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







def       A_ (			):
      _lowerCAmelCase		 = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _lowerCAmelCase		 = {
          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.
      _lowerCAmelCase		 = collections.defaultdict(_lowerCamelCase     )
      _lowerCAmelCase		 = collections.defaultdict(_lowerCamelCase     )
      _lowerCAmelCase		 = collections.defaultdict(_lowerCamelCase     )

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

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

      _lowerCAmelCase		 = set(list(pt_models.keys()     ) + list(tf_models.keys()     ) + list(flax_models.keys()     )     )
      _lowerCAmelCase		 = list(_lowerCamelCase     )
      all_models.sort()

      _lowerCAmelCase		 = {'model_type': all_models}
      _lowerCAmelCase		 = [pt_models[t] for t in all_models]
      _lowerCAmelCase		 = [tf_models[t] for t in all_models]
      _lowerCAmelCase		 = [flax_models[t] for t in all_models]

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

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

      return pd.DataFrame(_lowerCamelCase     )







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

            # 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_ (			_lowerCamelCase      :				List[Any]	,	_lowerCamelCase      :				Union[str, Any]     ):
      _lowerCAmelCase		 = get_frameworks_table()
      _lowerCAmelCase		 = Dataset.from_pandas(_lowerCamelCase     )

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

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      _lowerCAmelCase		 = sorted(table.keys()     )
      _lowerCAmelCase		 = 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],
          }     )
      _lowerCAmelCase		 = Dataset.from_pandas(_lowerCamelCase     )

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

            if commit_sha is not None:
                  _lowerCAmelCase		 = (
                      F"Update with commit {commit_sha}\n\nSee: "
                      F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                  )
            else:
                  _lowerCAmelCase		 = 'Update'

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







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

      if len(_lowerCamelCase     ) > 0:
            _lowerCAmelCase		 = ', '.join(_lowerCamelCase     )
            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)









'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
 lowerCamelCase				:  int    		=      argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
 )
 # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
 parser.add_argument(
     '--original_config_file',
     default=None,
     type=str,
     help='The YAML config file corresponding to the original architecture.',
 )
 parser.add_argument(
     '--num_in_channels',
     default=None,
     type=int,
     help='The number of input channels. If `None` number of input channels will be automatically inferred.',
 )
 parser.add_argument(
     '--scheduler_type',
     default='pndm',
     type=str,
     help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
 )
 parser.add_argument(
     '--pipeline_type',
     default=None,
     type=str,
     help=(
         'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
         '. If `None` pipeline will be automatically inferred.'
     ),
 )
 parser.add_argument(
     '--image_size',
     default=None,
     type=int,
     help=(
         'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
         ' Base. Use 768 for Stable Diffusion v2.'
     ),
 )
 parser.add_argument(
     '--prediction_type',
     default=None,
     type=str,
     help=(
         'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
         ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
     ),
 )
 parser.add_argument(
     '--extract_ema',
     action='store_true',
     help=(
         'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
         ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
         ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
     ),
 )
 parser.add_argument(
     '--upcast_attention',
     action='store_true',
     help=(
         'Whether the attention computation should always be upcasted. This is necessary when running stable'
         ' diffusion 2.1.'
     ),
 )
 parser.add_argument(
     '--from_safetensors',
     action='store_true',
     help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
 )
 parser.add_argument(
     '--to_safetensors',
     action='store_true',
     help='Whether to store pipeline in safetensors format or not.',
 )
 parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
 parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
 parser.add_argument(
     '--stable_unclip',
     type=str,
     default=None,
     required=False,
     help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
 )
 parser.add_argument(
     '--stable_unclip_prior',
     type=str,
     default=None,
     required=False,
     help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
 )
 parser.add_argument(
     '--clip_stats_path',
     type=str,
     help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
     required=False,
 )
 parser.add_argument(
     '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
 )
 parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
 parser.add_argument(
     '--vae_path',
     type=str,
     default=None,
     required=False,
     help='Set to a path, hub id to an already converted vae to not convert it again.',
 )
 lowerCamelCase				:  str    		=      parser.parse_args()

 lowerCamelCase				:  Tuple    		=      download_from_original_stable_diffusion_ckpt(
     checkpoint_path=args.checkpoint_path,
     original_config_file=args.original_config_file,
     image_size=args.image_size,
     prediction_type=args.prediction_type,
     model_type=args.pipeline_type,
     extract_ema=args.extract_ema,
     scheduler_type=args.scheduler_type,
     num_in_channels=args.num_in_channels,
     upcast_attention=args.upcast_attention,
     from_safetensors=args.from_safetensors,
     device=args.device,
     stable_unclip=args.stable_unclip,
     stable_unclip_prior=args.stable_unclip_prior,
     clip_stats_path=args.clip_stats_path,
     controlnet=args.controlnet,
     vae_path=args.vae_path,
 )

 if args.half:
  pipe.to(torch_dtype=torch.floataa)

 if args.controlnet:
  # only save the controlnet model
  pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
 else:
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




