
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 				a						(							snake_case__:					Optional[Any]					):


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




def 				a						(							):


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

				lowercase_       =					set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
				lowercase_       =					list(snake_case__					)
				all_models.sort()

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

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

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

				return pd.DataFrame(snake_case__					)




def 				a						(							snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				lowercase_       =					[
				    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:
								lowercase_       =					[model_mapping, F'''TF_{model_mapping}''', F'''FLAX_{model_mapping}''']
								lowercase_       =					[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
												lowercase_       =					[]
												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 				a						(							snake_case__:					List[Any]						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				lowercase_       =					get_frameworks_table()
				lowercase_       =					Dataset.from_pandas(snake_case__					)

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

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

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




def 				a						(							):


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

				if len(snake_case__					) > 0:
								lowercase_       =					''', '''.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)






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'upernet'
	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[1, 2, 3, 6]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_8_4       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_5       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
									lowercase_       =					CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					backbone_config.get('''model_type'''       )
									lowercase_       =					CONFIG_MAPPING[backbone_model_type]
									lowercase_       =					config_class.from_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					backbone_config
					lowercase_       =					hidden_size
					lowercase_       =					initializer_range
					lowercase_       =					pool_scales
					lowercase_       =					use_auxiliary_head
					lowercase_       =					auxiliary_loss_weight
					lowercase_       =					auxiliary_in_channels
					lowercase_       =					auxiliary_channels
					lowercase_       =					auxiliary_num_convs
					lowercase_       =					auxiliary_concat_input
					lowercase_       =					loss_ignore_index




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.backbone_config.to_dict()
					lowercase_       =					self.__class__.model_type
					return output




