





"""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_     :						int						=					direct_transformers_import(TRANSFORMERS_PATH)


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase_     :						int						=					[
    ('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__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





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

  A_  :       str										=  set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
  A_  :       Tuple										=  list(a_					)
  all_models.sort()

  A_  :       Optional[int]										=  {'model_type': all_models}
  A_  :       int										=  [pt_models[t] for t in all_models]
  A_  :       Optional[Any]										=  [tf_models[t] for t in all_models]
  A_  :       List[Any]										=  [flax_models[t] for t in all_models]

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

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

  return pd.DataFrame(a_					)





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       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:
    A_  :       int										=  [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    A_  :       Optional[int]										=  [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
      A_  :       Tuple										=  []
      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__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  A_  :       str										=  get_frameworks_table()
  A_  :       List[str]										=  Dataset.from_pandas(a_					)

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

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

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





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





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

  if len(a_					) > 0:
    A_  :       List[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_     :						Optional[int]						=					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_     :						int						=					parser.parse_args()

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

from typing import Dict, Optional

import numpy as np

import datasets


SCREAMING_SNAKE_CASE							:List[Any]							 =				'\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

SCREAMING_SNAKE_CASE							:List[str]							 =				'\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

SCREAMING_SNAKE_CASE							:str							 =				'\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Tuple:







				"""simple docstring"""



				if label_map is not None:
								for old_id, new_id in label_map.items():
												__A   			= new_id

    # turn into Numpy arrays
				__A   			= np.array(a_						)
				__A   			= np.array(a_						)

				if reduce_labels:
								__A   			= 2_5_5
								__A   			= label - 1
								__A   			= 2_5_5

				__A   			= label != ignore_index
				__A   			= np.not_equal(a_		,   a_						)
				__A   			= pred_label[mask]
				__A   			= np.array(a_						)[mask]

				__A   			= pred_label[pred_label == label]

				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]
				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]
				__A   			= np.histogram(a_		,   bins=a_		,   range=(0, num_labels - 1)						)[0]

				__A   			= area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = False		,   )		->	Union[str, Any]:







				"""simple docstring"""



				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				__A   			= np.zeros((num_labels,)		,   dtype=np.floataa						)
				for result, gt_seg_map in zip(a_		,   a_						):
								__A			,							__A			,							__A			,							__A   			= intersect_and_union(
								    a_		,   a_		,   a_		,   a_		,   a_		,   a_						)
								total_area_intersect += area_intersect
								total_area_union += area_union
								total_area_pred_label += area_pred_label
								total_area_label += area_label
				return total_area_intersect, total_area_union, total_area_pred_label, total_area_label


def 						UpperCAmelCase			(	a_		,   a_		,   a_		,   a_		,   a_ = None		,   a_ = None		,   a_ = False		,   )		->	str:







				"""simple docstring"""



				__A			,							__A			,							__A			,							__A   			= total_intersect_and_union(
				    a_		,   a_		,   a_		,   a_		,   a_		,   a_						)

				# compute metrics
				__A   			= {}

				__A   			= total_area_intersect.sum() / total_area_label.sum()
				__A   			= total_area_intersect / total_area_union
				__A   			= total_area_intersect / total_area_label

				__A   			= np.nanmean(a_						)
				__A   			= np.nanmean(a_						)
				__A   			= all_acc
				__A   			= iou
				__A   			= acc

				if nan_to_num is not None:
								__A   			= {metric: np.nan_to_num(a_		,   nan=a_						) for metric, metric_value in metrics.items()}

				return metrics





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,			_KWARGS_DESCRIPTION  )
class 							UpperCAmelCase      (  datasets.Metric  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: List[Any] ):
					return datasets.MetricInfo(
					    description=_DESCRIPTION							,citation=_CITATION							,inputs_description=_KWARGS_DESCRIPTION							,features=datasets.Features(
					        # 1st Seq - height dim, 2nd - width dim
					        {
					            "predictions": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
					            "references": datasets.Sequence(datasets.Sequence(datasets.Value("uint16" ) ) ),
					        } )							,reference_urls=[
					        "https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py"
					    ]							,)







	def 				UpperCamelCase_  (       self					: int							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: bool							,A					: Optional[int] = None							,A					: Optional[Dict[int, int]] = None							,A					: bool = False							,):
					__A   			= mean_iou(
					    results=A							,gt_seg_maps=A							,num_labels=A							,ignore_index=A							,nan_to_num=A							,label_map=A							,reduce_labels=A							,)
					return iou_result
