'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      int      							=   AudioLDMPipeline
      UpperCAmelCase_      :      Optional[int]      							=   TEXT_TO_AUDIO_PARAMS
      UpperCAmelCase_      :      Optional[Any]      							=   TEXT_TO_AUDIO_BATCH_PARAMS
      UpperCAmelCase_      :      Tuple      							=   frozenset(
          [
              '''num_inference_steps''',
              '''num_waveforms_per_prompt''',
              '''generator''',
              '''latents''',
              '''output_type''',
              '''return_dict''',
              '''callback''',
              '''callback_steps''',
          ]   )




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         torch.manual_seed(0							)
         UpperCAmelCase__							:				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, 64)					,class_embed_type='''simple_projection'''					,projection_class_embeddings_input_dim=32					,class_embeddings_concat=lowerCamelCase_					,)
         UpperCAmelCase__							:				Union[str, Any]   				=  DDIMScheduler(
             beta_start=0.0_0085					,beta_end=0.012					,beta_schedule='''scaled_linear'''					,clip_sample=lowerCamelCase_					,set_alpha_to_one=lowerCamelCase_					,)
         torch.manual_seed(0							)
         UpperCAmelCase__							:				List[str]   				=  AutoencoderKL(
             block_out_channels=[32, 64]					,in_channels=1					,out_channels=1					,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,latent_channels=4					,)
         torch.manual_seed(0							)
         UpperCAmelCase__							:				List[str]   				=  ClapTextConfig(
             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=1000					,projection_dim=32					,)
         UpperCAmelCase__							:				Tuple   				=  ClapTextModelWithProjection(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  RobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-roberta'''					,model_max_length=77							)

         UpperCAmelCase__							:				List[str]   				=  SpeechTaHifiGanConfig(
             model_in_dim=8					,sampling_rate=16000					,upsample_initial_channel=16					,upsample_rates=[2, 2]					,upsample_kernel_sizes=[4, 4]					,resblock_kernel_sizes=[3, 7]					,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]					,normalize_before=lowerCamelCase_					,)

         UpperCAmelCase__							:				List[Any]   				=  SpeechTaHifiGan(lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''vocoder''': vocoder,
         }
         return components




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=0							)			->			Optional[Any]:



         '''simple docstring'''



         if str(lowerCamelCase_							).startswith('''mps'''							):
            UpperCAmelCase__							:				List[str]   				=  torch.manual_seed(lowerCamelCase_							)
         else:
            UpperCAmelCase__							:				List[str]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  {
             '''prompt''': '''A hammer hitting a wooden surface''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
         }
         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         UpperCAmelCase__							:				Optional[int]   				=  self.get_dummy_components()
         UpperCAmelCase__							:				int   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  output.audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) == 256

         UpperCAmelCase__							:				Optional[int]   				=  audio[:10]
         UpperCAmelCase__							:				Dict   				=  np.array(
             [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]							)

         assert np.abs(audio_slice - expected_slice							).max() < 1e-2




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



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.get_dummy_components()
         UpperCAmelCase__							:				str   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  audioldm_pipe.to(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  3 * [inputs['''prompt''']]

         # forward
         UpperCAmelCase__							:				int   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  output.audios[0]

         UpperCAmelCase__							:				str   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  3 * [inputs.pop('''prompt'''							)]

         UpperCAmelCase__							:				int   				=  audioldm_pipe.tokenizer(
             lowerCamelCase_					,padding='''max_length'''					,max_length=audioldm_pipe.tokenizer.model_max_length					,truncation=lowerCamelCase_					,return_tensors='''pt'''					,)
         UpperCAmelCase__							:				Optional[Any]   				=  text_inputs['''input_ids'''].to(lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  audioldm_pipe.text_encoder(
             lowerCamelCase_					,)
         UpperCAmelCase__							:				List[Any]   				=  prompt_embeds.text_embeds
         # additional L_2 normalization over each hidden-state
         UpperCAmelCase__							:				Union[str, Any]   				=  F.normalize(lowerCamelCase_					,dim=-1							)

         UpperCAmelCase__							:				List[Any]   				=  prompt_embeds

         # forward
         UpperCAmelCase__							:				int   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  output.audios[0]

         assert np.abs(audio_a - audio_a							).max() < 1e-2




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  self.get_dummy_components()
         UpperCAmelCase__							:				List[str]   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  audioldm_pipe.to(lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  3 * ['''this is a negative prompt''']
         UpperCAmelCase__							:				Optional[int]   				=  negative_prompt
         UpperCAmelCase__							:				Any   				=  3 * [inputs['''prompt''']]

         # forward
         UpperCAmelCase__							:				int   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  output.audios[0]

