
"""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     lowerCAmelCase__ (		)	->Any:
			__lowercase						      =					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"]:
						__lowercase						      =					getattr(transformers.models.auto ,      lowercase__ ,      lowercase__			)
						if module is None:
									continue
						# all mappings in a single auto modeling file
						__lowercase						      =					[
						    x
						    for x in dir(lowercase__			)
						    if x.endswith("_MAPPING_NAMES"			)
						    and (x.startswith("MODEL_"			) or x.startswith("TF_MODEL_"			) or x.startswith("FLAX_MODEL_"			))
						]
						for name in mapping_names:
									__lowercase						      =					getattr(lowercase__ ,      lowercase__			)
									if mapping is not None:
												for v in mapping.values():
															if isinstance(lowercase__ ,      (list, tuple)			):
																		model_names.update(lowercase__			)
															elif isinstance(lowercase__ ,      lowercase__			):
																		model_names.add(lowercase__			)

			return sorted(lowercase__			)



def     lowerCAmelCase__ (		)	->Any:
			# All model names defined in auto mappings
			__lowercase						      =					set(get_all_model_names()			)

			with open("tests/utils/tiny_model_summary.json"			) as fp:
						__lowercase						      =					json.load(lowercase__			)
			__lowercase						      =					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.
			__lowercase						      =					model_names.difference(lowercase__			)
			for model_name in copy.copy(lowercase__			):
						if not model_name.startswith("TF"			) and F'''TF{model_name}''' in not_on_hub:
									tiny_models_names.remove(lowercase__			)
						elif model_name.startswith("TF"			) and model_name[2:] in not_on_hub:
									tiny_models_names.remove(lowercase__			)

			return sorted(lowercase__			)



def     lowerCAmelCase__ (		__magic_name__			)	->Optional[Any]:
			__lowercase						      =					COMPOSITE_MODELS.values()

			# All tiny model base names on Hub
			__lowercase						      =					get_all_model_names()
			__lowercase						      =					hf_api.list_models(
			    filter=ModelFilter(
			        author="hf-internal-testing" ,      )			)
			__lowercase						      =					set()
			for x in models:
						__lowercase						      =					x.modelId
						__lowercase      ,				__lowercase						      =					model.split("/"			)
						if not model.startswith("tiny-random-"			):
									continue
						__lowercase						      =					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(lowercase__			)

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

						__lowercase						      =					sorted(content["tokenizer_classes"]			)
						__lowercase						      =					sorted(content["processor_classes"]			)
						__lowercase						      =					sorted(content["model_classes"]			)

						__lowercase						      =					content
						with open(os.path.join(lowercase__ ,      "hub_tiny_model_summary.json"			) ,      "w"			) as fp:
									json.dump(lowercase__ ,      lowercase__ ,      ensure_ascii=lowercase__ ,      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,
					)







"""simple docstring"""






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,
)


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

_lowercase							 =							'''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

_lowercase							 =							'''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'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}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,      _KWARGS_DESCRIPTION					)
class 				__a    (   datasets.Metric					):



							'''simple docstring'''







							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=1.0					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					{p["id"]: p["no_answer_probability"] for p in predictions}
										__lowercase						      =					[{"paragraphs": [{"qas": references}]}]
										__lowercase						      =					{p["id"]: p["prediction_text"] for p in predictions}

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

										__lowercase      ,				__lowercase						      =					get_raw_scores(_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					apply_no_ans_threshold(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					)
										__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase					)

										if has_ans_qids:
													__lowercase						      =					make_eval_dict(_lowerCamelCase     ,						_lowerCamelCase     ,						qid_list=_lowerCamelCase					)
													merge_eval(_lowerCamelCase     ,						_lowerCamelCase     ,						"HasAns"					)
										if no_ans_qids:
													__lowercase						      =					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					)





