



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__    =	"""src/transformers"""


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


# Regexes that match TF/Flax/PT model names.
a__    =	re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
a__    =	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__    =	re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
a__    =	[
    ("""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       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Union[str, Any]:
 _snake_case    :  Optional[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       lowercase       (   )  ->     Any:
 _snake_case    :  Dict					=					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
 _snake_case    :  Optional[int]					=					{
     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.
 _snake_case    :  List[Any]					=					collections.defaultdict(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  str					=					collections.defaultdict(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[str]					=					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__				):
  _snake_case    :  List[str]					=					None
  if _re_tf_models.match(SCREAMING_SNAKE_CASE__				) is not None:
   _snake_case    :  Tuple					=					tf_models
   _snake_case    :  str					=					_re_tf_models.match(SCREAMING_SNAKE_CASE__				).groups()[0]
  elif _re_flax_models.match(SCREAMING_SNAKE_CASE__				) is not None:
   _snake_case    :  Optional[Any]					=					flax_models
   _snake_case    :  List[str]					=					_re_flax_models.match(SCREAMING_SNAKE_CASE__				).groups()[0]
  elif _re_pt_models.match(SCREAMING_SNAKE_CASE__				) is not None:
   _snake_case    :  List[Any]					=					pt_models
   _snake_case    :  List[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:
     _snake_case    :  Tuple					=					True
     break
    # Try again after removing the last word in the name
    _snake_case    :  Tuple					=					"""""".join(camel_case_split(SCREAMING_SNAKE_CASE__				)[:-1]				)

 _snake_case    :  str					=					set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
 _snake_case    :  Dict					=					list(SCREAMING_SNAKE_CASE__				)
 all_models.sort()

 _snake_case    :  Union[str, Any]					=					{"""model_type""": all_models}
 _snake_case    :  Optional[Any]					=					[pt_models[t] for t in all_models]
 _snake_case    :  List[Any]					=					[tf_models[t] for t in all_models]
 _snake_case    :  Union[str, Any]					=					[flax_models[t] for t in all_models]

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

 _snake_case    :  Optional[int]					=					[processors[t] for t in all_models]

 return pd.DataFrame(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				)  ->     int:
 _snake_case    :  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:
  _snake_case    :  str					=					[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
  _snake_case    :  Union[str, 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
   _snake_case    :  Dict					=					[]
   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       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     int:
 _snake_case    :  Tuple					=					get_frameworks_table()
 _snake_case    :  Union[str, Any]					=					Dataset.from_pandas(SCREAMING_SNAKE_CASE__				)

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

 # Sort the model classes to avoid some nondeterministic updates to create false update commits.
 _snake_case    :  Any					=					sorted(table.keys()				)
 _snake_case    :  Union[str, 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],
     }				)
 _snake_case    :  Any					=					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:
   _snake_case    :  str					=					(
       F'''Update with commit {commit_sha}\n\nSee: '''
       F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
   )
  else:
   _snake_case    :  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       lowercase       (   )  ->     Optional[int]:
 _snake_case    :  List[Any]					=					{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
 _snake_case    :  int					=					transformers_module.pipelines.SUPPORTED_TASKS
 _snake_case    :  Dict					=					[]
 for key in pipeline_tasks:
  if key not in in_table:
   _snake_case    :  Union[str, Any]					=					pipeline_tasks[key]["""pt"""]
   if isinstance(SCREAMING_SNAKE_CASE__				,       (list, tuple)				):
    _snake_case    :  str					=					model[0]
   _snake_case    :  Any					=					model.__name__
   if model not in in_table.values():
    missing.append(SCREAMING_SNAKE_CASE__				)

 if len(SCREAMING_SNAKE_CASE__				) > 0:
  _snake_case    :  Optional[Any]					=					""", """.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__":
  a__    =	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__    =	parser.parse_args()

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





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a__    =	"""src/diffusers"""

# Matches is_xxx_available()
a__    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a__    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a__    =	"""
{0} = None
"""

a__    =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a__    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[Any]:
 _snake_case    :  str					=					_re_backend.findall(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Optional[Any]:
 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """__init__.py"""				)				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
  _snake_case    :  Dict					=					f.readlines()

 # Get to the point we do the actual imports for type checking
 _snake_case    :  Optional[int]					=					0
 _snake_case    :  Union[str, Any]					=					{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__				):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  _snake_case    :  int					=					find_backend(lines[line_index]				)
  if backend is not None:
   while not lines[line_index].startswith("""else:"""				):
    line_index += 1
   line_index += 1
   _snake_case    :  str					=					[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
    _snake_case    :  int					=					lines[line_index]
    _snake_case    :  str					=					_re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(""", """				)				)
    elif line.startswith(""" """ * 8				):
     objects.append(line[8:-2]				)
    line_index += 1

   if len(SCREAMING_SNAKE_CASE__				) > 0:
    _snake_case    :  Optional[int]					=					objects
  else:
   line_index += 1

 return backend_specific_objects



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Dict:
 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__				)
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict=None				)  ->     List[Any]:
 if backend_specific_objects is None:
  _snake_case    :  Dict					=					read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 _snake_case    :  Union[str, Any]					=					{}

 for backend, objects in backend_specific_objects.items():
  _snake_case    :  List[Any]					=					"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_"""				)				) + """]"""
  _snake_case    :  Any					=					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for o in objects]				)
  _snake_case    :  Any					=					dummy_file

 return dummy_files



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Optional[int]:
 _snake_case    :  str					=					create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _snake_case    :  List[Any]					=					{"""torch""": """pt"""}

 # Locate actual dummy modules and read their content.
 _snake_case    :  Dict					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """utils"""				)
 _snake_case    :  int					=					{
     backend: os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py'''				)
     for backend in dummy_files.keys()
 }

 _snake_case    :  int					=					{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE__				):
   with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
    _snake_case    :  Union[str, Any]					=					f.read()
  else:
   _snake_case    :  str					=					""""""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py as the main '''
        """__init__ has new objects."""				)
    with open(dummy_file_paths[backend]				,       """w"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
     f.write(dummy_files[backend]				)
   else:
    raise ValueError(
        """The main __init__ has objects that are not present in """
        F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '''
        """to fix this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  a__    =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)
