
"""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
_SCREAMING_SNAKE_CASE	       =      """src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
_SCREAMING_SNAKE_CASE	       =      re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
_SCREAMING_SNAKE_CASE	       =      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.
_SCREAMING_SNAKE_CASE	       =      re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_SCREAMING_SNAKE_CASE	       =      [
    ("""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   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,    SCREAMING_SNAKE_CASE )
							return [m.group(0 ) for m in matches]
def   __UpperCamelCase   (					)  ->     Optional[int]:







							"""simple docstring"""


							__snake_case     			=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							__snake_case     			=       {
							    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.
							__snake_case     			=       collections.defaultdict(SCREAMING_SNAKE_CASE )
							__snake_case     			=       collections.defaultdict(SCREAMING_SNAKE_CASE )
							__snake_case     			=       collections.defaultdict(SCREAMING_SNAKE_CASE )

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

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

							__snake_case     			=       set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
							__snake_case     			=       list(SCREAMING_SNAKE_CASE )
							all_models.sort()

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

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

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

							return pd.DataFrame(SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


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

            # 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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       get_frameworks_table()
							__snake_case     			=       Dataset.from_pandas(SCREAMING_SNAKE_CASE )

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

							# Sort the model classes to avoid some nondeterministic updates to create false update commits.
							__snake_case     			=       sorted(table.keys() )
							__snake_case     			=       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],
							    } )
							__snake_case     			=       Dataset.from_pandas(SCREAMING_SNAKE_CASE )

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

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

														upload_folder(
														    repo_id="huggingface/transformers-metadata"							,    folder_path=SCREAMING_SNAKE_CASE							,    repo_type="dataset"							,    token=SCREAMING_SNAKE_CASE							,    commit_message=SCREAMING_SNAKE_CASE							,    )
def   __UpperCamelCase   (					)  ->     Any:







							"""simple docstring"""


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

							if len(SCREAMING_SNAKE_CASE ) > 0:
														__snake_case     			=       ", ".join(SCREAMING_SNAKE_CASE )
														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__":
		_SCREAMING_SNAKE_CASE	       =      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.""")
		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()

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


"""simple docstring"""







import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


							__snake_case     			=       []
							for line in lines:
														__snake_case     			=       re.sub(r"#.*"							,    ""							,    SCREAMING_SNAKE_CASE )  # remove comments
														if line:
																					filtered_lines.append(SCREAMING_SNAKE_CASE )
							__snake_case     			=       "\n".join(SCREAMING_SNAKE_CASE )

							# Make a hash from all this code
							__snake_case     			=       full_str.encode("utf-8" )
							return shaaaa(SCREAMING_SNAKE_CASE ).hexdigest()


# get importable module names and hash for caching
_SCREAMING_SNAKE_CASE	       =      {
    """csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    """json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    """pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    """parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    """arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    """text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    """imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    """audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
_SCREAMING_SNAKE_CASE	       =      {
    """.csv""": ("""csv""", {}),
    """.tsv""": ("""csv""", {"""sep""": """\t"""}),
    """.json""": ("""json""", {}),
    """.jsonl""": ("""json""", {}),
    """.parquet""": ("""parquet""", {}),
    """.arrow""": ("""arrow""", {}),
    """.txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_SCREAMING_SNAKE_CASE	       =      {"""imagefolder""", """audiofolder"""}

# Used to filter data files based on extensions given a module name
_SCREAMING_SNAKE_CASE	       =      {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
		_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")



_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
