



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	:   List[str]    =   "src/transformers"


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


# Regexes that match TF/Flax/PT model names.
_lowerCAmelCase	:   Optional[Any]    =   re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
_lowerCAmelCase	:   Optional[Any]    =   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	:   Any    =   re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_lowerCAmelCase	:   Union[str, Any]    =   [
    ("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__					)	->     Any:



				"""simple docstring"""





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







def     UpperCAmelCase_		(					)	->     Any:



				"""simple docstring"""





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

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

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

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

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

				return pd.DataFrame(snake_case__					)







def     UpperCAmelCase_		(					snake_case__					)	->     Union[str, Any]:



				"""simple docstring"""





				lowerCAmelCase__		  =  [
				    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__		  =  [model_mapping, f'TF_{model_mapping}', f'FLAX_{model_mapping}']
								lowerCAmelCase__		  =  [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
												lowerCAmelCase__		  =  []
												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__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  get_frameworks_table()
				lowerCAmelCase__		  =  Dataset.from_pandas(snake_case__					)

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

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

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







def     UpperCAmelCase_		(					)	->     Any:



				"""simple docstring"""





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

				if len(snake_case__					) > 0:
								lowerCAmelCase__		  =  ', '.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__":
	_lowerCAmelCase	:   Optional[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.")
	_lowerCAmelCase	:   Any    =   parser.parse_args()

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





import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase	:   List[str]    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Tuple    =   {
    "microsoft/wavlm-base": "https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json",
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 	__snake_case   ( SCREAMING_SNAKE_CASE ):
		SCREAMING_SNAKE_CASE__								=	'wavlm'
		def __init__(							self    ,a_=32    ,a_=768    ,a_=12    ,a_=12    ,a_=3072    ,a_="gelu"    ,a_=0.1    ,a_=0.1    ,a_=0.1    ,a_=0.0    ,a_=0.1    ,a_=0.1    ,a_=0.02    ,a_=1e-5    ,a_="group"    ,a_="gelu"    ,a_=(512, 512, 512, 512, 512, 512, 512)    ,a_=(5, 2, 2, 2, 2, 2, 2)    ,a_=(10, 3, 3, 3, 3, 2, 2)    ,a_=False    ,a_=128    ,a_=16    ,a_=320    ,a_=800    ,a_=False    ,a_=True    ,a_=0.05    ,a_=10    ,a_=2    ,a_=0.0    ,a_=10    ,a_=320    ,a_=2    ,a_=0.1    ,a_=100    ,a_=256    ,a_=256    ,a_=0.1    ,a_="mean"    ,a_=False    ,a_=False    ,a_=256    ,a_=(512, 512, 512, 512, 1500)    ,a_=(5, 3, 3, 1, 1)    ,a_=(1, 2, 3, 1, 1)    ,a_=512    ,a_=80    ,a_=0    ,a_=1    ,a_=2    ,a_=False    ,a_=3    ,a_=2    ,a_=3    ,a_=None    ,**a_    ,):







						"""simple docstring"""
						super().__init__(**a_    ,pad_token_id=a_    ,bos_token_id=a_    ,eos_token_id=a_						)
						lowerCAmelCase__		  =  hidden_size
						lowerCAmelCase__		  =  feat_extract_norm
						lowerCAmelCase__		  =  feat_extract_activation
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  conv_bias
						lowerCAmelCase__		  =  num_buckets
						lowerCAmelCase__		  =  max_bucket_distance
						lowerCAmelCase__		  =  num_conv_pos_embeddings
						lowerCAmelCase__		  =  num_conv_pos_embedding_groups
						lowerCAmelCase__		  =  len(self.conv_dim						)
						lowerCAmelCase__		  =  num_hidden_layers
						lowerCAmelCase__		  =  intermediate_size
						lowerCAmelCase__		  =  hidden_act
						lowerCAmelCase__		  =  num_attention_heads
						lowerCAmelCase__		  =  hidden_dropout
						lowerCAmelCase__		  =  attention_dropout
						lowerCAmelCase__		  =  activation_dropout
						lowerCAmelCase__		  =  feat_proj_dropout
						lowerCAmelCase__		  =  final_dropout
						lowerCAmelCase__		  =  layerdrop
						lowerCAmelCase__		  =  layer_norm_eps
						lowerCAmelCase__		  =  initializer_range
						lowerCAmelCase__		  =  num_ctc_classes
						lowerCAmelCase__		  =  vocab_size
						lowerCAmelCase__		  =  do_stable_layer_norm
						lowerCAmelCase__		  =  use_weighted_layer_sum
						lowerCAmelCase__		  =  classifier_proj_size

						if (
						    (len(self.conv_stride						) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
						    or (len(self.conv_dim						) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
										    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
										    f' {len(self.conv_dim						)}`, `len(config.conv_stride) = {len(self.conv_stride						)}`,'
										    f' `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'						)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						lowerCAmelCase__		  =  apply_spec_augment
						lowerCAmelCase__		  =  mask_time_prob
						lowerCAmelCase__		  =  mask_time_length
						lowerCAmelCase__		  =  mask_time_min_masks
						lowerCAmelCase__		  =  mask_feature_prob
						lowerCAmelCase__		  =  mask_feature_length

						# parameters for pretraining with codevector quantized representations
						lowerCAmelCase__		  =  num_codevectors_per_group
						lowerCAmelCase__		  =  num_codevector_groups
						lowerCAmelCase__		  =  contrastive_logits_temperature
						lowerCAmelCase__		  =  num_negatives
						lowerCAmelCase__		  =  codevector_dim
						lowerCAmelCase__		  =  proj_codevector_dim
						lowerCAmelCase__		  =  diversity_loss_weight

						# ctc loss
						lowerCAmelCase__		  =  ctc_loss_reduction
						lowerCAmelCase__		  =  ctc_zero_infinity

						# adapter
						lowerCAmelCase__		  =  add_adapter
						lowerCAmelCase__		  =  adapter_kernel_size
						lowerCAmelCase__		  =  adapter_stride
						lowerCAmelCase__		  =  num_adapter_layers
						lowerCAmelCase__		  =  output_hidden_size or hidden_size

						# SequenceClassification-specific parameter. Feel free to ignore for other classes.
						lowerCAmelCase__		  =  classifier_proj_size

						# XVector-specific parameters. Feel free to ignore for other classes.
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  list(a_						)
						lowerCAmelCase__		  =  xvector_output_dim



		@property
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return functools.reduce(operator.mul    ,self.conv_stride    ,1						)
