
'''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
__lowercase							:   Union[str, Any]					=     '''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
__lowercase							:   int					=     re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowercase							:   str					=     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.
__lowercase							:   Optional[int]					=     re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowercase							:   int					=     [
    ('''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_	(   _lowercase				)     ->							int:
					'''simple docstring'''

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




def lowercase_	(   )     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      Dict     =		transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					lowerCamelCase_					:      List[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.
					lowerCamelCase_					:      int     =		collections.defaultdict(_lowercase				)
					lowerCamelCase_					:      str     =		collections.defaultdict(_lowercase				)
					lowerCamelCase_					:      List[Any]     =		collections.defaultdict(_lowercase				)

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

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

					lowerCamelCase_					:      Tuple     =		set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
					lowerCamelCase_					:      Union[str, Any]     =		list(_lowercase				)
					all_models.sort()

					lowerCamelCase_					:      List[str]     =		{'''model_type''': all_models}
					lowerCamelCase_					:      Optional[Any]     =		[pt_models[t] for t in all_models]
					lowerCamelCase_					:      List[Any]     =		[tf_models[t] for t in all_models]
					lowerCamelCase_					:      Dict     =		[flax_models[t] for t in all_models]

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

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

					return pd.DataFrame(_lowercase				)




def lowercase_	(   _lowercase				)     ->							List[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      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_					:      Optional[int]     =		[auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
										# Loop through all three frameworks
										for module, cls, mapping in zip(_lowercase			,       _lowercase			,       _lowercase				):
															# The type of pipeline may not exist in this framework
															if not hasattr(_lowercase			,       _lowercase				):
																				continue
															# First extract all model_names
															lowerCamelCase_					:      Optional[Any]     =		[]
															for name in getattr(_lowercase			,       _lowercase				).values():
																				if isinstance(_lowercase			,       _lowercase				):
																									model_names.append(_lowercase				)
																				else:
																									model_names.extend(list(_lowercase				)				)

            # 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_	(   _lowercase			,       _lowercase				)     ->							Any:
					'''simple docstring'''

					lowerCamelCase_					:      Any     =		get_frameworks_table()
					lowerCamelCase_					:      Optional[Any]     =		Dataset.from_pandas(_lowercase				)

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

					# Sort the model classes to avoid some nondeterministic updates to create false update commits.
					lowerCamelCase_					:      Dict     =		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_					:      List[Any]     =		Dataset.from_pandas(_lowercase				)

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

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

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




def lowercase_	(   )     ->							Any:
					'''simple docstring'''

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

					if len(_lowercase				) > 0:
										lowerCamelCase_					:      Optional[Any]     =		''', '''.join(_lowercase				)
										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__":
					__lowercase							:   Any					=     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.''')
					__lowercase							:   Any					=     parser.parse_args()

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




'''simple docstring'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if (direction == 1 and array[indexa] > array[indexa]) or (
					    direction == 0 and array[indexa] < array[indexa]
					):
										lowerCamelCase_,      lowerCamelCase_					:      str     =		array[indexa], array[indexa]




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      int     =		int(length / 2				)
										for i in range(_lowercase			,       low + middle				):
															comp_and_swap(_lowercase			,       _lowercase			,       i + middle			,       _lowercase				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)
										bitonic_merge(_lowercase			,       low + middle			,       _lowercase			,       _lowercase				)




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)     ->							None:
					'''simple docstring'''

					if length > 1:
										lowerCamelCase_					:      Dict     =		int(length / 2				)
										bitonic_sort(_lowercase			,       _lowercase			,       _lowercase			,       1				)
										bitonic_sort(_lowercase			,       low + middle			,       _lowercase			,       0				)
										bitonic_merge(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)


if __name__ == "__main__":
					__lowercase							:   Any					=     input('''Enter numbers separated by a comma:\n''').strip()
					__lowercase							:   Tuple					=     [int(item.strip()) for item in user_input.split(''',''')]

					bitonic_sort(unsorted, 0, len(unsorted), 1)
					print('''\nSorted array in ascending order is: ''', end='''''')
					print(*unsorted, sep=''', ''')

					bitonic_merge(unsorted, 0, len(unsorted), 0)
					print('''Sorted array in descending order is: ''', end='''''')
					print(*unsorted, sep=''', ''')