         UpperCAmelCase__							:				Optional[Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  3 * [inputs.pop('''prompt'''							)]

         UpperCAmelCase__							:				Optional[Any]   				=  []
         for p in [prompt, negative_prompt]:
            UpperCAmelCase__							:				Optional[Any]   				=  audioldm_pipe.tokenizer(
                lowerCamelCase_					,padding='''max_length'''					,max_length=audioldm_pipe.tokenizer.model_max_length					,truncation=lowerCamelCase_					,return_tensors='''pt'''					,)
            UpperCAmelCase__							:				Tuple   				=  text_inputs['''input_ids'''].to(lowerCamelCase_							)

            UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe.text_encoder(
                lowerCamelCase_					,)
            UpperCAmelCase__							:				Optional[Any]   				=  text_embeds.text_embeds
            # additional L_2 normalization over each hidden-state
            UpperCAmelCase__							:				Dict   				=  F.normalize(lowerCamelCase_					,dim=-1							)

            embeds.append(lowerCamelCase_							)

         UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  embeds

         # forward
         UpperCAmelCase__							:				Any   				=  audioldm_pipe(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  output.audios[0]

         assert np.abs(audio_a - audio_a							).max() < 1e-2




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_dummy_components()
         UpperCAmelCase__							:				Tuple   				=  PNDMScheduler(skip_prk_steps=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  '''egg cracking'''
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe(**lowerCamelCase_					,negative_prompt=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  output.audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) == 256

         UpperCAmelCase__							:				Tuple   				=  audio[:10]
         UpperCAmelCase__							:				Tuple   				=  np.array(
             [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]							)

         assert np.abs(audio_slice - expected_slice							).max() < 1e-2




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



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__							:				int   				=  self.get_dummy_components()
         UpperCAmelCase__							:				Any   				=  PNDMScheduler(skip_prk_steps=lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  '''A hammer hitting a wooden surface'''

         # test num_waveforms_per_prompt=1 (default)
         UpperCAmelCase__							:				str   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=2							).audios

         assert audios.shape == (1, 256)

         # test num_waveforms_per_prompt=1 (default) for batch of prompts
         UpperCAmelCase__							:				Union[str, Any]   				=  2
         UpperCAmelCase__							:				int   				=  audioldm_pipe([prompt] * batch_size					,num_inference_steps=2							).audios

         assert audios.shape == (batch_size, 256)

         # test num_waveforms_per_prompt for single prompt
         UpperCAmelCase__							:				Any   				=  2
         UpperCAmelCase__							:				Dict   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=2					,num_waveforms_per_prompt=lowerCamelCase_							).audios

         assert audios.shape == (num_waveforms_per_prompt, 256)

         # test num_waveforms_per_prompt for batch of prompts
         UpperCAmelCase__							:				List[str]   				=  2
         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe(
             [prompt] * batch_size					,num_inference_steps=2					,num_waveforms_per_prompt=lowerCamelCase_							).audios

         assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_components()
         UpperCAmelCase__							:				str   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  audioldm_pipe.vocoder.config.sampling_rate

         UpperCAmelCase__							:				Optional[Any]   				=  self.get_dummy_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  audioldm_pipe(audio_length_in_s=0.016					,**lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  output.audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) / vocoder_sampling_rate == 0.016

         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe(audio_length_in_s=0.032					,**lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  output.audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) / vocoder_sampling_rate == 0.032




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self.get_dummy_components()
         UpperCAmelCase__							:				Dict   				=  AudioLDMPipeline(**lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[int]   				=  ['''hey''']

         UpperCAmelCase__							:				int   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=1							)
         UpperCAmelCase__							:				Tuple   				=  output.audios.shape
         assert audio_shape == (1, 256)

         UpperCAmelCase__							:				List[Any]   				=  audioldm_pipe.vocoder.config
         config.model_in_dim *= 2
         UpperCAmelCase__							:				Dict   				=  SpeechTaHifiGan(lowerCamelCase_							).to(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  audioldm_pipe(lowerCamelCase_					,num_inference_steps=1							)
         UpperCAmelCase__							:				Optional[int]   				=  output.audios.shape
         # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
         assert audio_shape == (1, 256)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase_							)




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



         '''simple docstring'''



         self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCamelCase_							)




      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()					,reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,)
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase_							)




@slow
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_="cpu"					,lowerCamelCase_=torch.floataa					,lowerCamelCase_=0							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  torch.Generator(device=lowerCamelCase_							).manual_seed(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.random.RandomState(lowerCamelCase_							).standard_normal((1, 8, 128, 16)							)
         UpperCAmelCase__							:				Optional[int]   				=  torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_					,dtype=lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  {
             '''prompt''': '''A hammer hitting a wooden surface''',
             '''latents''': latents,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 2.5,
         }
         return inputs




