
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()





class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    StableDiffusionLDMaDPipeline
			UpperCamelCase__          =    TEXT_TO_IMAGE_PARAMS
			UpperCamelCase__          =    TEXT_TO_IMAGE_BATCH_PARAMS
			UpperCamelCase__          =    TEXT_TO_IMAGE_IMAGE_PARAMS







			def 			SCREAMING_SNAKE_CASE    (				self		):
				torch.manual_seed(0		)
				__magic_name__		:		Tuple	   =     UNetaDConditionModel(
				    block_out_channels=(32, 64)						,   layers_per_block=2						,   sample_size=32						,   in_channels=4						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=32						,   )
				__magic_name__		:		int	   =     DDIMScheduler(
				    beta_start=0.0_00_85						,   beta_end=0.0_12						,   beta_schedule="scaled_linear"						,   clip_sample=_a						,   set_alpha_to_one=_a						,   )
				torch.manual_seed(0		)
				__magic_name__		:		int	   =     AutoencoderKL(
				    block_out_channels=[32, 64]						,   in_channels=6						,   out_channels=6						,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,   latent_channels=4						,   )
				torch.manual_seed(0		)
				__magic_name__		:		List[str]	   =     CLIPTextConfig(
				    bos_token_id=0						,   eos_token_id=2						,   hidden_size=32						,   intermediate_size=37						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   pad_token_id=1						,   vocab_size=1_000						,   )
				__magic_name__		:		Optional[Any]	   =     CLIPTextModel(_a		)
				__magic_name__		:		Union[str, Any]	   =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

				__magic_name__		:		List[Any]	   =     {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				if str(_a		).startswith("mps"		):
					__magic_name__		:		Tuple	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		Optional[int]	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "cpu"  # ensure determinism for the device-dependent torch.Generator

				__magic_name__		:		Optional[int]	   =     self.get_dummy_components()
				__magic_name__		:		List[str]	   =     StableDiffusionLDMaDPipeline(**_a		)
				__magic_name__		:		List[Any]	   =     ldmad_pipe.to(_a		)
				ldmad_pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Optional[Any]	   =     self.get_dummy_inputs(_a		)
				__magic_name__		:		List[str]	   =     ldmad_pipe(**_a		)
				__magic_name__   ,    __magic_name__		:		Tuple	   =     output.rgb, output.depth

				__magic_name__		:		Union[str, Any]	   =     rgb[0, -3:, -3:, -1]
				__magic_name__		:		List[str]	   =     depth[0, -3:, -1]

				assert rgb.shape == (1, 64, 64, 3)
				assert depth.shape == (1, 64, 64)

				__magic_name__		:		str	   =     np.array(
				    [0.37_33_81_76, 0.7_02_47, 0.74_20_31_93, 0.51_64_36_04, 0.58_25_67_93, 0.60_93_21_36, 0.4_18_10_95, 0.48_35_58_77, 0.46_53_52_62]		)
				__magic_name__		:		Optional[int]	   =     np.array([1_03.4_67_27, 85.81_20_04, 87.84_92_36]		)

				assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb		).max() < 1e-2
				assert np.abs(image_slice_depth.flatten() - expected_slice_depth		).max() < 1e-2







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Optional[Any]	   =     self.get_dummy_components()
				__magic_name__		:		Tuple	   =     StableDiffusionLDMaDPipeline(**_a		)
				__magic_name__		:		Optional[Any]	   =     ldmad_pipe.to(_a		)
				ldmad_pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Any	   =     self.get_dummy_inputs(_a		)
				__magic_name__		:		List[str]	   =     3 * [inputs["prompt"]]

				# forward
				__magic_name__		:		Union[str, Any]	   =     ldmad_pipe(**_a		)
				__magic_name__   ,    __magic_name__		:		int	   =     output.rgb, output.depth
				__magic_name__		:		Union[str, Any]	   =     rgb_slice_a[0, -3:, -3:, -1]
				__magic_name__		:		List[str]	   =     depth_slice_a[0, -3:, -1]

				__magic_name__		:		List[str]	   =     self.get_dummy_inputs(_a		)
				__magic_name__		:		List[str]	   =     3 * [inputs.pop("prompt"		)]

				__magic_name__		:		List[Any]	   =     ldmad_pipe.tokenizer(
				    _a						,   padding="max_length"						,   max_length=ldmad_pipe.tokenizer.model_max_length						,   truncation=_a						,   return_tensors="pt"						,   )
				__magic_name__		:		Any	   =     text_inputs["input_ids"].to(_a		)

