






import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


__snake_case      :      Dict             =       (
    '''https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'''
)


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







def 		_UpperCamelCase				(      )					->				int:

      """simple docstring"""



      lowerCAmelCase__        =  '''https://pypi.org/pypi/diffusers/json'''
      lowerCAmelCase__        =  json.loads(request.urlopen(A_						).read()						)['''releases'''].keys()
      return sorted(A_      ,    key=lambda UpperCamelCase_						: version.Version(A_						)						)







def 		_UpperCamelCase				(      )					->				Tuple:

      """simple docstring"""



      if HF_MODULES_CACHE in sys.path:
            return

      sys.path.append(A_						)
      os.makedirs(A_      ,    exist_ok=A_						)
      lowerCAmelCase__        =  Path(A_						) / '''__init__.py'''
      if not init_path.exists():
            init_path.touch()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict						)					->				Any:

      """simple docstring"""



      init_hf_modules()
      lowerCAmelCase__        =  Path(A_						) / name
      # If the parent module does not exist yet, recursively create it.
      if not dynamic_module_path.parent.exists():
            create_dynamic_module(dynamic_module_path.parent						)
      os.makedirs(A_      ,    exist_ok=A_						)
      lowerCAmelCase__        =  dynamic_module_path / '''__init__.py'''
      if not init_path.exists():
            init_path.touch()







def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				Tuple:

      """simple docstring"""



      with open(A_      ,    'r'      ,    encoding='utf-8'						) as f:
            lowerCAmelCase__        =  f.read()

      # Imports of the form `import .xxx`
      lowerCAmelCase__        =  re.findall('^\s*import\s+\.(\S+)\s*$'      ,    A_      ,    flags=re.MULTILINE						)
      # Imports of the form `from .xxx import yyy`
      relative_imports += re.findall('^\s*from\s+\.(\S+)\s+import'      ,    A_      ,    flags=re.MULTILINE						)
      # Unique-ify
      return list(set(A_						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any						)					->				str:

      """simple docstring"""



      lowerCAmelCase__        =  False
      lowerCAmelCase__        =  [module_file]
      lowerCAmelCase__        =  []

      # Let's recurse through all relative imports
      while not no_change:
            lowerCAmelCase__        =  []
            for f in files_to_check:
                  new_imports.extend(get_relative_imports(A_						)						)

            lowerCAmelCase__        =  Path(A_						).parent
            lowerCAmelCase__        =  [str(module_path / m						) for m in new_imports]
            lowerCAmelCase__        =  [f for f in new_import_files if f not in all_relative_imports]
            lowerCAmelCase__        =  [F"{f}.py" for f in new_import_files]

            lowerCAmelCase__        =  len(A_						) == 0
            all_relative_imports.extend(A_						)

      return all_relative_imports







def 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]						)					->				Optional[int]:

      """simple docstring"""



      with open(A_      ,    'r'      ,    encoding='utf-8'						) as f:
            lowerCAmelCase__        =  f.read()

      # Imports of the form `import xxx`
      lowerCAmelCase__        =  re.findall('^\s*import\s+(\S+)\s*$'      ,    A_      ,    flags=re.MULTILINE						)
      # Imports of the form `from xxx import yyy`
      imports += re.findall('^\s*from\s+(\S+)\s+import'      ,    A_      ,    flags=re.MULTILINE						)
      # Only keep the top-level module
      lowerCAmelCase__        =  [imp.split('.'						)[0] for imp in imports if not imp.startswith('.'						)]

      # Unique-ify and test we got them all
      lowerCAmelCase__        =  list(set(A_						)						)
      lowerCAmelCase__        =  []
      for imp in imports:
            try:
                  importlib.import_module(A_						)
            except ImportError:
                  missing_packages.append(A_						)

      if len(A_						) > 0:
            raise ImportError(
                'This modeling file requires the following packages that were not found in your environment: '
                F"{', '.join(A_						)}. Run `pip install {' '.join(A_						)}`"						)

      return get_relative_imports(A_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Any						)					->				Optional[int]:

