




'''simple docstring'''





import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			set()
   # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
   for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
      SCREAMING_SNAKE_CASE__      :					Any							    =			getattr(transformers.models.auto						,  snake_case						,  snake_case )
      if module is None:
         continue
      # all mappings in a single auto modeling file
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			[
          x
          for x in dir(snake_case )
          if x.endswith('''_MAPPING_NAMES''' )
          and (x.startswith('''MODEL_''' ) or x.startswith('''TF_MODEL_''' ) or x.startswith('''FLAX_MODEL_''' ))
      ]
      for name in mapping_names:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			getattr(snake_case						,  snake_case )
         if mapping is not None:
            for v in mapping.values():
               if isinstance(snake_case						,  (list, tuple) ):
                  model_names.update(snake_case )
               elif isinstance(snake_case						,  snake_case ):
                  model_names.add(snake_case )

   return sorted(snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			set(get_all_model_names() )

   with open('''tests/utils/tiny_model_summary.json''' ) as fp:
      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			json.load(snake_case )
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			set()
   for model_base_name in tiny_model_info:
      tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes'''] )

   # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
   SCREAMING_SNAKE_CASE__      :					List[str]							    =			model_names.difference(snake_case )
   for model_name in copy.copy(snake_case ):
      if not model_name.startswith('''TF''' ) and f"""TF{model_name}""" in not_on_hub:
         tiny_models_names.remove(snake_case )
      elif model_name.startswith('''TF''' ) and model_name[2:] in not_on_hub:
         tiny_models_names.remove(snake_case )

   return sorted(snake_case )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			COMPOSITE_MODELS.values()

   # All tiny model base names on Hub
   SCREAMING_SNAKE_CASE__      :					Any							    =			get_all_model_names()
   SCREAMING_SNAKE_CASE__      :					int							    =			hf_api.list_models(
       filter=ModelFilter(
           author='''hf-internal-testing'''						,  ) )
   SCREAMING_SNAKE_CASE__      :					int							    =			set()
   for x in models:
      SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			x.modelId
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			model.split('''/''' )
      if not model.startswith('''tiny-random-''' ):
         continue
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			model.replace('''tiny-random-'''						,  '''''' )
      if not model[0].isupper():
         continue
      if model not in model_names and model not in special_models:
         continue
      _models.add(snake_case )

   SCREAMING_SNAKE_CASE__      :					List[Any]							    =			sorted(_models )
   # All tiny model names on Hub
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			{}
   for model in models:
      SCREAMING_SNAKE_CASE__      :					str							    =			f"""hf-internal-testing/tiny-random-{model}"""
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			model.split('''-''' )[0]
      try:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			hf_api.repo_info(snake_case )
         SCREAMING_SNAKE_CASE__      :					str							    =			{
             '''tokenizer_classes''': set(),
             '''processor_classes''': set(),
             '''model_classes''': set(),
             '''sha''': repo_info.sha,
         }
      except Exception:
         continue
      try:
         time.sleep(1 )
         SCREAMING_SNAKE_CASE__      :					Any							    =			AutoTokenizer.from_pretrained(snake_case )
         content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__ )
      except Exception:
         pass
      try:
         time.sleep(1 )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			AutoTokenizer.from_pretrained(snake_case						,  use_fast=snake_case )
         content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__ )
      except Exception:
         pass
      try:
         time.sleep(1 )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			AutoImageProcessor.from_pretrained(snake_case )
         content["processor_classes"].add(img_p.__class__.__name__ )
      except Exception:
         pass
      try:
         time.sleep(1 )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			AutoFeatureExtractor.from_pretrained(snake_case )
         if not isinstance(snake_case						,  snake_case ):
            content["processor_classes"].add(feat_p.__class__.__name__ )
      except Exception:
         pass
      try:
         time.sleep(1 )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			getattr(snake_case						,  snake_case )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model_class.from_pretrained(snake_case )
         content["model_classes"].add(m.__class__.__name__ )
      except Exception:
         pass
      try:
         time.sleep(1 )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			getattr(snake_case						,  f"""TF{model}""" )
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model_class.from_pretrained(snake_case )
         content["model_classes"].add(m.__class__.__name__ )
      except Exception:
         pass

