



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
UpperCAmelCase__     :   str     = '''src/transformers'''


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCAmelCase__     :   Optional[Any]     = [
    ('''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 							A			(  _UpperCAmelCase       :     int					)       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'							,     _UpperCAmelCase					)
			return [m.group(0					) for m in matches]





def 							A			(  )       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			_UpperCAmelCase	 =     {
			    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.
			_UpperCAmelCase	 =     collections.defaultdict(_UpperCAmelCase					)
			_UpperCAmelCase	 =     collections.defaultdict(_UpperCAmelCase					)
			_UpperCAmelCase	 =     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					):
						_UpperCAmelCase	 =     None
						if _re_tf_models.match(_UpperCAmelCase					) is not None:
									_UpperCAmelCase	 =     tf_models
									_UpperCAmelCase	 =     _re_tf_models.match(_UpperCAmelCase					).groups()[0]
						elif _re_flax_models.match(_UpperCAmelCase					) is not None:
									_UpperCAmelCase	 =     flax_models
									_UpperCAmelCase	 =     _re_flax_models.match(_UpperCAmelCase					).groups()[0]
						elif _re_pt_models.match(_UpperCAmelCase					) is not None:
									_UpperCAmelCase	 =     pt_models
									_UpperCAmelCase	 =     _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:
															_UpperCAmelCase	 =     True
															break
												# Try again after removing the last word in the name
												_UpperCAmelCase	 =     ''.join(camel_case_split(_UpperCAmelCase					)[:-1]					)

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

			_UpperCAmelCase	 =     {'model_type': all_models}
			_UpperCAmelCase	 =     [pt_models[t] for t in all_models]
			_UpperCAmelCase	 =     [tf_models[t] for t in all_models]
			_UpperCAmelCase	 =     [flax_models[t] for t in all_models]

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

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

			return pd.DataFrame(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     Tuple					)       ->				List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     [
			    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:
						_UpperCAmelCase	 =     [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
						_UpperCAmelCase	 =     [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
									_UpperCAmelCase	 =     []
									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 							A			(  _UpperCAmelCase       :     Any							,     _UpperCAmelCase       :     Any					)       ->				Any:







			'''simple docstring'''
			_UpperCAmelCase	 =     get_frameworks_table()
			_UpperCAmelCase	 =     Dataset.from_pandas(_UpperCAmelCase					)

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

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			_UpperCAmelCase	 =     sorted(table.keys()					)
			_UpperCAmelCase	 =     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],
			    }					)
			_UpperCAmelCase	 =     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:
									_UpperCAmelCase	 =     (
									    F"Update with commit {commit_sha}\n\nSee: "
									    F"https://github.com/huggingface/transformers/commit/{commit_sha}"
									)
						else:
									_UpperCAmelCase	 =     'Update'

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





def 							A			(  )       ->				int:







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

			if len(_UpperCAmelCase					) > 0:
						_UpperCAmelCase	 =     ', '.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__":
							UpperCAmelCase__     :   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.")
							UpperCAmelCase__     :   int     = parser.parse_args()

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__     = {
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "CTRLForSequenceClassification",
							    "CTRLLMHeadModel",
							    "CTRLModel",
							    "CTRLPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__     = [
							    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFCTRLForSequenceClassification",
							    "TFCTRLLMHeadModel",
							    "TFCTRLModel",
							    "TFCTRLPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
							from .tokenization_ctrl import CTRLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ctrl import (
														    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CTRLForSequenceClassification,
														    CTRLLMHeadModel,
														    CTRLModel,
														    CTRLPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ctrl import (
														    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCTRLForSequenceClassification,
														    TFCTRLLMHeadModel,
														    TFCTRLModel,
														    TFCTRLPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase__     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