      """simple docstring"""



      lowerCAmelCase__        =  module_path.replace(os.path.sep      ,    '.'						)
      lowerCAmelCase__        =  importlib.import_module(A_						)

      if class_name is None:
            return find_pipeline_class(A_						)
      return getattr(A_      ,    A_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict						)					->				Any:

      """simple docstring"""



      from ..pipelines import DiffusionPipeline

      lowerCAmelCase__        =  dict(inspect.getmembers(A_      ,    inspect.isclass						)						)

      lowerCAmelCase__        =  None
      for cls_name, cls in cls_members.items():
            if (
                cls_name != DiffusionPipeline.__name__
                and issubclass(cls      ,    A_						)
                and cls.__module__.split('.'						)[0] != "diffusers"
            ):
                  if pipeline_class is not None:
                        raise ValueError(
                            F"Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"
                            F" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"
                            F" {loaded_module}."						)
                  lowerCAmelCase__        =  cls

      return pipeline_class







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[int] = None      ,    UpperCamelCase_    :   Any = False      ,    UpperCamelCase_    :   List[Any] = False      ,    UpperCamelCase_    :   List[Any] = None      ,    UpperCamelCase_    :   int = None      ,    UpperCamelCase_    :   str = None      ,    UpperCamelCase_    :   Optional[Any] = False      ,    )					->				Union[str, Any]:

      """simple docstring"""



      lowerCAmelCase__        =  str(A_						)

      lowerCAmelCase__        =  os.path.join(A_      ,    A_						)

      if os.path.isfile(A_						):
            lowerCAmelCase__        =  module_file_or_url
            lowerCAmelCase__        =  '''local'''
      elif pretrained_model_name_or_path.count('/'						) == 0:
            lowerCAmelCase__        =  get_diffusers_versions()
            # cut ".dev0"
            lowerCAmelCase__        =  '''v''' + '''.'''.join(__version__.split('.'						)[:3]						)

            # retrieve github version that matches
            if revision is None:
                  lowerCAmelCase__        =  latest_version if latest_version[1:] in available_versions else '''main'''
                  logger.info(F"Defaulting to latest_version: {revision}."						)
            elif revision in available_versions:
                  lowerCAmelCase__        =  F"v{revision}"
            elif revision == "main":
                  lowerCAmelCase__        =  revision
            else:
                  raise ValueError(
                      F"`custom_revision`: {revision} does not exist. Please make sure to choose one of"
                      F" {', '.join(available_versions + ['main']						)}."						)

            # community pipeline on GitHub
            lowerCAmelCase__        =  COMMUNITY_PIPELINES_URL.format(revision=A_      ,    pipeline=A_						)
            try:
                  lowerCAmelCase__        =  cached_download(
                      A_      ,    cache_dir=A_      ,    force_download=A_      ,    proxies=A_      ,    resume_download=A_      ,    local_files_only=A_      ,    use_auth_token=A_      ,    )
                  lowerCAmelCase__        =  '''git'''
                  lowerCAmelCase__        =  pretrained_model_name_or_path + '''.py'''
            except EnvironmentError:
                  logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."						)
                  raise
      else:
            try:
                  # Load from URL or cache if already cached
                  lowerCAmelCase__        =  hf_hub_download(
                      A_      ,    A_      ,    cache_dir=A_      ,    force_download=A_      ,    proxies=A_      ,    resume_download=A_      ,    local_files_only=A_      ,    use_auth_token=A_      ,    )
                  lowerCAmelCase__        =  os.path.join('local'      ,    '--'.join(pretrained_model_name_or_path.split('/'						)						)						)
            except EnvironmentError:
                  logger.error(F"Could not locate the {module_file} inside {pretrained_model_name_or_path}."						)
                  raise