      SCREAMING_SNAKE_CASE__      :					Any							    =			sorted(content['''tokenizer_classes'''] )
      SCREAMING_SNAKE_CASE__      :					str							    =			sorted(content['''processor_classes'''] )
      SCREAMING_SNAKE_CASE__      :					Any							    =			sorted(content['''model_classes'''] )

      SCREAMING_SNAKE_CASE__      :					List[Any]							    =			content
      with open(os.path.join(snake_case						,  '''hub_tiny_model_summary.json''' )						,  '''w''' ) as fp:
         json.dump(snake_case						,  snake_case						,  ensure_ascii=snake_case						,  indent=4 )


if __name__ == "__main__":
       lowercase__								=						argparse.ArgumentParser()
       parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
       lowercase__								=						parser.parse_args()

       # This has to be `spawn` to avoid hanging forever!
       multiprocessing.set_start_method('''spawn''')

       lowercase__								=						'tiny_models'
       lowercase__								=						True
       lowercase__								=						None
       lowercase__								=						get_tiny_model_names_from_repo()
       lowercase__								=						True
       lowercase__								=						True
       lowercase__								=						'hf-internal-testing'

       create_tiny_models(
           output_path,
           all,
           model_types,
           models_to_skip,
           no_check,
           upload,
           organization,
           token=os.environ.get('''TOKEN''', None),
           num_workers=args.num_workers,
       )







import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


__lowerCAmelCase						:							Optional[int]			     =						'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

__lowerCAmelCase						:							str			     =						'\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n'

__lowerCAmelCase						:							Optional[Any]			     =						'\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer \'id\' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - \'id\': id of the question-answer pair (see above),\n            - \'answers\': a list of Dict {\'text\': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    \'exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\n    \'total\': Number of score considered\n    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'HasAns_total\': Number of score considered\n    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)\n    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer\n    \'NoAns_total\': Number of score considered\n    \'best_exact\': Best exact match (with varying threshold)\n    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match\n    \'best_f1\': Best F1 (with varying threshold)\n    \'best_f1_thresh\': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_v2_metric = datasets.load_metric("squad_v2")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,			_KWARGS_DESCRIPTION  )
class        snake_case__				(datasets.Metric  ):
				"""simple docstring"""



				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Union[str, Any]:
							return datasets.MetricInfo(
							    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
							        {
							            "predictions": {
							                "id": datasets.Value("string"				),
							                "prediction_text": datasets.Value("string"				),
							                "no_answer_probability": datasets.Value("float32"				),
							            },
							            "references": {
							                "id": datasets.Value("string"				),
							                "answers": datasets.features.Sequence(
							                    {"text": datasets.Value("string"				), "answer_start": datasets.Value("int32"				)}				),
							            },
							        }				)   , codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"]   , )





				def 				__UpperCAmelCase				( self							:	List[str]   , __lowerCamelCase							:	Tuple   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Dict=1.0				)   ->	List[str]:
							a       =     {p["id"]: p["no_answer_probability"] for p in predictions}
							a       =     [{"paragraphs": [{"qas": references}]}]
							a       =     {p["id"]: p["prediction_text"] for p in predictions}

							a       =     make_qid_to_has_ans(__lowerCamelCase				)  # maps qid to True/False
							a       =     [k for k, v in qid_to_has_ans.items() if v]
							a       =     [k for k, v in qid_to_has_ans.items() if not v]

							a       ,						a       =     get_raw_scores(__lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     apply_no_ans_threshold(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase				)

							if has_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "HasAns"				)
							if no_ans_qids:
										a       =     make_eval_dict(__lowerCamelCase   , __lowerCamelCase   , qid_list=__lowerCamelCase				)
										merge_eval(__lowerCamelCase   , __lowerCamelCase   , "NoAns"				)
							find_all_best_thresh(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				)
							return dict(__lowerCamelCase				)






