




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   :						str				=			'''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
__lowerCamelCase   :						Union[str, Any]				=			re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__lowerCamelCase   :						Tuple				=			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   :						Tuple				=			re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__lowerCamelCase   :						Dict				=			[
    ('''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__   (		__A:	Optional[Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
					__magic_name__							:	str    							=							{
					    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.
					__magic_name__							:	int    							=							collections.defaultdict(__A				)
					__magic_name__							:	Dict    							=							collections.defaultdict(__A				)
					__magic_name__							:	Optional[int]    							=							collections.defaultdict(__A				)

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

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

					__magic_name__							:	Optional[Any]    							=							set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
					__magic_name__							:	str    							=							list(__A				)
					all_models.sort()

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

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

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

					return pd.DataFrame(__A				)







def 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






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

            # 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__   (		__A:	List[str]							,__A:	Optional[Any]				):





					'''simple docstring'''






					__magic_name__							:	Optional[Any]    							=							get_frameworks_table()
					__magic_name__							:	Union[str, Any]    							=							Dataset.from_pandas(__A				)

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

					# Sort the model classes to avoid some nondeterministic updates to create false update commits.
					__magic_name__							:	int    							=							sorted(table.keys()				)
					__magic_name__							:	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],
					    }				)
					__magic_name__							:	List[str]    							=							Dataset.from_pandas(__A				)

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

										if commit_sha is not None:
															__magic_name__							:	Optional[Any]    							=							(
															    F'''Update with commit {commit_sha}\n\nSee: '''
															    F'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
															)
										else:
															__magic_name__							:	List[Any]    							=							'''Update'''

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







def 		lowercase__   (		):





					'''simple docstring'''






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

					if len(__A				) > 0:
										__magic_name__							:	Union[str, Any]    							=							''', '''.join(__A				)
										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   :						int				=			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   :						List[str]				=			parser.parse_args()

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









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



