






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
_a    :   Tuple        =			"""src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
_a    :   str        =			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_a    :   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.
_a    :   Union[str, Any]        =			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_a    :   Dict        =			[
    ("""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 				snake_case__							(							UpperCAmelCase							:  int       ):
    lowerCAmelCase__   :int      							=				re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"    ,			UpperCAmelCase       )
    return [m.group(0       ) for m in matches]


def 				snake_case__							(							):
    lowerCAmelCase__   :Dict      							=				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    lowerCAmelCase__   :Dict      							=				{
        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.
    lowerCAmelCase__   :Dict      							=				collections.defaultdict(UpperCAmelCase       )
    lowerCAmelCase__   :Optional[int]      							=				collections.defaultdict(UpperCAmelCase       )
    lowerCAmelCase__   :int      							=				collections.defaultdict(UpperCAmelCase       )

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

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

    lowerCAmelCase__   :Optional[Any]      							=				set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
    lowerCAmelCase__   :Optional[int]      							=				list(UpperCAmelCase       )
    all_models.sort()

    lowerCAmelCase__   :Union[str, Any]      							=				{"model_type": all_models}
    lowerCAmelCase__   :List[Any]      							=				[pt_models[t] for t in all_models]
    lowerCAmelCase__   :int      							=				[tf_models[t] for t in all_models]
    lowerCAmelCase__   :Optional[Any]      							=				[flax_models[t] for t in all_models]

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

    lowerCAmelCase__   :Dict      							=				[processors[t] for t in all_models]

    return pd.DataFrame(UpperCAmelCase       )


def 				snake_case__							(							UpperCAmelCase							:  Any       ):
    lowerCAmelCase__   :Optional[Any]      							=				[
        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:
        lowerCAmelCase__   :Union[str, Any]      							=				[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
        lowerCAmelCase__   :Any      							=				[auto_class, F'''TF_{auto_class}''', F'''Flax_{auto_class}''']
        # Loop through all three frameworks
        for module, cls, mapping in zip(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       ):
            # The type of pipeline may not exist in this framework
            if not hasattr(UpperCAmelCase    ,			UpperCAmelCase       ):
                continue
            # First extract all model_names
            lowerCAmelCase__   :Any      							=				[]
            for name in getattr(UpperCAmelCase    ,			UpperCAmelCase       ).values():
                if isinstance(UpperCAmelCase    ,			UpperCAmelCase       ):
                    model_names.append(UpperCAmelCase       )
                else:
                    model_names.extend(list(UpperCAmelCase       )       )

            # 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 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :int      							=				get_frameworks_table()
    lowerCAmelCase__   :str      							=				Dataset.from_pandas(UpperCAmelCase       )

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

    # Sort the model classes to avoid some nondeterministic updates to create false update commits.
    lowerCAmelCase__   :Optional[Any]      							=				sorted(table.keys()       )
    lowerCAmelCase__   :Dict      							=				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],
        }       )
    lowerCAmelCase__   :Tuple      							=				Dataset.from_pandas(UpperCAmelCase       )

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

        if commit_sha is not None:
            lowerCAmelCase__   :Union[str, Any]      							=				(
                F'''Update with commit {commit_sha}\n\nSee: '''
                F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
            )
        else:
            lowerCAmelCase__   :Optional[Any]      							=				"Update"

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


def 				snake_case__							(							):
    lowerCAmelCase__   :Dict      							=				{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
    lowerCAmelCase__   :Optional[Any]      							=				transformers_module.pipelines.SUPPORTED_TASKS
    lowerCAmelCase__   :Optional[Any]      							=				[]
    for key in pipeline_tasks:
        if key not in in_table:
            lowerCAmelCase__   :Union[str, Any]      							=				pipeline_tasks[key]["pt"]
            if isinstance(UpperCAmelCase    ,			(list, tuple)       ):
                lowerCAmelCase__   :Optional[int]      							=				model[0]
            lowerCAmelCase__   :List[str]      							=				model.__name__
            if model not in in_table.values():
                missing.append(UpperCAmelCase       )

    if len(UpperCAmelCase       ) > 0:
        lowerCAmelCase__   :Union[str, Any]      							=				", ".join(UpperCAmelCase       )
        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__":
 _a    :   List[str]        =			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.""")
 _a    :   List[str]        =			parser.parse_args()

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











from __future__ import annotations

import math
from collections.abc import Callable


def 				snake_case__							(							UpperCAmelCase							:  Callable[[int | float], int | float]    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int = 1_0_0    ,			):
    lowerCAmelCase__   :int      							=				x_start
    lowerCAmelCase__   :int      							=				fnc(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				0.0

    for _ in range(UpperCAmelCase       ):
        # Approximates curve as a sequence of linear lines and sums their length
        lowerCAmelCase__   :Dict      							=				(x_end - x_start) / steps + xa
        lowerCAmelCase__   :Optional[Any]      							=				fnc(UpperCAmelCase       )
        length += math.hypot(xa - xa    ,			fxa - fxa       )

        # Increment step
        lowerCAmelCase__   :Union[str, Any]      							=				xa
        lowerCAmelCase__   :str      							=				fxa

    return length


if __name__ == "__main__":


 def 				snake_case__							(							UpperCAmelCase							:  int       ):
     return math.sin(1_0 * x       )

 print("""f(x) = sin(10 * x)""")
 print("""The length of the curve from x = -10 to x = 10 is:""")
 _a    :   Any        =			10
 while i <= 10_0000:
  print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
  i *= 10