    # Check we have all the requirements in our environment
      lowerCAmelCase__        =  check_imports(A_						)

      # Now we move the module inside our cached dynamic modules.
      lowerCAmelCase__        =  DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
      create_dynamic_module(A_						)
      lowerCAmelCase__        =  Path(A_						) / full_submodule
      if submodule == "local" or submodule == "git":
            # We always copy local files (we could hash the file to see if there was a change, and give them the name of
            # that hash, to only copy when there is a modification but it seems overkill for now).
            # The only reason we do the copy is to avoid putting too many folders in sys.path.
            shutil.copy(A_      ,    submodule_path / module_file						)
            for module_needed in modules_needed:
                  lowerCAmelCase__        =  F"{module_needed}.py"
                  shutil.copy(os.path.join(A_      ,    A_						)      ,    submodule_path / module_needed						)
      else:
            # Get the commit hash
            # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
            if isinstance(A_      ,    A_						):
                  lowerCAmelCase__        =  use_auth_token
            elif use_auth_token is True:
                  lowerCAmelCase__        =  HfFolder.get_token()
            else:
                  lowerCAmelCase__        =  None

            lowerCAmelCase__        =  model_info(A_      ,    revision=A_      ,    token=A_						).sha

            # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
            # benefit of versioning.
            lowerCAmelCase__        =  submodule_path / commit_hash
            lowerCAmelCase__        =  full_submodule + os.path.sep + commit_hash
            create_dynamic_module(A_						)

            if not (submodule_path / module_file).exists():
                  shutil.copy(A_      ,    submodule_path / module_file						)
            # Make sure we also have every file with relative
            for module_needed in modules_needed:
                  if not (submodule_path / module_needed).exists():
                        get_cached_module_file(
                            A_      ,    F"{module_needed}.py"      ,    cache_dir=A_      ,    force_download=A_      ,    resume_download=A_      ,    proxies=A_      ,    use_auth_token=A_      ,    revision=A_      ,    local_files_only=A_      ,    )
      return os.path.join(A_      ,    A_						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Tuple = None      ,    UpperCamelCase_    :   int = None      ,    UpperCamelCase_    :   List[str] = False      ,    UpperCamelCase_    :   int = False      ,    UpperCamelCase_    :   Tuple = None      ,    UpperCamelCase_    :   Union[str, Any] = None      ,    UpperCamelCase_    :   Any = None      ,    UpperCamelCase_    :   Union[str, Any] = False      ,    **UpperCamelCase_    :   int      ,    )					->				Union[str, Any]:

      """simple docstring"""



      lowerCAmelCase__        =  get_cached_module_file(
          A_      ,    A_      ,    cache_dir=A_      ,    force_download=A_      ,    resume_download=A_      ,    proxies=A_      ,    use_auth_token=A_      ,    revision=A_      ,    local_files_only=A_      ,    )
      return get_class_in_module(A_      ,    final_module.replace('.py'      ,    ''						)						)













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

import numpy as np


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

__snake_case      :      List[Any]             =       None







def 		_UpperCamelCase				(      )					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  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=UpperCamelCase_      ,    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=UpperCamelCase_      ,    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				(      UpperCamelCase_    :   Dict						)					->				List[str]:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]						)					->				Any:

						"""simple docstring"""



						def remove_articles(UpperCamelCase_    :   Optional[int]						):
												return ARTICLES_REGEX.sub(' '      ,    UpperCamelCase_						)

						def white_space_fix(UpperCamelCase_    :   Any						):
												return " ".join(text.split()						)

						def remove_punc(UpperCamelCase_    :   Union[str, Any]						):
												lowerCAmelCase__        =  set(string.punctuation						)
												return "".join(ch for ch in text if ch not in exclude						)

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

						return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase_						)						)						)						)







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

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   int      ,    UpperCamelCase_    :   List[str]						)					->				Tuple:

						"""simple docstring"""



						return int(normalize_answer(UpperCamelCase_						) == normalize_answer(UpperCamelCase_						)						)







