
"""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
__SCREAMING_SNAKE_CASE							=     'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__SCREAMING_SNAKE_CASE							=     [
    ('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	):
 UpperCamelCase_     :					Tuple   	=re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'						,	__lowercase	)
 return [m.group(0	) for m in matches]




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

 UpperCamelCase_     :					Any   	=set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
 UpperCamelCase_     :					Any   	=list(__lowercase	)
 all_models.sort()

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

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

 UpperCamelCase_     :					Dict   	=[processors[t] for t in all_models]

 return pd.DataFrame(__lowercase	)




def       A_  (				__lowercase	):
 UpperCamelCase_     :					int   	=[
     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_     :					Any   	=[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
  UpperCamelCase_     :					Optional[int]   	=[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_     :					List[str]   	=[]
   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	):
 UpperCamelCase_     :					int   	=get_frameworks_table()
 UpperCamelCase_     :					Optional[int]   	=Dataset.from_pandas(__lowercase	)

 UpperCamelCase_     :					List[Any]   	=hf_hub_download(
     'huggingface/transformers-metadata'						,	'pipeline_tags.json'						,	repo_type='dataset'						,	token=__lowercase	)
 UpperCamelCase_     :					Tuple   	=Dataset.from_json(__lowercase	)
 UpperCamelCase_     :					List[str]   	={
     tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
     for i in range(len(__lowercase	)	)
 }
 UpperCamelCase_     :					Union[str, 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_     :					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_     :					Tuple   	=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       A_  (				):
 UpperCamelCase_     :					Dict   	={tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 UpperCamelCase_     :					Union[str, Any]   	=transformers_module.pipelines.SUPPORTED_TASKS
 UpperCamelCase_     :					Tuple   	=[]
 for key in pipeline_tasks:
  if key not in in_table:
   UpperCamelCase_     :					List[Any]   	=pipeline_tasks[key]['pt']
   if isinstance(__lowercase						,	(list, tuple)	):
    UpperCamelCase_     :					List[Any]   	=model[0]
   UpperCamelCase_     :					Optional[int]   	=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__":
 __SCREAMING_SNAKE_CASE							=     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.')
 __SCREAMING_SNAKE_CASE							=     parser.parse_args()

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


"""simple docstring"""






def       A_  (				__lowercase						,	__lowercase						,	__lowercase	):
 if len(__lowercase	) != len(__lowercase	):
  raise ValueError('The length of profit and weight must be same.'	)
 if max_weight <= 0:
  raise ValueError('max_weight must greater than zero.'	)
 if any(p < 0 for p in profit	):
  raise ValueError('Profit can not be negative.'	)
 if any(w < 0 for w in weight	):
  raise ValueError('Weight can not be negative.'	)

 # List created to store profit gained for the 1kg in case of each weight
 # respectively.  Calculate and append profit/weight for each element.
 UpperCamelCase_     :					int   	=[p / w for p, w in zip(__lowercase						,	__lowercase	)]

 # Creating a copy of the list and sorting profit/weight in ascending order
 UpperCamelCase_     :					Optional[int]   	=sorted(__lowercase	)

 # declaring useful variables
 UpperCamelCase_     :					Optional[int]   	=len(__lowercase	)
 UpperCamelCase_     :					Optional[int]   	=0
 UpperCamelCase_     :					List[Any]   	=0
 UpperCamelCase_     :					Optional[int]   	=0

 # loop till the total weight do not reach max limit e.g. 15 kg and till i<length
 while limit <= max_weight and i < length:
  # flag value for encountered greatest element in sorted_profit_by_weight
  UpperCamelCase_     :					List[str]   	=sorted_profit_by_weight[length - i - 1]
  UpperCamelCase_     :					Optional[Any]   	=profit_by_weight.index(__lowercase	)
  UpperCamelCase_     :					str   	=-1

  # check if the weight encountered is less than the total weight
  # encountered before.
  if max_weight - limit >= weight[index]:
   limit += weight[index]
   # Adding profit gained for the given weight 1 ===
   # weight[index]/weight[index]
   gain += 1 * profit[index]
  else:
   # Since the weight encountered is greater than limit, therefore take the
   # required number of remaining kgs and calculate profit for it.
   # weight remaining / weight[index]
   gain += (max_weight - limit) / weight[index] * profit[index]
   break
  i += 1
 return gain


if __name__ == "__main__":
 print(
     'Input profits, weights, and then max_weight (all positive ints) separated by '
     'spaces.'
 )

 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input profits separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     [int(x) for x in input('Input weights separated by spaces: ').split()]
 __SCREAMING_SNAKE_CASE							=     int(input('Max weight allowed: '))

 # Function Call
 calc_profit(profit, weight, max_weight)
