

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_			  = '''src/transformers'''


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





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

def 	snake_case__			(    ):



     '''simple docstring'''





     lowercase__    :   Tuple      =       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     lowercase__    :   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.
     lowercase__    :   Dict      =       collections.defaultdict(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   List[Any]      =       collections.defaultdict(SCREAMING_SNAKE_CASE_			)
     lowercase__    :   Any      =       collections.defaultdict(SCREAMING_SNAKE_CASE_			)

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

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

     lowercase__    :   List[str]      =       set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
     lowercase__    :   int      =       list(SCREAMING_SNAKE_CASE_			)
     all_models.sort()

     lowercase__    :   Dict      =       {'model_type': all_models}
     lowercase__    :   str      =       [pt_models[t] for t in all_models]
     lowercase__    :   List[Any]      =       [tf_models[t] for t in all_models]
     lowercase__    :   Tuple      =       [flax_models[t] for t in all_models]

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

     lowercase__    :   Tuple      =       [processors[t] for t in all_models]

     return pd.DataFrame(SCREAMING_SNAKE_CASE_			)

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[str]			):



     '''simple docstring'''





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

            # 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 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





     lowercase__    :   List[str]      =       get_frameworks_table()
     lowercase__    :   List[Any]      =       Dataset.from_pandas(SCREAMING_SNAKE_CASE_			)

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

     # Sort the model classes to avoid some nondeterministic updates to create false update commits.
     lowercase__    :   Optional[int]      =       sorted(table.keys()			)
     lowercase__    :   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],
         }			)
     lowercase__    :   int      =       Dataset.from_pandas(SCREAMING_SNAKE_CASE_			)

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

          if commit_sha is not None:
               lowercase__    :   Union[str, Any]      =       (
                   f"""Update with commit {commit_sha}\n\nSee: """
                   f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
               )
          else:
               lowercase__    :   Union[str, Any]      =       'Update'

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

def 	snake_case__			(    ):



     '''simple docstring'''





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

     if len(SCREAMING_SNAKE_CASE_			) > 0:
          lowercase__    :   Dict      =       ', '.join(SCREAMING_SNAKE_CASE_			)
          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_			  = 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_			  = parser.parse_args()

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




import math

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    int			):



     '''simple docstring'''





     lowercase__    :   Optional[Any]      =       []
     lowercase__    :   str      =       2
     lowercase__    :   Optional[Any]      =       int(math.sqrt(SCREAMING_SNAKE_CASE_			)			)  # Size of every segment
     lowercase__    :   Dict      =       [True] * (end + 1)
     lowercase__    :   Union[str, Any]      =       []

     while start <= end:
          if temp[start] is True:
               in_prime.append(SCREAMING_SNAKE_CASE_			)
               for i in range(start * start	,     end + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   int      =       False
          start += 1
     prime += in_prime

     lowercase__    :   Optional[int]      =       end + 1
     lowercase__    :   List[str]      =       min(2 * end	,     SCREAMING_SNAKE_CASE_			)

     while low <= n:
          lowercase__    :   str      =       [True] * (high - low + 1)
          for each in in_prime:
               lowercase__    :   str      =       math.floor(low / each			) * each
               if t < low:
                    t += each

               for j in range(SCREAMING_SNAKE_CASE_	,     high + 1	,     SCREAMING_SNAKE_CASE_			):
                    lowercase__    :   Optional[Any]      =       False

          for j in range(len(SCREAMING_SNAKE_CASE_			)			):
               if temp[j] is True:
                    prime.append(j + low			)

          lowercase__    :   Optional[Any]      =       high + 1
          lowercase__    :   Optional[int]      =       min(high + end	,     SCREAMING_SNAKE_CASE_			)

     return prime

print(sieve(10**6))