				__magic_name__		:		Optional[int]	   =     ldmad_pipe.text_encoder(_a		)[0]

				__magic_name__		:		List[Any]	   =     prompt_embeds

				# forward
				__magic_name__		:		Dict	   =     ldmad_pipe(**_a		)
				__magic_name__   ,    __magic_name__		:		Optional[int]	   =     output.rgb, output.depth
				__magic_name__		:		str	   =     rgb_slice_a[0, -3:, -3:, -1]
				__magic_name__		:		Union[str, Any]	   =     depth_slice_a[0, -3:, -1]

				assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()		).max() < 1e-4
				assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()		).max() < 1e-4







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[Any]	   =     "cpu"  # ensure determinism for the device-dependent torch.Generator
				__magic_name__		:		List[Any]	   =     self.get_dummy_components()
				__magic_name__		:		List[str]	   =     PNDMScheduler(skip_prk_steps=_a		)
				__magic_name__		:		Optional[Any]	   =     StableDiffusionLDMaDPipeline(**_a		)
				__magic_name__		:		Union[str, Any]	   =     ldmad_pipe.to(_a		)
				ldmad_pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		str	   =     self.get_dummy_inputs(_a		)
				__magic_name__		:		Tuple	   =     "french fries"
				__magic_name__		:		List[Any]	   =     ldmad_pipe(**_a						,   negative_prompt=_a		)

				__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     output.rgb, output.depth
				__magic_name__		:		Dict	   =     rgb[0, -3:, -3:, -1]
				__magic_name__		:		List[str]	   =     depth[0, -3:, -1]

				assert rgb.shape == (1, 64, 64, 3)
				assert depth.shape == (1, 64, 64)

				__magic_name__		:		Dict	   =     np.array(
				    [0.3_70_44, 0.71_81_15_03, 0.7_22_32_51, 0.48_60_36_75, 0.5_63_83_91, 0.6_36_49_48, 0.42_83_37_04, 0.4_90_13_15, 0.47_92_62_17]		)
				__magic_name__		:		Dict	   =     np.array([1_07.8_47_38, 84.6_28_02, 89.96_21_35]		)
				assert np.abs(rgb_slice.flatten() - expected_slice_rgb		).max() < 1e-2
				assert np.abs(depth_slice.flatten() - expected_slice_depth		).max() < 1e-2







@slow
@require_torch_gpu
class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a="cpu"						,   _a=torch.floataa						,   _a=0		):
				__magic_name__		:		Tuple	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		Union[str, Any]	   =     np.random.RandomState(_a		).standard_normal((1, 4, 64, 64)		)
				__magic_name__		:		Tuple	   =     torch.from_numpy(_a		).to(device=_a						,   dtype=_a		)
				__magic_name__		:		Union[str, Any]	   =     {
				    "prompt": "a photograph of an astronaut riding a horse",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 3,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Tuple	   =     StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		)
				__magic_name__		:		Optional[Any]	   =     ldmad_pipe.to(_a		)
				ldmad_pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		List[Any]	   =     self.get_inputs(_a		)
				__magic_name__		:		List[str]	   =     ldmad_pipe(**_a		)
				__magic_name__   ,    __magic_name__		:		int	   =     output.rgb, output.depth
				__magic_name__		:		Dict	   =     rgb[0, -3:, -3:, -1].flatten()
				__magic_name__		:		str	   =     rgb[0, -3:, -1].flatten()

				assert rgb.shape == (1, 512, 512, 3)
				assert depth.shape == (1, 512, 512)

				__magic_name__		:		Union[str, Any]	   =     np.array(
				    [0.53_80_54_65, 0.56_70_73_05, 0.5_48_65_15, 0.57_01_22_36, 0.5_81_45_11, 0.56_25_34_87, 0.54_84_30_14, 0.55_09_22_63, 0.6_45_97_06]		)
				__magic_name__		:		Any	   =     np.array(
				    [0.9_26_37_81, 0.6_67_86_72, 0.5_48_65_15, 0.92_20_21_45, 0.67_83_11_35, 0.56_25_34_87, 0.9_24_16_94, 0.7_55_14_78, 0.6_45_97_06]		)
				assert np.abs(rgb_slice - expected_slice_rgb		).max() < 3e-3
				assert np.abs(depth_slice - expected_slice_depth		).max() < 3e-3







@nightly
@require_torch_gpu
class       _snake_case					(     unittest.TestCase				):







			def 			SCREAMING_SNAKE_CASE    (				self		):
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a="cpu"						,   _a=torch.floataa						,   _a=0		):
				__magic_name__		:		Union[str, Any]	   =     torch.Generator(device=_a		).manual_seed(_a		)
				__magic_name__		:		Tuple	   =     np.random.RandomState(_a		).standard_normal((1, 4, 64, 64)		)
				__magic_name__		:		List[str]	   =     torch.from_numpy(_a		).to(device=_a						,   dtype=_a		)
				__magic_name__		:		Optional[Any]	   =     {
				    "prompt": "a photograph of an astronaut riding a horse",
				    "latents": latents,
				    "generator": generator,
				    "num_inference_steps": 50,
				    "guidance_scale": 7.5,
				    "output_type": "numpy",
				}
				return inputs







