



"""simple docstring"""
class 				lowerCamelCase  :






					'''simple docstring'''





					def __init__(       self:							Tuple						,     snake_case:							str = ""						,     snake_case:							bool = False						)   ->	None:
											# Mapping from the first character of the prefix of the node
											snake_case_ :dict[str, RadixNode]						=			{}

											# A node will be a leaf if the tree contains its word
											snake_case_ :Union[str, Any]						=			is_leaf

											snake_case_ :Dict						=			prefix






					def    lowerCAmelCase_				(       self:							Dict						,     snake_case:							str						)   ->	tuple[str, str, str]:
											snake_case_ :int						=			0
											for q, w in zip(self.prefix						,     snake_case						):
																	if q != w:
																							break

																	x += 1

											return self.prefix[:x], self.prefix[x:], word[x:]






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							list[str]						)   ->	None:
											for word in words:
																	self.insert(snake_case						)






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							str						)   ->	None:
											# Case 1: If the word is the prefix of the node
											# Solution: We set the current node as leaf
											if self.prefix == word:
																	snake_case_ :int						=			True

											# Case 2: The node has no edges that have a prefix to the word
											# Solution: We create an edge from the current node to a new one
											# containing the word
											elif word[0] not in self.nodes:
																	snake_case_ :List[Any]						=			RadixNode(prefix=snake_case						,     is_leaf=snake_case						)

											else:
																	snake_case_ :Dict						=			self.nodes[word[0]]
																	snake_case_,       snake_case_,       snake_case_ :Optional[int]						=			incoming_node.match(
																	    snake_case						)

																	# Case 3: The node prefix is equal to the matching
																	# Solution: We insert remaining word on the next node
																	if remaining_prefix == "":
																							self.nodes[matching_string[0]].insert(snake_case						)

																	# Case 4: The word is greater equal to the matching
																	# Solution: Create a node in between both nodes, change
																	# prefixes and add the new node for the remaining word
																	else:
																							snake_case_ :Union[str, Any]						=			remaining_prefix

																							snake_case_ :List[str]						=			self.nodes[matching_string[0]]
																							snake_case_ :Optional[int]						=			RadixNode(snake_case						,     snake_case						)
																							snake_case_ :str						=			aux_node

																							if remaining_word == "":
																													snake_case_ :Optional[int]						=			True
																							else:
																													self.nodes[matching_string[0]].insert(snake_case						)






					def    lowerCAmelCase_				(       self:							Optional[int]						,     snake_case:							str						)   ->	bool:
											snake_case_ :Union[str, Any]						=			self.nodes.get(word[0]						,     snake_case						)
											if not incoming_node:
																	return False
											else:
																	snake_case_,       snake_case_,       snake_case_ :int						=			incoming_node.match(
																	    snake_case						)
																	# If there is remaining prefix, the word can't be on the tree
																	if remaining_prefix != "":
																							return False
																	# This applies when the word and the prefix are equal
																	elif remaining_word == "":
																							return incoming_node.is_leaf
																	# We have word remaining so we check the next node
																	else:
																							return incoming_node.find(snake_case						)






					def    lowerCAmelCase_				(       self:							int						,     snake_case:							str						)   ->	bool:
											snake_case_ :List[str]						=			self.nodes.get(word[0]						,     snake_case						)
											if not incoming_node:
																	return False
											else:
																	snake_case_,       snake_case_,       snake_case_ :Any						=			incoming_node.match(
																	    snake_case						)
																	# If there is remaining prefix, the word can't be on the tree
																	if remaining_prefix != "":
																							return False
																	# We have word remaining so we check the next node
																	elif remaining_word != "":
																							return incoming_node.delete(snake_case						)
																	else:
																							# If it is not a leaf, we don't have to delete
																							if not incoming_node.is_leaf:
																													return False
																							else:
																													# We delete the nodes if no edges go from it
																													if len(incoming_node.nodes						) == 0:
																																			del self.nodes[word[0]]
																																			# We merge the current node with its only child
																																			if len(self.nodes						) == 1 and not self.is_leaf:
																																									snake_case_ :List[str]						=			list(self.nodes.values()						)[0]
																																									snake_case_ :Optional[Any]						=			merging_node.is_leaf
																																									self.prefix += merging_node.prefix
																																									snake_case_ :Dict						=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
																													elif len(incoming_node.nodes						) > 1:
																																			snake_case_ :List[str]						=			False
																													# If there is 1 edge, we merge it with its child
																													else:
																																			snake_case_ :Optional[Any]						=			list(incoming_node.nodes.values()						)[0]
																																			snake_case_ :Optional[Any]						=			merging_node.is_leaf
																																			incoming_node.prefix += merging_node.prefix
																																			snake_case_ :List[str]						=			merging_node.nodes

