






"""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      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      Any       = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""  ,				_A )
				return [m.group(0 ) for m in matches]


def      UpperCamelCase       ( )     ->       Optional[Any]:
				lowercase    :      Optional[int]       = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
				lowercase    :      int       = {
				    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    :      Union[str, Any]       = collections.defaultdict(_A )
				lowercase    :      Optional[Any]       = collections.defaultdict(_A )
				lowercase    :      Dict       = 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 ):
								lowercase    :      int       = None
								if _re_tf_models.match(_A ) is not None:
												lowercase    :      Optional[Any]       = tf_models
												lowercase    :      int       = _re_tf_models.match(_A ).groups()[0]
								elif _re_flax_models.match(_A ) is not None:
												lowercase    :      Optional[Any]       = flax_models
												lowercase    :      Union[str, Any]       = _re_flax_models.match(_A ).groups()[0]
								elif _re_pt_models.match(_A ) is not None:
												lowercase    :      int       = pt_models
												lowercase    :      Optional[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:
																				lowercase    :      str       = True
																				break
																# Try again after removing the last word in the name
																lowercase    :      int       = """""".join(camel_case_split(_A )[:-1] )

				lowercase    :      Any       = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
				lowercase    :      Optional[int]       = list(_A )
				all_models.sort()

				lowercase    :      Any       = {"""model_type""": all_models}
				lowercase    :      Union[str, Any]       = [pt_models[t] for t in all_models]
				lowercase    :      Tuple       = [tf_models[t] for t in all_models]
				lowercase    :      int       = [flax_models[t] for t in all_models]

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

				lowercase    :      Dict       = [processors[t] for t in all_models]

				return pd.DataFrame(_A )


def      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      Dict       = [
				    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    :      Any       = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
								lowercase    :      Union[str, Any]       = [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
												lowercase    :      List[str]       = []
												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      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				lowercase    :      int       = get_frameworks_table()
				lowercase    :      str       = Dataset.from_pandas(_A )

				lowercase    :      Union[str, Any]       = hf_hub_download(
				    """huggingface/transformers-metadata"""  ,				"""pipeline_tags.json"""  ,				repo_type="""dataset"""  ,				token=_A )
				lowercase    :      Any       = Dataset.from_json(_A )
				lowercase    :      str       = {
				    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
				    for i in range(len(_A ) )
				}
				lowercase    :      str       = update_pipeline_and_auto_class_table(_A )

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

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


def      UpperCamelCase       ( )     ->       Optional[int]:
				lowercase    :      Tuple       = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
				lowercase    :      str       = transformers_module.pipelines.SUPPORTED_TASKS
				lowercase    :      str       = []
				for key in pipeline_tasks:
								if key not in in_table:
												lowercase    :      Optional[int]       = pipeline_tasks[key]["""pt"""]
												if isinstance(_A  ,				(list, tuple) ):
																lowercase    :      Tuple       = model[0]
												lowercase    :      Any       = model.__name__
												if model not in in_table.values():
																missing.append(_A )

				if len(_A ) > 0:
								lowercase    :      Dict       = """, """.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            =	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 argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase            =	logging.get_logger(__name__)

set_seed(7_70)


_lowerCAmelCase            =	{
    'c_attn': 'att_proj',
    'c_proj': 'out_proj',
    'c_fc': 'in_proj',
    'transformer.': '',
    'h.': 'layers.',
    'ln_1': 'layernorm_1',
    'ln_2': 'layernorm_2',
    'ln_f': 'layernorm_final',
    'wpe': 'position_embeds_layer',
    'wte': 'input_embeds_layer',
}


_lowerCAmelCase            =	{
    'text_small': {
        'repo_id': 'suno/bark',
        'file_name': 'text.pt',
    },
    'coarse_small': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse.pt',
    },
    'fine_small': {
        'repo_id': 'suno/bark',
        'file_name': 'fine.pt',
    },
    'text': {
        'repo_id': 'suno/bark',
        'file_name': 'text_2.pt',
    },
    'coarse': {
        'repo_id': 'suno/bark',
        'file_name': 'coarse_2.pt',
    },
    'fine': {
        'repo_id': 'suno/bark',
        'file_name': 'fine_2.pt',
    },
}