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''							)
         UpperCAmelCase__							:				Any   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				str   				=  self.get_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  25
         UpperCAmelCase__							:				Dict   				=  audioldm_pipe(**lowerCamelCase_							).audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) == 81920

         UpperCAmelCase__							:				Union[str, Any]   				=  audio[77230:77240]
         UpperCAmelCase__							:				Union[str, Any]   				=  np.array(
             [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]							)
         UpperCAmelCase__							:				List[Any]   				=  np.abs(expected_slice - audio_slice							).max()
         assert max_diff < 1e-2




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  AudioLDMPipeline.from_pretrained('''cvssp/audioldm'''							)
         UpperCAmelCase__							:				Tuple   				=  LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config							)
         UpperCAmelCase__							:				int   				=  audioldm_pipe.to(lowerCamelCase_							)
         audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_inputs(lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  audioldm_pipe(**lowerCamelCase_							).audios[0]

         assert audio.ndim == 1
         assert len(lowerCamelCase_							) == 81920

         UpperCAmelCase__							:				List[Any]   				=  audio[27780:27790]
         UpperCAmelCase__							:				Union[str, Any]   				=  np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]							)
         UpperCAmelCase__							:				Any   				=  np.abs(expected_slice - audio_slice							).max()
         assert max_diff < 3e-2





'''simple docstring'''

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

import numpy as np


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

UpperCamelCase__     :	List[Any]				=			None







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, 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=_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 __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				Union[str, Any]   				=  bool(qa['''answers''']['''text''']    )
   return qid_to_has_ans







def __UpperCamelCase(   _A					:				Dict    ):
   '''simple docstring'''

   def remove_articles(_A					:				Union[str, Any]    ):
      return ARTICLES_REGEX.sub(''' '''  ,			_A    )

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

   def remove_punc(_A					:				Optional[Any]    ):
      UpperCAmelCase__							:				Any   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Union[str, Any]    ):
      return text.lower()

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







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   if not s:
      return []
   return normalize_answer(_A    ).split()







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str    ):
   '''simple docstring'''
   return int(normalize_answer(_A    ) == normalize_answer(_A    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Tuple   				=  get_tokens(_A    )
   UpperCAmelCase__							:				Any   				=  collections.Counter(_A    ) & collections.Counter(_A    )
   UpperCAmelCase__							:				List[Any]   				=  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__							:				Optional[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Tuple   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  {}
   UpperCAmelCase__							:				Any   				=  {}
   for article in dataset:
      for p in article["paragraphs"]:
         for qa in p["qas"]:
            UpperCAmelCase__							:				str   				=  qa['''id''']
            UpperCAmelCase__							:				List[Any]   				=  [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__							:				Tuple   				=  ['''''']
            if qid not in preds:
               print(F'''Missing prediction for {qid}'''    )
               continue
            UpperCAmelCase__							:				Union[str, Any]   				=  preds[qid]
            # Take max over all gold answers
            UpperCAmelCase__							:				List[str]   				=  max(compute_exact(_A  ,			_A    ) for a in gold_answers    )
            UpperCAmelCase__							:				List[str]   				=  max(compute_fa(_A  ,			_A    ) for a in gold_answers    )
   return exact_scores, fa_scores







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  {}
   for qid, s in scores.items():
      UpperCAmelCase__							:				Dict   				=  na_probs[qid] > na_prob_thresh
      if pred_na:
         UpperCAmelCase__							:				Any   				=  float(not qid_to_has_ans[qid]    )
      else:
         UpperCAmelCase__							:				List[str]   				=  s
   return new_scores







