




"""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_      =    '''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  UpperCAmelCase__	(snake_case__							: Dict   ):
  """simple docstring"""

  _snake_case							:		Union[str, Any]								=  re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""		,       snake_case__   )
  return [m.group(0   ) for m in matches]


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		List[str]								=  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  _snake_case							:		Union[str, 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.
  _snake_case							:		Optional[Any]								=  collections.defaultdict(snake_case__   )
  _snake_case							:		Any								=  collections.defaultdict(snake_case__   )
  _snake_case							:		List[str]								=  collections.defaultdict(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(snake_case__   ):
    _snake_case							:		Dict								=  None
    if _re_tf_models.match(snake_case__   ) is not None:
      _snake_case							:		Optional[Any]								=  tf_models
      _snake_case							:		List[str]								=  _re_tf_models.match(snake_case__   ).groups()[0]
    elif _re_flax_models.match(snake_case__   ) is not None:
      _snake_case							:		List[Any]								=  flax_models
      _snake_case							:		str								=  _re_flax_models.match(snake_case__   ).groups()[0]
    elif _re_pt_models.match(snake_case__   ) is not None:
      _snake_case							:		int								=  pt_models
      _snake_case							:		Any								=  _re_pt_models.match(snake_case__   ).groups()[0]

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

  _snake_case							:		str								=  set(list(pt_models.keys()   ) + list(tf_models.keys()   ) + list(flax_models.keys()   )   )
  _snake_case							:		List[Any]								=  list(snake_case__   )
  all_models.sort()

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

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

  _snake_case							:		Dict								=  [processors[t] for t in all_models]

  return pd.DataFrame(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: List[str]   ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [
      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							:		List[Any]								=  [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
    _snake_case							:		Optional[int]								=  [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
    # Loop through all three frameworks
    for module, cls, mapping in zip(snake_case__		,       snake_case__		,       snake_case__   ):
      # The type of pipeline may not exist in this framework
      if not hasattr(snake_case__		,       snake_case__   ):
        continue
      # First extract all model_names
      _snake_case							:		Tuple								=  []
      for name in getattr(snake_case__		,       snake_case__   ).values():
        if isinstance(snake_case__		,       snake_case__   ):
          model_names.append(snake_case__   )
        else:
          model_names.extend(list(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  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  get_frameworks_table()
  _snake_case							:		Optional[Any]								=  Dataset.from_pandas(snake_case__   )

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

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  _snake_case							:		Union[str, Any]								=  sorted(table.keys()   )
  _snake_case							:		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							:		List[str]								=  Dataset.from_pandas(snake_case__   )

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(snake_case__		,       """frameworks.json"""   )   )
    tags_dataset.to_json(os.path.join(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							:		Optional[Any]								=  """Update"""

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


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		List[Any]								=  {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  _snake_case							:		Optional[int]								=  transformers_module.pipelines.SUPPORTED_TASKS
  _snake_case							:		Any								=  []
  for key in pipeline_tasks:
    if key not in in_table:
      _snake_case							:		Optional[Any]								=  pipeline_tasks[key]["""pt"""]
      if isinstance(snake_case__		,       (list, tuple)   ):
        _snake_case							:		Any								=  model[0]
      _snake_case							:		List[str]								=  model.__name__
      if model not in in_table.values():
        missing.append(snake_case__   )

  if len(snake_case__   ) > 0:
    _snake_case							:		List[str]								=  """, """.join(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)








"""simple docstring"""


import math

import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def  UpperCAmelCase__	(snake_case__							: int = 3   ):
  """simple docstring"""

  if isinstance(snake_case__		,       snake_case__   ):
    raise TypeError("""number of qubits must be a integer."""   )
  if number_of_qubits <= 0:
    raise ValueError("""number of qubits must be > 0."""   )
  if math.floor(snake_case__   ) != number_of_qubits:
    raise ValueError("""number of qubits must be exact integer."""   )
  if number_of_qubits > 10:
    raise ValueError("""number of qubits too large to simulate(>10)."""   )

  _snake_case							:		Optional[int]								=  QuantumRegister(snake_case__		,       """qr"""   )
  _snake_case							:		List[Any]								=  ClassicalRegister(snake_case__		,       """cr"""   )

  _snake_case							:		Optional[int]								=  QuantumCircuit(snake_case__		,       snake_case__   )

  _snake_case							:		Dict								=  number_of_qubits

  for i in range(snake_case__   ):
    quantum_circuit.h(number_of_qubits - i - 1   )
    counter -= 1
    for j in range(snake_case__   ):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)		,       snake_case__		,       snake_case__   )

  for k in range(number_of_qubits // 2   ):
    quantum_circuit.swap(snake_case__		,       number_of_qubits - k - 1   )

  # measure all the qubits
  quantum_circuit.measure(snake_case__		,       snake_case__   )
  # simulate with 10000 shots
  _snake_case							:		Optional[int]								=  Aer.get_backend("""qasm_simulator"""   )
  _snake_case							:		Optional[int]								=  execute(snake_case__		,       snake_case__		,       shots=1_00_00   )

  return job.result().get_counts(snake_case__   )


if __name__ == "__main__":
     print(
         F'''Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'''
     )


