





'''simple docstring'''

import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__lowerCamelCase : int											=			"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__lowerCamelCase : Dict											=			direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__lowerCamelCase : Dict											=			re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__lowerCamelCase : List[str]											=			re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCamelCase : List[str]											=			re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase : Tuple											=			[
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,							lowerCAmelCase_							)
    return [m.group(0							) for m in matches]







def 							UpperCAmelCase_    (	):

    """simple docstring"""






    lowercase         = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    lowercase         = {
        config.replace("Config"		,							""							): model_type for model_type, config in config_maping_names.items()
    }

    # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
    lowercase         = collections.defaultdict(lowerCAmelCase_							)
    lowercase         = collections.defaultdict(lowerCAmelCase_							)
    lowercase         = collections.defaultdict(lowerCAmelCase_							)

    # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
    for attr_name in dir(lowerCAmelCase_							):
        lowercase         = None
        if _re_tf_models.match(lowerCAmelCase_							) is not None:
            lowercase         = tf_models
            lowercase         = _re_tf_models.match(lowerCAmelCase_							).groups()[0]
        elif _re_flax_models.match(lowerCAmelCase_							) is not None:
            lowercase         = flax_models
            lowercase         = _re_flax_models.match(lowerCAmelCase_							).groups()[0]
        elif _re_pt_models.match(lowerCAmelCase_							) is not None:
            lowercase         = pt_models
            lowercase         = _re_pt_models.match(lowerCAmelCase_							).groups()[0]

        if lookup_dict is not None:
            while len(lowerCAmelCase_							) > 0:
                if attr_name in model_prefix_to_model_type:
                    lowercase         = True
                    break
                # Try again after removing the last word in the name
                lowercase         = "".join(camel_case_split(lowerCAmelCase_							)[:-1]							)

    lowercase         = set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
    lowercase         = list(lowerCAmelCase_							)
    all_models.sort()

    lowercase         = {"model_type": all_models}
    lowercase         = [pt_models[t] for t in all_models]
    lowercase         = [tf_models[t] for t in all_models]
    lowercase         = [flax_models[t] for t in all_models]

    # Now let's use the auto-mapping names to make sure
    lowercase         = {}
    for t in all_models:
        if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
            lowercase         = "AutoProcessor"
        elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
            lowercase         = "AutoTokenizer"
        elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
            lowercase         = "AutoFeatureExtractor"
        else:
            # Default to AutoTokenizer if a model has nothing, for backward compatibility.
            lowercase         = "AutoTokenizer"

    lowercase         = [processors[t] for t in all_models]

    return pd.DataFrame(lowerCAmelCase_							)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = [
        transformers_module.models.auto.modeling_auto,
        transformers_module.models.auto.modeling_tf_auto,
        transformers_module.models.auto.modeling_flax_auto,
    ]
    for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
        lowercase         = [model_mapping, f'TF_{model_mapping}', f'FLAX_{model_mapping}']
        lowercase         = [auto_class, f'TF_{auto_class}', f'Flax_{auto_class}']
        # Loop through all three frameworks
        for module, cls, mapping in zip(lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):
            # The type of pipeline may not exist in this framework
            if not hasattr(lowerCAmelCase_		,							lowerCAmelCase_							):
                continue
            # First extract all model_names
            lowercase         = []
            for name in getattr(lowerCAmelCase_		,							lowerCAmelCase_							).values():
                if isinstance(lowerCAmelCase_		,							lowerCAmelCase_							):
                    model_names.append(lowerCAmelCase_							)
                else:
                    model_names.extend(list(lowerCAmelCase_							)							)

            # Add pipeline tag and auto model class for those models
            table.update({model_name: (pipeline_tag, cls) for model_name in model_names}							)

    return table







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

    """simple docstring"""






    lowercase         = get_frameworks_table()
    lowercase         = Dataset.from_pandas(lowerCAmelCase_							)

    lowercase         = hf_hub_download(
        "huggingface/transformers-metadata"		,							"pipeline_tags.json"		,							repo_type="dataset"		,							token=lowerCAmelCase_							)
    lowercase         = Dataset.from_json(lowerCAmelCase_							)
    lowercase         = {
        tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
        for i in range(len(lowerCAmelCase_							)							)
    }
    lowercase         = update_pipeline_and_auto_class_table(lowerCAmelCase_							)

    # Sort the model classes to avoid some nondeterministic updates to create false update commits.
    lowercase         = sorted(table.keys()							)
    lowercase         = pd.DataFrame(
        {
            "model_class": model_classes,
            "pipeline_tag": [table[m][0] for m in model_classes],
            "auto_class": [table[m][1] for m in model_classes],
        }							)
    lowercase         = Dataset.from_pandas(lowerCAmelCase_							)

    with tempfile.TemporaryDirectory() as tmp_dir:
        frameworks_dataset.to_json(os.path.join(lowerCAmelCase_		,							"frameworks.json"							)							)
        tags_dataset.to_json(os.path.join(lowerCAmelCase_		,							"pipeline_tags.json"							)							)

        if commit_sha is not None:
            lowercase         = (
                f'Update with commit {commit_sha}\n\nSee: '
                f'https://github.com/huggingface/transformers/commit/{commit_sha}'
            )
        else:
            lowercase         = "Update"

        upload_folder(
            repo_id="huggingface/transformers-metadata"		,							folder_path=lowerCAmelCase_		,							repo_type="dataset"		,							token=lowerCAmelCase_		,							commit_message=lowerCAmelCase_		,							)







def 							UpperCAmelCase_    (	):

    """simple docstring"""






    lowercase         = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
    lowercase         = transformers_module.pipelines.SUPPORTED_TASKS
    lowercase         = []
    for key in pipeline_tasks:
        if key not in in_table:
            lowercase         = pipeline_tasks[key]["pt"]
            if isinstance(lowerCAmelCase_		,							(list, tuple)							):
                lowercase         = model[0]
            lowercase         = model.__name__
            if model not in in_table.values():
                missing.append(lowerCAmelCase_							)

    if len(lowerCAmelCase_							) > 0:
        lowercase         = ", ".join(lowerCAmelCase_							)
        raise ValueError(
            "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
            f'`utils/update_metadata.py`: {msg}. Please add them!'							)


if __name__ == "__main__":
   __lowerCamelCase : Dict											=			argparse.ArgumentParser()
   parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
   parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
   parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
   __lowerCamelCase : Dict											=			parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)











'''simple docstring'''

import datasets

from .evaluate import evaluate


__lowerCamelCase : Optional[Any]											=			"\\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 : Any											=			"\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering 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"

__lowerCamelCase : Union[str, Any]											=			"\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the 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 Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"

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




		def 				UpperCAmelCase__   (self      :	Union[str, Any]				)					-> 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"				)},
						            "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]							,			A__      :	List[Any]							,			A__      :	List[str]				)					-> str:
						lowercase         = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
						lowercase         = [
						    {
						        "paragraphs": [
						            {
						                "qas": [
						                    {
						                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
						                        "id": ref["id"],
						                    }
						                    for ref in references
						                ]
						            }
						        ]
						    }
						]
						lowercase         = evaluate(dataset=A__							,			predictions=A__				)
						return score




