

"""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
snake_case_   :   str        =       '''src/transformers'''


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
snake_case_   :   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   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   Any      =				re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"							,      _lowercase	)
					return [m.group(0	) for m in matches]



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					UpperCAmelCase			:   int      =				{
					    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			:   str      =				collections.defaultdict(_lowercase	)
					UpperCAmelCase			:   List[str]      =				collections.defaultdict(_lowercase	)
					UpperCAmelCase			:   str      =				collections.defaultdict(_lowercase	)

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

										if lookup_dict is not None:
															while len(_lowercase	) > 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			:   str      =				"".join(camel_case_split(_lowercase	)[:-1]	)

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

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

					# Now let's use the auto-mapping names to make sure
					UpperCAmelCase			:   int      =				{}
					for t in all_models:
										if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
															UpperCAmelCase			:   int      =				"AutoProcessor"
										elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
															UpperCAmelCase			:   List[str]      =				"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			:   int      =				"AutoTokenizer"

					UpperCAmelCase			:   str      =				[processors[t] for t in all_models]

					return pd.DataFrame(_lowercase	)



def   lowercase_	(    _lowercase       :		Tuple	):




					'''simple docstring'''






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

            # 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   lowercase_	(    _lowercase       :		Any							,      _lowercase       :		Any	):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				get_frameworks_table()
					UpperCAmelCase			:   int      =				Dataset.from_pandas(_lowercase	)

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

					# Sort the model classes to avoid some nondeterministic updates to create false update commits.
					UpperCAmelCase			:   Any      =				sorted(table.keys()	)
					UpperCAmelCase			:   List[str]      =				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			:   str      =				Dataset.from_pandas(_lowercase	)

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

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

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



def   lowercase_	(    ):




					'''simple docstring'''






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

					if len(_lowercase	) > 0:
										UpperCAmelCase			:   Union[str, Any]      =				", ".join(_lowercase	)
										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__":
	snake_case_   :   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.""")
	snake_case_   :   int        =       parser.parse_args()

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





"""simple docstring"""






def   lowercase_	(    _lowercase       :		List[str]							,      _lowercase       :		Tuple							,      _lowercase       :		int							,      _lowercase       :		Optional[Any]	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				[False] * len(_lowercase	)
					UpperCAmelCase			:   List[str]      =				[]
					queue.append(_lowercase	)
					UpperCAmelCase			:   List[Any]      =				True

					while queue:
										UpperCAmelCase			:   Any      =				queue.pop(0	)
										for ind in range(len(graph[u]	)	):
															if visited[ind] is False and graph[u][ind] > 0:
																				queue.append(_lowercase	)
																				UpperCAmelCase			:   Optional[int]      =				True
																				UpperCAmelCase			:   int      =				u

					return visited[t]



def   lowercase_	(    _lowercase       :		Tuple							,      _lowercase       :		Dict							,      _lowercase       :		Optional[int]	):




					'''simple docstring'''






					UpperCAmelCase			:   List[Any]      =				[-1] * (len(_lowercase	))
					UpperCAmelCase			:   List[Any]      =				0
					while bfs(_lowercase							,      _lowercase							,      _lowercase							,      _lowercase	):
										UpperCAmelCase			:   List[str]      =				float("Inf"	)
										UpperCAmelCase			:   str      =				sink

										while s != source:
															# Find the minimum value in select path
															UpperCAmelCase			:   Tuple      =				min(_lowercase							,      graph[parent[s]][s]	)
															UpperCAmelCase			:   Optional[Any]      =				parent[s]

										max_flow += path_flow
										UpperCAmelCase			:   List[str]      =				sink

										while v != source:
															UpperCAmelCase			:   Dict      =				parent[v]
															graph[u][v] -= path_flow
															graph[v][u] += path_flow
															UpperCAmelCase			:   Dict      =				parent[v]
					return max_flow


snake_case_   :   Optional[Any]        =       [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

snake_case_     ,      snake_case_   :   str        =       0, 5




print(ford_fulkerson(graph, source, sink))


