
"""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
_A			:       List[Any]   		=       """src/transformers"""


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_A			:       List[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   __magic_name__	(      __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase    :				Optional[Any]       		=							re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"     , __a  )
       return [m.group(0  ) for m in matches]


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

       lowercase    :				Tuple       		=							set(list(pt_models.keys()  ) + list(tf_models.keys()  ) + list(flax_models.keys()  )  )
       lowercase    :				Union[str, Any]       		=							list(__a  )
       all_models.sort()

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

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

       lowercase    :				Optional[Any]       		=							[processors[t] for t in all_models]

       return pd.DataFrame(__a  )


def   __magic_name__	(      __snake_case				:       Optional[Any]  ) ->				Optional[Any]:
       lowercase    :				List[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:
              lowercase    :				Dict       		=							[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
              lowercase    :				str       		=							[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
                     lowercase    :				int       		=							[]
                     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   __magic_name__	(      __snake_case				:       str     , __snake_case				:       Any  ) ->				Optional[Any]:
       lowercase    :				List[str]       		=							get_frameworks_table()
       lowercase    :				Dict       		=							Dataset.from_pandas(__a  )

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

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

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


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

       if len(__a  ) > 0:
              lowercase    :				Optional[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__":
   _A			:       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.""")
   _A			:       List[str]   		=       parser.parse_args()

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




'''simple docstring'''







import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


__lowerCAmelCase         =    HUGGINGFACE_HUB_CACHE


__lowerCAmelCase         =    """config.json"""
__lowerCAmelCase         =    """diffusion_pytorch_model.bin"""
__lowerCAmelCase         =    """diffusion_flax_model.msgpack"""
__lowerCAmelCase         =    """model.onnx"""
__lowerCAmelCase         =    """diffusion_pytorch_model.safetensors"""
__lowerCAmelCase         =    """weights.pb"""
__lowerCAmelCase         =    """https://huggingface.co"""
__lowerCAmelCase         =    default_cache_path
__lowerCAmelCase         =    """diffusers_modules"""
__lowerCAmelCase         =    os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
__lowerCAmelCase         =    ["""fp16""", """non-ema"""]
__lowerCAmelCase         =    """.self_attn"""



