





'''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 : int											=			"src/transformers"


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase : Tuple											=			[
    ("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_    (	lowerCAmelCase_							):

				"""simple docstring"""






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







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowercase         = {
				    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.
				lowercase         = collections.defaultdict(lowerCAmelCase_							)
				lowercase         = collections.defaultdict(lowerCAmelCase_							)
				lowercase         = collections.defaultdict(lowerCAmelCase_							)

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

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

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

				lowercase         = {"model_type": all_models}
				lowercase         = [pt_models[t] for t in all_models]
				lowercase         = [tf_models[t] for t in all_models]
				lowercase         = [flax_models[t] for t in all_models]

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

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

				return pd.DataFrame(lowerCAmelCase_							)







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






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

            # 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_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = get_frameworks_table()
				lowercase         = Dataset.from_pandas(lowerCAmelCase_							)

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

				# Sort the model classes to avoid some nondeterministic updates to create false update commits.
				lowercase         = sorted(table.keys()							)
				lowercase         = 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],
				    }							)
				lowercase         = Dataset.from_pandas(lowerCAmelCase_							)

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

								if commit_sha is not None:
												lowercase         = (
												    f'Update with commit {commit_sha}\n\nSee: '
												    f'https://github.com/huggingface/transformers/commit/{commit_sha}'
												)
								else:
												lowercase         = "Update"

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







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






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

				if len(lowerCAmelCase_							) > 0:
								lowercase         = ", ".join(lowerCAmelCase_							)
								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 : 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.")
			__lowerCamelCase : Dict											=			parser.parse_args()

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











'''simple docstring'''

# Algorithm for the pigeonhole sorting









def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = min(lowerCAmelCase_							)  # min() finds the minimum value
				lowercase         = max(lowerCAmelCase_							)  # max() finds the maximum value

				lowercase         = max_val - min_val + 1  # size is difference of max and min values plus one

				# list of pigeonholes of size equal to the variable size
				lowercase         = [0] * size

				# Populate the pigeonholes.
				for x in a:
								assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							), "integers only please"
								holes[x - min_val] += 1

				# Putting the elements back into the array in an order.
				lowercase         = 0
				for count in range(lowerCAmelCase_							):
								while holes[count] > 0:
												holes[count] -= 1
												lowercase         = count + min_val
												i += 1







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = [8, 3, 2, 7, 4, 6, 8]
				pigeonhole_sort(lowerCAmelCase_							)
				print("Sorted order is:"		,							" ".join(lowerCAmelCase_							)							)


if __name__ == "__main__":
			main()