																													return True






					def    lowerCAmelCase_				(       self:							str						,     snake_case:							int = 0						)   ->	None:
											if self.prefix != "":
																	print("""-""" * height						,     self.prefix						,     """  (leaf)""" if self.is_leaf else """"""						)

											for value in self.nodes.values():
																	value.print_tree(height + 1						)

def    A_  (			):







						'''simple docstring'''


						snake_case_ :Any						=			"""banana bananas bandana band apple all beast""".split()
						snake_case_ :List[Any]						=			RadixNode()
						root.insert_many(_lowercase	)

						assert all(root.find(_lowercase	) for word in words	)
						assert not root.find("""bandanas"""	)
						assert not root.find("""apps"""	)
						root.delete("""all"""	)
						assert not root.find("""all"""	)
						root.delete("""banana"""	)
						assert not root.find("""banana"""	)
						assert root.find("""bananas"""	)

						return True
def    A_  (			):







						'''simple docstring'''


						assert test_trie()
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Dict						=			RadixNode()
						snake_case_ :Dict						=			"""banana bananas bandanas bandana band apple all beast""".split()
						root.insert_many(_lowercase	)

						print("""Words:""",					_lowercase	)
						print("""Tree:"""	)
						root.print_tree()


if __name__ == "__main__":
						main()





"""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      =				"src/transformers"


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__a      =				[
    ("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_  (			_lowercase	):







						'''simple docstring'''


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







						'''simple docstring'''


						snake_case_ :int						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_ :Dict						=			{
						    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_ :Optional[Any]						=			collections.defaultdict(_lowercase	)
						snake_case_ :int						=			collections.defaultdict(_lowercase	)
						snake_case_ :List[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	):
												snake_case_ :int						=			None
												if _re_tf_models.match(_lowercase	) is not None:
																		snake_case_ :int						=			tf_models
																		snake_case_ :List[str]						=			_re_tf_models.match(_lowercase	).groups()[0]
												elif _re_flax_models.match(_lowercase	) is not None:
																		snake_case_ :List[Any]						=			flax_models
																		snake_case_ :Any						=			_re_flax_models.match(_lowercase	).groups()[0]
												elif _re_pt_models.match(_lowercase	) is not None:
																		snake_case_ :Optional[Any]						=			pt_models
																		snake_case_ :int						=			_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:
																														snake_case_ :Optional[int]						=			True
																														break
																								# Try again after removing the last word in the name
																								snake_case_ :Optional[Any]						=			"""""".join(camel_case_split(_lowercase	)[:-1]	)

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

						snake_case_ :Optional[int]						=			{"""model_type""": all_models}
						snake_case_ :Optional[int]						=			[pt_models[t] for t in all_models]
						snake_case_ :Any						=			[tf_models[t] for t in all_models]
						snake_case_ :Dict						=			[flax_models[t] for t in all_models]

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

						snake_case_ :int						=			[processors[t] for t in all_models]

						return pd.DataFrame(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[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:
												snake_case_ :Optional[int]						=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												snake_case_ :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
																		snake_case_ :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    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			get_frameworks_table()
						snake_case_ :str						=			Dataset.from_pandas(_lowercase	)

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

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

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







						'''simple docstring'''


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

						if len(_lowercase	) > 0:
												snake_case_ :Optional[int]						=			""", """.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__":
						__a      =				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      =				parser.parse_args()

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