
'''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
__lowerCAmelCase												=	'src/transformers'


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


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


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCAmelCase												=	[
    ('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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""							, _SCREAMING_SNAKE_CASE				)
							return [m.group(0				) for m in matches]






def  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
							_snake_case       =  {
							    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       =  collections.defaultdict(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  collections.defaultdict(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  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       =  None
														if _re_tf_models.match(_SCREAMING_SNAKE_CASE				) is not None:
																					_snake_case       =  tf_models
																					_snake_case       =  _re_tf_models.match(_SCREAMING_SNAKE_CASE				).groups()[0]
														elif _re_flax_models.match(_SCREAMING_SNAKE_CASE				) is not None:
																					_snake_case       =  flax_models
																					_snake_case       =  _re_flax_models.match(_SCREAMING_SNAKE_CASE				).groups()[0]
														elif _re_pt_models.match(_SCREAMING_SNAKE_CASE				) is not None:
																					_snake_case       =  pt_models
																					_snake_case       =  _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       =  True
																																			break
																												# Try again after removing the last word in the name
																												_snake_case       =  """""".join(camel_case_split(_SCREAMING_SNAKE_CASE				)[:-1]				)

							_snake_case       =  set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
							_snake_case       =  list(_SCREAMING_SNAKE_CASE				)
							all_models.sort()

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

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

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

							return pd.DataFrame(_SCREAMING_SNAKE_CASE				)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  [
							    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       =  [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
														_snake_case       =  [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       =  []
																					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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  get_frameworks_table()
							_snake_case       =  Dataset.from_pandas(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  hf_hub_download(
							    """huggingface/transformers-metadata"""							, """pipeline_tags.json"""							, repo_type="""dataset"""							, token=_SCREAMING_SNAKE_CASE				)
							_snake_case       =  Dataset.from_json(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  {
							    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       =  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       =  sorted(table.keys()				)
							_snake_case       =  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       =  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       =  (
																					    f"""Update with commit {commit_sha}\n\nSee: """
																					    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																					)
														else:
																					_snake_case       =  """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  __SCREAMING_SNAKE_CASE				(  ):
							_snake_case       =  {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
							_snake_case       =  transformers_module.pipelines.SUPPORTED_TASKS
							_snake_case       =  []
							for key in pipeline_tasks:
														if key not in in_table:
																					_snake_case       =  pipeline_tasks[key]["""pt"""]
																					if isinstance(_SCREAMING_SNAKE_CASE							, (list, tuple)				):
																												_snake_case       =  model[0]
																					_snake_case       =  model.__name__
																					if model not in in_table.values():
																												missing.append(_SCREAMING_SNAKE_CASE				)

							if len(_SCREAMING_SNAKE_CASE				) > 0:
														_snake_case       =  """, """.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__":
				__lowerCAmelCase												=	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.')
				__lowerCAmelCase												=	parser.parse_args()

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

'''simple docstring'''
import qiskit






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
							_snake_case       =  qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							_snake_case       =  qiskit.QuantumCircuit(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)

							# Apply X (NOT) Gate to Qubits 0 & 1
							circuit.x(0				)
							circuit.x(1				)

							# Map the quantum measurement to the classical bits
							circuit.measure([0, 1]							, [0, 1]				)

							# Execute the circuit on the qasm simulator
							_snake_case       =  qiskit.execute(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, shots=1000				)

							# Return the histogram data of the results of the experiment.
							return job.result().get_counts(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__lowerCAmelCase												=	single_qubit_measure(2, 2)
				print(f'''Total count for various states are: {counts}''')