"""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__:Optional[Any]			=	"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__:List[str]			=	direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__:Dict			=	re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE__: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.
SCREAMING_SNAKE_CASE__:Union[str, Any]			=	re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__:str			=	[
    ("""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 					_lowerCamelCase(					a							):
       __a     				=  re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" ,  a							)
       return [m.group(0							) for m in matches]



def 					_lowerCamelCase(					):
       __a     				=  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __a     				=  {
           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     				=  collections.defaultdict(a							)
       __a     				=  collections.defaultdict(a							)
       __a     				=  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     				=  None
              if _re_tf_models.match(a							) is not None:
                     __a     				=  tf_models
                     __a     				=  _re_tf_models.match(a							).groups()[0]
              elif _re_flax_models.match(a							) is not None:
                     __a     				=  flax_models
                     __a     				=  _re_flax_models.match(a							).groups()[0]
              elif _re_pt_models.match(a							) is not None:
                     __a     				=  pt_models
                     __a     				=  _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     				=  True
                                   break
                            # Try again after removing the last word in the name
                            __a     				=  "".join(camel_case_split(a							)[:-1]							)

       __a     				=  set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
       __a     				=  list(a							)
       all_models.sort()

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

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

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

       return pd.DataFrame(a							)



def 					_lowerCamelCase(					a							):
       __a     				=  [
           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     				=  [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
              __a     				=  [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     				=  []
                     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 					_lowerCamelCase(					a ,  a							):
       __a     				=  get_frameworks_table()
       __a     				=  Dataset.from_pandas(a							)

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

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       __a     				=  sorted(table.keys()							)
       __a     				=  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     				=  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     				=  (
                         F"Update with commit {commit_sha}\n\nSee: "
                         F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     __a     				=  "Update"

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



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

       if len(a							) > 0:
              __a     				=  ", ".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__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]			=	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__:int			=	parser.parse_args()

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

"""simple docstring"""



import os



def 					_lowerCamelCase(					):
       with open(os.path.dirname(a							) + "/grid.txt"							) as f:
              __a     				=  []  # noqa: E741
              for _ in range(2_0							):
                     l.append([int(a							) for x in f.readline().split()]							)

              __a     				=  0

              # right
              for i in range(2_0							):
                     for j in range(1_7							):
                            __a     				=  l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
                            if temp > maximum:
                                   __a     				=  temp

        # down
              for i in range(1_7							):
                     for j in range(2_0							):
                            __a     				=  l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
                            if temp > maximum:
                                   __a     				=  temp

        # diagonal 1
              for i in range(1_7							):
                     for j in range(1_7							):
                            __a     				=  l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
                            if temp > maximum:
                                   __a     				=  temp

        # diagonal 2
              for i in range(1_7							):
                     for j in range(3 ,  2_0							):
                            __a     				=  l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
                            if temp > maximum:
                                   __a     				=  temp
              return maximum


if __name__ == "__main__":
   print(solution())