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		int	   =     StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"		).to(_a		)
				ldmad_pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		Tuple	   =     self.get_inputs(_a		)
				__magic_name__		:		Dict	   =     ldmad_pipe(**_a		)
				__magic_name__   ,    __magic_name__		:		Tuple	   =     output.rgb, output.depth

				__magic_name__		:		List[Any]	   =     0.49_55_86
				__magic_name__		:		Any	   =     0.33_79_55_15
				__magic_name__		:		str	   =     1_12.4_85_18
				__magic_name__		:		Tuple	   =     98.48_97_46
				assert np.abs(expected_rgb_mean - rgb.mean()		) < 1e-3
				assert np.abs(expected_rgb_std - rgb.std()		) < 1e-3
				assert np.abs(expected_depth_mean - depth.mean()		) < 1e-3
				assert np.abs(expected_depth_std - depth.std()		) < 1e-3







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"		).to(_a		)
				ldmad_pipe.set_progress_bar_config(disable=_a		)

				__magic_name__		:		str	   =     self.get_inputs(_a		)
				__magic_name__		:		int	   =     ldmad_pipe(**_a		)
				__magic_name__   ,    __magic_name__		:		Any	   =     output.rgb, output.depth

				__magic_name__		:		Optional[int]	   =     0.4_19_41_27
				__magic_name__		:		Any	   =     0.35_37_55_86
				__magic_name__		:		Tuple	   =     0.5_63_85_02
				__magic_name__		:		Dict	   =     0.34_68_61_03

				assert rgb.shape == (1, 512, 512, 3)
				assert depth.shape == (1, 512, 512, 1)
				assert np.abs(expected_rgb_mean - rgb.mean()		) < 1e-3
				assert np.abs(expected_rgb_std - rgb.std()		) < 1e-3
				assert np.abs(expected_depth_mean - depth.mean()		) < 1e-3
				assert np.abs(expected_depth_std - depth.std()		) < 1e-3


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

import numpy as np


snake_case :		Dict							      =  re.compile(R"\b(a|an|the)\b", re.UNICODE)

snake_case :		Optional[int]							      =  None






def   lowerCAmelCase_						(					)				->		Union[str, Any]:







	'''simple docstring'''


	__magic_name__		:		Any	   =     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=_snake_case      ,   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=_snake_case      ,   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   lowerCAmelCase_						(					_snake_case      :  Optional[Any]   )				->		Tuple:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]   )				->		Optional[Any]:







	'''simple docstring'''


	def remove_articles(_snake_case      :  List[str]   ):
		return ARTICLES_REGEX.sub(" "      ,   _snake_case   )

	def white_space_fix(_snake_case      :  Optional[int]   ):
		return " ".join(text.split()   )

	def remove_punc(_snake_case      :  Optional[int]   ):
		__magic_name__		:		Dict	   =     set(string.punctuation   )
		return "".join(ch for ch in text if ch not in exclude   )

	def lower(_snake_case      :  str   ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_snake_case   )   )   )   )






def   lowerCAmelCase_						(					_snake_case      :  Any   )				->		Optional[Any]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  str      ,   _snake_case      :  Dict   )				->		Tuple:







	'''simple docstring'''


	return int(normalize_answer(_snake_case   ) == normalize_answer(_snake_case   )   )






def   lowerCAmelCase_						(					_snake_case      :  List[str]      ,   _snake_case      :  int   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Any	   =     get_tokens(_snake_case   )
	__magic_name__		:		Optional[int]	   =     get_tokens(_snake_case   )
	__magic_name__		:		Tuple	   =     collections.Counter(_snake_case   ) & collections.Counter(_snake_case   )
	__magic_name__		:		Tuple	   =     sum(common.values()   )
	if len(_snake_case   ) == 0 or len(_snake_case   ) == 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
	__magic_name__		:		Dict	   =     1.0 * num_same / len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     1.0 * num_same / len(_snake_case   )
	__magic_name__		:		List[Any]	   =     (2 * precision * recall) / (precision + recall)
	return fa






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







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  Optional[Any]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  Dict   )				->		Union[str, Any]:







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  List[Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  Tuple=None   )				->		Tuple:







	'''simple docstring'''


	if not qid_list:
		__magic_name__		:		Any	   =     len(_snake_case   )
		return collections.OrderedDict(
		    [
		        ("exact", 100.0 * sum(exact_scores.values()   ) / total),
		        ("f1", 100.0 * sum(fa_scores.values()   ) / total),
		        ("total", total),
		    ]   )
	else:
		__magic_name__		:		Tuple	   =     len(_snake_case   )
		return collections.OrderedDict(
		    [
		        ("exact", 100.0 * sum(exact_scores[k] for k in qid_list   ) / total),
		        ("f1", 100.0 * sum(fa_scores[k] for k in qid_list   ) / total),
		        ("total", total),
		    ]   )






def   lowerCAmelCase_						(					_snake_case      :  Optional[int]      ,   _snake_case      :  str      ,   _snake_case      :  str   )				->		Dict:







