




"""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
a_				:    Union[str, Any]										=     '''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
a_				:    Any										=     re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
a_				:    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.
a_				:    Optional[int]										=     re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a_				:    Union[str, 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 						UpperCAmelCase				(			A__:    int     )  ->     Optional[int]:
			__lowerCamelCase					:	str	       =				re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'							,				A__     )
			return [m.group(0     ) for m in matches]





def 						UpperCAmelCase				(			)  ->     Union[str, Any]:
			__lowerCamelCase					:	Optional[Any]	       =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
			__lowerCamelCase					:	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.
			__lowerCamelCase					:	List[str]	       =				collections.defaultdict(A__     )
			__lowerCamelCase					:	Dict	       =				collections.defaultdict(A__     )
			__lowerCamelCase					:	Any	       =				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__     ):
						__lowerCamelCase					:	Tuple	       =				None
						if _re_tf_models.match(A__     ) is not None:
									__lowerCamelCase					:	List[str]	       =				tf_models
									__lowerCamelCase					:	Optional[int]	       =				_re_tf_models.match(A__     ).groups()[0]
						elif _re_flax_models.match(A__     ) is not None:
									__lowerCamelCase					:	Optional[Any]	       =				flax_models
									__lowerCamelCase					:	Optional[int]	       =				_re_flax_models.match(A__     ).groups()[0]
						elif _re_pt_models.match(A__     ) is not None:
									__lowerCamelCase					:	Any	       =				pt_models
									__lowerCamelCase					:	Tuple	       =				_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:
															__lowerCamelCase					:	List[str]	       =				True
															break
												# Try again after removing the last word in the name
												__lowerCamelCase					:	Optional[int]	       =				''.join(camel_case_split(A__     )[:-1]     )

			__lowerCamelCase					:	Tuple	       =				set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
			__lowerCamelCase					:	Optional[Any]	       =				list(A__     )
			all_models.sort()

			__lowerCamelCase					:	str	       =				{'model_type': all_models}
			__lowerCamelCase					:	str	       =				[pt_models[t] for t in all_models]
			__lowerCamelCase					:	List[Any]	       =				[tf_models[t] for t in all_models]
			__lowerCamelCase					:	List[str]	       =				[flax_models[t] for t in all_models]

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

			__lowerCamelCase					:	Tuple	       =				[processors[t] for t in all_models]

			return pd.DataFrame(A__     )





def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     List[str]:
			__lowerCamelCase					:	List[str]	       =				[
			    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					:	Any	       =				[model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
						__lowerCamelCase					:	Optional[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
									__lowerCamelCase					:	List[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 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    Optional[Any]     )  ->     Tuple:
			__lowerCamelCase					:	int	       =				get_frameworks_table()
			__lowerCamelCase					:	Optional[Any]	       =				Dataset.from_pandas(A__     )

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

			# Sort the model classes to avoid some nondeterministic updates to create false update commits.
			__lowerCamelCase					:	List[str]	       =				sorted(table.keys()     )
			__lowerCamelCase					:	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],
			    }     )
			__lowerCamelCase					:	Optional[Any]	       =				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:
									__lowerCamelCase					:	Dict	       =				(
									    f'''Update with commit {commit_sha}\n\nSee: '''
									    f'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
									)
						else:
									__lowerCamelCase					:	Any	       =				'Update'

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





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

			if len(A__     ) > 0:
						__lowerCamelCase					:	List[str]	       =				', '.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__":
					a_				:    Optional[Any]										=     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[Any]										=     parser.parse_args()

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









"""simple docstring"""
def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations(A__:    int     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						return sum(count_of_possible_combinations(target - item     ) for item in array     )

			return count_of_possible_combinations(A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:

			def count_of_possible_combinations_with_dp_array(
			    A__:    int							,				A__:    list[int]     ) -> int:
						if target < 0:
									return 0
						if target == 0:
									return 1
						if dp_array[target] != -1:
									return dp_array[target]
						__lowerCamelCase					:	Dict	       =				sum(
						    count_of_possible_combinations_with_dp_array(target - item							,				A__     )
						    for item in array     )
						__lowerCamelCase					:	str	       =				answer
						return answer

			__lowerCamelCase					:	Union[str, Any]	       =				[-1] * (target + 1)
			return count_of_possible_combinations_with_dp_array(A__							,				A__     )





def 						UpperCAmelCase				(			A__:    int							,				A__:    list[int]							,				A__:    int     )  ->     int:
			__lowerCamelCase					:	int	       =				[0] * (target + 1)
			__lowerCamelCase					:	str	       =				1

			for i in range(1							,				target + 1     ):
						for j in range(A__     ):
									if i - array[j] >= 0:
												dp_array[i] += dp_array[i - array[j]]

			return dp_array[target]


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					a_				:    Any										=     3
					a_				:    str										=     5
					a_				:    List[Any]										=     [1, 2, 5]
					print(combination_sum_iv(n, array, target))



