						UpperCamelCase__         = make_precision_recall_eval(
						    _A						,      _A						,      _A						,      _A						,      out_image=os.path.join(_A						,      """pr_oracle.png"""					)						,      title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""						,      )
						merge_eval(_A						,      _A						,      """pr_exact"""					)
						merge_eval(_A						,      _A						,      """pr_f1"""					)
						merge_eval(_A						,      _A						,      """pr_oracle"""					)







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						if not qid_list:
												return
						UpperCamelCase__         = [na_probs[k] for k in qid_list]
						UpperCamelCase__         = np.ones_like(_A					) / float(len(_A					)					)
						plt.hist(_A						,      weights=_A						,      bins=20						,      range=(0.0, 1.0)					)
						plt.xlabel("""Model probability of no-answer"""					)
						plt.ylabel("""Proportion of dataset"""					)
						plt.title(f"Histogram of no-answer probability: {name}"					)
						plt.savefig(os.path.join(_A						,      f"na_prob_hist_{name}.png"					)					)
						plt.clf()







def 	__magic_name__( _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]					)
						UpperCamelCase__         = num_no_ans
						UpperCamelCase__         = cur_score
						UpperCamelCase__         = 0.0
						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: na_probs[k]					)
						for i, qid in enumerate(_A					):
												if qid not in scores:
																		continue
												if qid_to_has_ans[qid]:
																		UpperCamelCase__         = scores[qid]
												else:
																		if preds[qid]:
																								UpperCamelCase__         = -1
																		else:
																								UpperCamelCase__         = 0
												cur_score += diff
												if cur_score > best_score:
																		UpperCamelCase__         = cur_score
																		UpperCamelCase__         = na_probs[qid]
						return 1_0_0.0 * best_score / len(_A					), best_thresh







def 	__magic_name__( _A						,      _A						,      _A						,      _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__    ,   UpperCamelCase__         = find_best_thresh(_A						,      _A						,      _A						,      _A					)
						UpperCamelCase__         = best_exact
						UpperCamelCase__         = exact_thresh
						UpperCamelCase__         = best_fa
						UpperCamelCase__         = fa_thresh







def 	__magic_name__( ):
						'''simple docstring'''





						with open(OPTS.data_file					) as f:
												UpperCamelCase__         = json.load(_A					)
												UpperCamelCase__         = dataset_json["""data"""]
						with open(OPTS.pred_file					) as f:
												UpperCamelCase__         = json.load(_A					)
						if OPTS.na_prob_file:
												with open(OPTS.na_prob_file					) as f:
																		UpperCamelCase__         = json.load(_A					)
						else:
												UpperCamelCase__         = {k: 0.0 for k in preds}
						UpperCamelCase__         = make_qid_to_has_ans(_A					)  # maps qid to True/False
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if v]
						UpperCamelCase__         = [k for k, v in qid_to_has_ans.items() if not v]
						UpperCamelCase__    ,   UpperCamelCase__         = get_raw_scores(_A						,      _A					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = apply_no_ans_threshold(_A						,      _A						,      _A						,      OPTS.na_prob_thresh					)
						UpperCamelCase__         = make_eval_dict(_A						,      _A					)
						if has_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """HasAns"""					)
						if no_ans_qids:
												UpperCamelCase__         = make_eval_dict(_A						,      _A						,      qid_list=_A					)
												merge_eval(_A						,      _A						,      """NoAns"""					)
						if OPTS.na_prob_file:
												find_all_best_thresh(_A						,      _A						,      _A						,      _A						,      _A						,      _A					)
						if OPTS.na_prob_file and OPTS.out_image_dir:
												run_precision_recall_analysis(_A						,      _A						,      _A						,      _A						,      _A						,      OPTS.out_image_dir					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """hasAns"""					)
												histogram_na_prob(_A						,      _A						,      OPTS.out_image_dir						,      """noAns"""					)
						if OPTS.out_file:
												with open(OPTS.out_file						,      """w"""					) as f:
																		json.dump(_A						,      _A					)
						else:
												print(json.dumps(_A						,      indent=2					)					)


if __name__ == "__main__":
					lowerCamelCase_				: str    	=							parse_args()
					if OPTS.out_image_dir:
										import matplotlib

										matplotlib.use('''Agg''')
										import matplotlib.pyplot as plt
					main()