	'''simple docstring'''


	for k in new_eval:
		__magic_name__		:		int	   =     new_eval[k]






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Dict      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  Union[str, Any]   )				->		str:







	'''simple docstring'''


	plt.step(_snake_case      ,   _snake_case      ,   color="b"      ,   alpha=0.2      ,   where="post"   )
	plt.fill_between(_snake_case      ,   _snake_case      ,   step="post"      ,   alpha=0.2      ,   color="b"   )
	plt.xlabel("Recall"   )
	plt.ylabel("Precision"   )
	plt.xlim([0.0, 1.05]   )
	plt.ylim([0.0, 1.05]   )
	plt.title(_snake_case   )
	plt.savefig(_snake_case   )
	plt.clf()






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Any      ,   _snake_case      :  Optional[int]      ,   _snake_case      :  List[Any]      ,   _snake_case      :  Optional[int]=None      ,   _snake_case      :  int=None   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     sorted(_snake_case      ,   key=lambda _snake_case   : na_probs[k]   )
	__magic_name__		:		Optional[int]	   =     0.0
	__magic_name__		:		str	   =     1.0
	__magic_name__		:		str	   =     0.0
	__magic_name__		:		List[str]	   =     [1.0]
	__magic_name__		:		str	   =     [0.0]
	__magic_name__		:		Optional[Any]	   =     0.0
	for i, qid in enumerate(_snake_case   ):
		if qid_to_has_ans[qid]:
			true_pos += scores[qid]
		__magic_name__		:		List[str]	   =     true_pos / float(i + 1   )
		__magic_name__		:		Any	   =     true_pos / float(_snake_case   )
		if i == len(_snake_case   ) - 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(_snake_case   )
			recalls.append(_snake_case   )
	if out_image:
		plot_pr_curve(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )
	return {"ap": 100.0 * avg_prec}






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







	'''simple docstring'''


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






def   lowerCAmelCase_						(					_snake_case      :  int      ,   _snake_case      :  Optional[Any]      ,   _snake_case      :  List[str]      ,   _snake_case      :  Optional[Any]   )				->		Dict:







	'''simple docstring'''


	if not qid_list:
		return
	__magic_name__		:		Dict	   =     [na_probs[k] for k in qid_list]
	__magic_name__		:		str	   =     np.ones_like(_snake_case   ) / float(len(_snake_case   )   )
	plt.hist(_snake_case      ,   weights=_snake_case      ,   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(_snake_case      ,   F'''na_prob_hist_{name}.png'''   )   )
	plt.clf()






def   lowerCAmelCase_						(					_snake_case      :  Union[str, Any]      ,   _snake_case      :  Tuple      ,   _snake_case      :  List[str]      ,   _snake_case      :  Dict   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Union[str, Any]	   =     sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]   )
	__magic_name__		:		List[str]	   =     num_no_ans
	__magic_name__		:		Dict	   =     cur_score
	__magic_name__		:		Dict	   =     0.0
	__magic_name__		:		Any	   =     sorted(_snake_case      ,   key=lambda _snake_case   : na_probs[k]   )
	for i, qid in enumerate(_snake_case   ):
		if qid not in scores:
			continue
		if qid_to_has_ans[qid]:
			__magic_name__		:		Union[str, Any]	   =     scores[qid]
		else:
			if preds[qid]:
				__magic_name__		:		List[Any]	   =     -1
			else:
				__magic_name__		:		Optional[int]	   =     0
		cur_score += diff
		if cur_score > best_score:
			__magic_name__		:		Optional[int]	   =     cur_score
			__magic_name__		:		List[Any]	   =     na_probs[qid]
	return 100.0 * best_score / len(_snake_case   ), best_thresh






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







	'''simple docstring'''


	__magic_name__   ,    __magic_name__		:		List[str]	   =     find_best_thresh(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )
	__magic_name__   ,    __magic_name__		:		int	   =     find_best_thresh(_snake_case      ,   _snake_case      ,   _snake_case      ,   _snake_case   )
	__magic_name__		:		Optional[int]	   =     best_exact
	__magic_name__		:		List[Any]	   =     exact_thresh
	__magic_name__		:		Dict	   =     best_fa
	__magic_name__		:		Any	   =     fa_thresh






def   lowerCAmelCase_						(					)				->		int:







	'''simple docstring'''


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


if __name__ == "__main__":
				snake_case :		int							      =  parse_args()
				if OPTS.out_image_dir:
								import matplotlib

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