def __UpperCamelCase(   _A					:				str  ,			_A					:				Optional[Any]  ,			_A					:				Any=None    ):
   '''simple docstring'''
   if not qid_list:
      UpperCAmelCase__							:				List[Any]   				=  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__							:				List[str]   				=  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 __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[Any]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   for k in new_eval:
      UpperCAmelCase__							:				List[str]   				=  new_eval[k]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Any  ,			_A					:				Optional[int]  ,			_A					:				int    ):
   '''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 __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Tuple  ,			_A					:				Any  ,			_A					:				Any  ,			_A					:				Tuple=None  ,			_A					:				Union[str, Any]=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  sorted(_A  ,			key=lambda _A    : na_probs[k]    )
   UpperCAmelCase__							:				Tuple   				=  0.0
   UpperCAmelCase__							:				Any   				=  1.0
   UpperCAmelCase__							:				Any   				=  0.0
   UpperCAmelCase__							:				Union[str, Any]   				=  [1.0]
   UpperCAmelCase__							:				int   				=  [0.0]
   UpperCAmelCase__							:				Optional[Any]   				=  0.0
   for i, qid in enumerate(_A    ):
      if qid_to_has_ans[qid]:
         true_pos += scores[qid]
      UpperCAmelCase__							:				Optional[Any]   				=  true_pos / float(i + 1    )
      UpperCAmelCase__							:				int   				=  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 __UpperCamelCase(   _A					:				Any  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Any  ,			_A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   if out_image_dir and not os.path.exists(_A    ):
      os.makedirs(_A    )
   UpperCAmelCase__							:				List[str]   				=  sum(1 for v in qid_to_has_ans.values() if v    )
   if num_true_pos == 0:
      return
   UpperCAmelCase__							:				Dict   				=  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__							:				Any   				=  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__							:				Tuple   				=  {k: float(_A    ) for k, v in qid_to_has_ans.items()}
   UpperCAmelCase__							:				Any   				=  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 __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Dict  ,			_A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   if not qid_list:
      return
   UpperCAmelCase__							:				Optional[Any]   				=  [na_probs[k] for k in qid_list]
   UpperCAmelCase__							:				Union[str, Any]   				=  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 __UpperCamelCase(   _A					:				List[Any]  ,			_A					:				List[str]  ,			_A					:				Optional[int]  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]    )
   UpperCAmelCase__							:				List[str]   				=  num_no_ans
   UpperCAmelCase__							:				Any   				=  cur_score
   UpperCAmelCase__							:				List[str]   				=  0.0
   UpperCAmelCase__							:				Dict   				=  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__							:				int   				=  scores[qid]
      else:
         if preds[qid]:
            UpperCAmelCase__							:				Any   				=  -1
         else:
            UpperCAmelCase__							:				Dict   				=  0
      cur_score += diff
      if cur_score > best_score:
         UpperCAmelCase__							:				Optional[Any]   				=  cur_score
         UpperCAmelCase__							:				Tuple   				=  na_probs[qid]
   return 1_0_0.0 * best_score / len(_A    ), best_thresh







def __UpperCamelCase(   _A					:				str  ,			_A					:				str  ,			_A					:				int  ,			_A					:				int  ,			_A					:				Tuple  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__				,      UpperCAmelCase__							:				Tuple   				=  find_best_thresh(_A  ,			_A  ,			_A  ,			_A    )
   UpperCAmelCase__							:				List[str]   				=  best_exact
   UpperCAmelCase__							:				Any   				=  exact_thresh
   UpperCAmelCase__							:				Dict   				=  best_fa
   UpperCAmelCase__							:				Dict   				=  fa_thresh







def __UpperCamelCase(   ):
   '''simple docstring'''
   with open(OPTS.data_file    ) as f:
      UpperCAmelCase__							:				Dict   				=  json.load(_A    )
      UpperCAmelCase__							:				str   				=  dataset_json['''data''']
   with open(OPTS.pred_file    ) as f:
      UpperCAmelCase__							:				Any   				=  json.load(_A    )
   if OPTS.na_prob_file:
      with open(OPTS.na_prob_file    ) as f:
         UpperCAmelCase__							:				Optional[Any]   				=  json.load(_A    )
   else:
      UpperCAmelCase__							:				Dict   				=  {k: 0.0 for k in preds}
   UpperCAmelCase__							:				int   				=  make_qid_to_has_ans(_A    )  # maps qid to True/False
   UpperCAmelCase__							:				Any   				=  [k for k, v in qid_to_has_ans.items() if v]
   UpperCAmelCase__							:				Optional[Any]   				=  [k for k, v in qid_to_has_ans.items() if not v]
   UpperCAmelCase__				,      UpperCAmelCase__							:				Union[str, Any]   				=  get_raw_scores(_A  ,			_A    )
   UpperCAmelCase__							:				Optional[int]   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Dict   				=  apply_no_ans_threshold(_A  ,			_A  ,			_A  ,			OPTS.na_prob_thresh    )
   UpperCAmelCase__							:				Union[str, Any]   				=  make_eval_dict(_A  ,			_A    )
   if has_ans_qids:
      UpperCAmelCase__							:				Optional[int]   				=  make_eval_dict(_A  ,			_A  ,			qid_list=_A    )
      merge_eval(_A  ,			_A  ,			'''HasAns'''    )
   if no_ans_qids:
      UpperCAmelCase__							:				Dict   				=  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__":
  UpperCamelCase__     :	Tuple				=			parse_args()
  if OPTS.out_image_dir:
    import matplotlib

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




