






"""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_					=      '''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
lowerCamelCase_					=      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCamelCase_					=      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_					=      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase_					=      [
    ('''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      (   A__						):
			UpperCAmelCase_     : Tuple			     =					re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"	,A__						)
			return [m.group(0						) for m in matches]

def 				snake_case      (   ):
			UpperCAmelCase_     : List[Any]			     =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			UpperCAmelCase_     : str			     =					{
			    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_     : Optional[Any]			     =					collections.defaultdict(A__						)
			UpperCAmelCase_     : Optional[Any]			     =					collections.defaultdict(A__						)
			UpperCAmelCase_     : Optional[int]			     =					collections.defaultdict(A__						)

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

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

			UpperCAmelCase_     : Any			     =					set(list(pt_models.keys()						) + list(tf_models.keys()						) + list(flax_models.keys()						)						)
			UpperCAmelCase_     : str			     =					list(A__						)
			all_models.sort()

			UpperCAmelCase_     : Any			     =					{"model_type": all_models}
			UpperCAmelCase_     : Optional[Any]			     =					[pt_models[t] for t in all_models]
			UpperCAmelCase_     : Optional[Any]			     =					[tf_models[t] for t in all_models]
			UpperCAmelCase_     : str			     =					[flax_models[t] for t in all_models]

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

			UpperCAmelCase_     : Optional[int]			     =					[processors[t] for t in all_models]

			return pd.DataFrame(A__						)

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

            # 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      (   A__	,A__						):
			UpperCAmelCase_     : Dict			     =					get_frameworks_table()
			UpperCAmelCase_     : Optional[Any]			     =					Dataset.from_pandas(A__						)

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

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			UpperCAmelCase_     : Optional[int]			     =					sorted(table.keys()						)
			UpperCAmelCase_     : Any			     =					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_     : int			     =					Dataset.from_pandas(A__						)

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

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

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

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

			if len(A__						) > 0:
						UpperCAmelCase_     : Optional[Any]			     =					", ".join(A__						)
						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_					=      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_					=      parser.parse_args()

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











"""simple docstring"""
lowerCamelCase_					=      [
    (1000, '''M'''),
    (900, '''CM'''),
    (500, '''D'''),
    (400, '''CD'''),
    (100, '''C'''),
    (90, '''XC'''),
    (50, '''L'''),
    (40, '''XL'''),
    (10, '''X'''),
    (9, '''IX'''),
    (5, '''V'''),
    (4, '''IV'''),
    (1, '''I'''),
]

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[str]			     =					{"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}
			UpperCAmelCase_     : Optional[Any]			     =					0
			UpperCAmelCase_     : Tuple			     =					0
			while place < len(A__						):
						if (place + 1 < len(A__						)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def 				snake_case      (   A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					[]
			for arabic, roman in ROMAN:
						((UpperCAmelCase_)     ,	(UpperCAmelCase_))     : str			     =					divmod(A__	,A__						)
						result.append(roman * factor						)
						if number == 0:
									break
			return "".join(A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



