



'''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
_UpperCamelCase											=		'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_UpperCamelCase											=		[
    ('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			) -> Optional[int]:
  __lowerCamelCase			:			List[str]       			=					re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'			,_lowerCAmelCase			)
  return [m.group(0			) for m in matches]

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

  __lowerCamelCase			:			List[Any]       			=					set(list(pt_models.keys()			) + list(tf_models.keys()			) + list(flax_models.keys()			)			)
  __lowerCamelCase			:			int       			=					list(_lowerCAmelCase			)
  all_models.sort()

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

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

  __lowerCamelCase			:			List[Any]       			=					[processors[t] for t in all_models]

  return pd.DataFrame(_lowerCAmelCase			)

def 			a_	(				_lowerCAmelCase			) -> Optional[Any]:
  __lowerCamelCase			:			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:
    __lowerCamelCase			:			str       			=					[model_mapping, F'TF_{model_mapping}', F'FLAX_{model_mapping}']
    __lowerCamelCase			:			List[Any]       			=					[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			:			List[Any]       			=					[]
      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			,_lowerCAmelCase			) -> Tuple:
  __lowerCamelCase			:			int       			=					get_frameworks_table()
  __lowerCamelCase			:			Optional[int]       			=					Dataset.from_pandas(_lowerCAmelCase			)

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

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

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

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

  if len(_lowerCAmelCase			) > 0:
    __lowerCamelCase			:			Any       			=					', '.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__":
   _UpperCamelCase											=		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.')
   _UpperCamelCase											=		parser.parse_args()

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











'''simple docstring'''





from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase=1E-12			) -> List[Any]:
  __lowerCamelCase			:			int       			=					jnp.divide(emb_a.T			,jnp.clip(jnp.linalg.norm(_lowerCAmelCase			,axis=1			)			,a_min=_lowerCAmelCase			)			).T
  __lowerCamelCase			:			Tuple       			=					jnp.divide(emb_a.T			,jnp.clip(jnp.linalg.norm(_lowerCAmelCase			,axis=1			)			,a_min=_lowerCAmelCase			)			).T
  return jnp.matmul(_lowerCAmelCase			,norm_emb_a.T			)






class    lowerCamelCase_       (							nn.Module	):
  """simple docstring"""




  a_				   =42
  a_				   =jnp.floataa




  def 		_lowercase	(					self :      Optional[Any]  )   ->   Tuple:
    __lowerCamelCase			:			Optional[int]       			=					FlaxCLIPVisionModule(self.config.vision_config  )
    __lowerCamelCase			:			Any       			=					nn.Dense(self.config.projection_dim     ,      use_bias=_a     ,      dtype=self.dtype  )

    __lowerCamelCase			:			str       			=					self.param('concept_embeds'     ,      jax.nn.initializers.ones     ,      (17, self.config.projection_dim)  )
    __lowerCamelCase			:			str       			=					self.param(
        'special_care_embeds'     ,      jax.nn.initializers.ones     ,      (3, self.config.projection_dim)  )

    __lowerCamelCase			:			Optional[int]       			=					self.param('concept_embeds_weights'     ,      jax.nn.initializers.ones     ,      (17,)  )
    __lowerCamelCase			:			Dict       			=					self.param('special_care_embeds_weights'     ,      jax.nn.initializers.ones     ,      (3,)  )




  def __call__(					self :      Tuple     ,      _a :      Any  )   ->   Optional[int]:
    __lowerCamelCase			:			int       			=					self.vision_model(_a  )[1]
    __lowerCamelCase			:			Optional[int]       			=					self.visual_projection(_a  )

    __lowerCamelCase			:			Optional[Any]       			=					jax_cosine_distance(_a     ,      self.special_care_embeds  )
    __lowerCamelCase			:			Tuple       			=					jax_cosine_distance(_a     ,      self.concept_embeds  )

    # increase this value to create a stronger `nfsw` filter
    # at the cost of increasing the possibility of filtering benign image inputs
    __lowerCamelCase			:			Dict       			=					0.0

    __lowerCamelCase			:			str       			=					special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
    __lowerCamelCase			:			Optional[int]       			=					jnp.round(_a     ,      3  )
    __lowerCamelCase			:			Dict       			=					jnp.any(special_scores > 0     ,      axis=1     ,      keepdims=_a  )
    # Use a lower threshold if an image has any special care concept
    __lowerCamelCase			:			Dict       			=					is_special_care * 0.01

    __lowerCamelCase			:			Optional[int]       			=					cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
    __lowerCamelCase			:			List[str]       			=					jnp.round(_a     ,      3  )
    __lowerCamelCase			:			List[Any]       			=					jnp.any(concept_scores > 0     ,      axis=1  )

    return has_nsfw_concepts






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =CLIPConfig
  a_				   ="""clip_input"""
  a_				   =FlaxStableDiffusionSafetyCheckerModule




  def __init__(					self :      List[str]     ,      _a :      CLIPConfig     ,      _a :      Optional[Tuple] = None     ,      _a :      int = 0     ,      _a :      jnp.dtype = jnp.floataa     ,      _a :      bool = True     ,      **_a :      List[Any]     ,      )   ->   List[Any]:
    if input_shape is None:
      __lowerCamelCase			:			str       			=					(1, 224, 224, 3)
    __lowerCamelCase			:			List[str]       			=					self.module_class(config=_a     ,      dtype=_a     ,      **_a  )
    super().__init__(_a     ,      _a     ,      input_shape=_a     ,      seed=_a     ,      dtype=_a     ,      _do_init=_do_init  )




  def 		_lowercase	(					self :      int     ,      _a :      jax.random.KeyArray     ,      _a :      Tuple     ,      _a :      FrozenDict = None  )   ->   FrozenDict:
    # init input tensor
    __lowerCamelCase			:			Optional[int]       			=					jax.random.normal(_a     ,      _a  )

    __lowerCamelCase			,__lowerCamelCase			:			Optional[Any]       			=					jax.random.split(_a  )
    __lowerCamelCase			:			int       			=					{'params': params_rng, 'dropout': dropout_rng}

    __lowerCamelCase			:			Tuple       			=					self.module.init(_a     ,      _a  )['params']

    return random_params




  def __call__(					self :      Optional[int]     ,      _a :      int     ,      _a :      dict = None     ,      )   ->   Dict:
    __lowerCamelCase			:			str       			=					jnp.transpose(_a     ,      (0, 2, 3, 1)  )

    return self.module.apply(
        {'params': params or self.params}     ,      jnp.array(_a     ,      dtype=jnp.floataa  )     ,      rngs={}     ,      )