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   List[Any]						)					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  get_tokens(UpperCamelCase_						)
						lowerCAmelCase__        =  get_tokens(UpperCamelCase_						)
						lowerCAmelCase__        =  collections.Counter(UpperCamelCase_						) & collections.Counter(UpperCamelCase_						)
						lowerCAmelCase__        =  sum(common.values()						)
						if len(UpperCamelCase_						) == 0 or len(UpperCamelCase_						) == 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
						lowerCAmelCase__        =  1.0 * num_same / len(UpperCamelCase_						)
						lowerCAmelCase__        =  1.0 * num_same / len(UpperCamelCase_						)
						lowerCAmelCase__        =  (2 * precision * recall) / (precision + recall)
						return fa







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Optional[int]						)					->				List[Any]:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   List[Any]						)					->				str:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   str=None						)					->				Union[str, Any]:

						"""simple docstring"""



						if not qid_list:
												lowerCAmelCase__        =  len(UpperCamelCase_						)
												return collections.OrderedDict(
												    [
												        ('exact', 100.0 * sum(exact_scores.values()						) / total),
												        ('f1', 100.0 * sum(fa_scores.values()						) / total),
												        ('total', total),
												    ]						)
						else:
												lowerCAmelCase__        =  len(UpperCamelCase_						)
												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 		_UpperCamelCase				(      UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Tuple						)					->				Tuple:

						"""simple docstring"""



						for k in new_eval:
												lowerCAmelCase__        =  new_eval[k]







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[int]						)					->				int:

						"""simple docstring"""



						plt.step(UpperCamelCase_      ,    UpperCamelCase_      ,    color='b'      ,    alpha=0.2      ,    where='post'						)
						plt.fill_between(UpperCamelCase_      ,    UpperCamelCase_      ,    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(UpperCamelCase_						)
						plt.savefig(UpperCamelCase_						)
						plt.clf()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   List[str]=None      ,    UpperCamelCase_    :   Any=None						)					->				List[str]:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   Tuple						)					->				Tuple:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Dict      ,    UpperCamelCase_    :   Dict						)					->				int:

						"""simple docstring"""



						if not qid_list:
												return
						lowerCAmelCase__        =  [na_probs[k] for k in qid_list]
						lowerCAmelCase__        =  np.ones_like(UpperCamelCase_						) / float(len(UpperCamelCase_						)						)
						plt.hist(UpperCamelCase_      ,    weights=UpperCamelCase_      ,    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(UpperCamelCase_      ,    F"na_prob_hist_{name}.png"						)						)
						plt.clf()







def 		_UpperCamelCase				(      UpperCamelCase_    :   Any      ,    UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   str						)					->				int:

						"""simple docstring"""



						lowerCAmelCase__        =  sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]						)
						lowerCAmelCase__        =  num_no_ans
						lowerCAmelCase__        =  cur_score
						lowerCAmelCase__        =  0.0
						lowerCAmelCase__        =  sorted(UpperCamelCase_      ,    key=lambda UpperCamelCase_						: na_probs[k]						)
						for i, qid in enumerate(UpperCamelCase_						):
												if qid not in scores:
																		continue
												if qid_to_has_ans[qid]:
																		lowerCAmelCase__        =  scores[qid]
												else:
																		if preds[qid]:
																								lowerCAmelCase__        =  -1
																		else:
																								lowerCAmelCase__        =  0
												cur_score += diff
												if cur_score > best_score:
																		lowerCAmelCase__        =  cur_score
																		lowerCAmelCase__        =  na_probs[qid]
						return 100.0 * best_score / len(UpperCamelCase_						), best_thresh







def 		_UpperCamelCase				(      UpperCamelCase_    :   List[str]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   str      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   List[str]						)					->				str:

						"""simple docstring"""



						lowerCAmelCase__  ,    lowerCAmelCase__        =  find_best_thresh(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						lowerCAmelCase__  ,    lowerCAmelCase__        =  find_best_thresh(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_						)
						lowerCAmelCase__        =  best_exact
						lowerCAmelCase__        =  exact_thresh
						lowerCAmelCase__        =  best_fa
						lowerCAmelCase__        =  fa_thresh







def 		_UpperCamelCase				(      )					->				Dict:

						"""simple docstring"""



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