_lowerCAmelCase            =	os.path.dirname(os.path.abspath(__file__))
_lowerCAmelCase            =	os.path.join(os.path.expanduser('~'), '.cache')
_lowerCAmelCase            =	os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')


def      UpperCamelCase       ( _A  ,				_A=False )     ->       Dict:
				lowercase    :      Union[str, Any]       = model_type
				if use_small:
								key += "_small"
				return os.path.join(_A  ,				REMOTE_MODEL_PATHS[key]["""file_name"""] )


def      UpperCamelCase       ( _A  ,				_A )     ->       List[Any]:
				os.makedirs(_A  ,				exist_ok=_A )
				hf_hub_download(repo_id=_A  ,				filename=_A  ,				local_dir=_A )


def      UpperCamelCase       ( _A  ,				_A  ,				_A=False  ,				_A="text" )     ->       List[Any]:
				if model_type == "text":
								lowercase    :      Any       = BarkSemanticModel
								lowercase    :      List[Any]       = BarkSemanticConfig
								lowercase    :      List[Any]       = BarkSemanticGenerationConfig
				elif model_type == "coarse":
								lowercase    :      Tuple       = BarkCoarseModel
								lowercase    :      str       = BarkCoarseConfig
								lowercase    :      Optional[int]       = BarkCoarseGenerationConfig
				elif model_type == "fine":
								lowercase    :      str       = BarkFineModel
								lowercase    :      Optional[Any]       = BarkFineConfig
								lowercase    :      Optional[int]       = BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				lowercase    :      Optional[int]       = F"""{model_type}_small""" if use_small else model_type
				lowercase    :      str       = REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(_A ):
								logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`.""" )
								_download(model_info["""repo_id"""]  ,				model_info["""file_name"""] )
				lowercase    :      Dict       = torch.load(_A  ,				map_location=_A )
				# this is a hack
				lowercase    :      int       = checkpoint["""model_args"""]
				if "input_vocab_size" not in model_args:
								lowercase    :      str       = model_args["""vocab_size"""]
								lowercase    :      Optional[Any]       = model_args["""vocab_size"""]
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				lowercase    :      Optional[int]       = model_args.pop("""n_head""" )
				lowercase    :      Union[str, Any]       = model_args.pop("""n_embd""" )
				lowercase    :      Dict       = model_args.pop("""n_layer""" )

				lowercase    :      List[str]       = ConfigClass(**checkpoint["""model_args"""] )
				lowercase    :      Dict       = ModelClass(config=_A )
				lowercase    :      List[str]       = GenerationConfigClass()

				lowercase    :      int       = model_generation_config
				lowercase    :      List[Any]       = checkpoint["""model"""]
				# fixup checkpoint
				lowercase    :      Union[str, Any]       = """_orig_mod."""
				for k, v in list(state_dict.items() ):
								if k.startswith(_A ):
												# replace part of the key with corresponding layer name in HF implementation
												lowercase    :      List[str]       = k[len(_A ) :]
												for old_layer_name in new_layer_name_dict:
																lowercase    :      int       = new_k.replace(_A  ,				new_layer_name_dict[old_layer_name] )

												lowercase    :      List[Any]       = state_dict.pop(_A )

				lowercase    :      Dict       = set(state_dict.keys() ) - set(model.state_dict().keys() )
				lowercase    :      Dict       = {k for k in extra_keys if not k.endswith(""".attn.bias""" )}
				lowercase    :      Optional[int]       = set(model.state_dict().keys() ) - set(state_dict.keys() )
				lowercase    :      Dict       = {k for k in missing_keys if not k.endswith(""".attn.bias""" )}
				if len(_A ) != 0:
								raise ValueError(F"""extra keys found: {extra_keys}""" )
				if len(_A ) != 0:
								raise ValueError(F"""missing keys: {missing_keys}""" )
				model.load_state_dict(_A  ,				strict=_A )
				lowercase    :      Union[str, Any]       = model.num_parameters(exclude_embeddings=_A )
				lowercase    :      Union[str, Any]       = checkpoint["""best_val_loss"""].item()
				logger.info(F"""model loaded: {round(n_params/1e6  ,				1 )}M params, {round(_A  ,				3 )} loss""" )
				model.eval()
				model.to(_A )
				del checkpoint, state_dict

				return model


def      UpperCamelCase       ( _A  ,				_A=False  ,				_A="text" )     ->       Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				lowercase    :      Optional[Any]       = """cpu"""  # do conversion on cpu

				lowercase    :      Dict       = _get_ckpt_path(_A  ,				use_small=_A )
				lowercase    :      Any       = _load_model(_A  ,				_A  ,				model_type=_A  ,				use_small=_A )

				# load bark initial model
				lowercase    :      Tuple       = _bark_load_model(_A  ,				"""cpu"""  ,				model_type=_A  ,				use_small=_A )

				if model_type == "text":
								lowercase    :      str       = bark_model["""model"""]

				if model.num_parameters(exclude_embeddings=_A ) != bark_model.get_num_params():
								raise ValueError("""initial and new models don't have the same number of parameters""" )

				# check if same output as the bark model
				lowercase    :      Union[str, Any]       = 5
				lowercase    :      Tuple       = 10

				if model_type in ["text", "coarse"]:
								lowercase    :      Any       = torch.randint(256  ,				(batch_size, sequence_length)  ,				dtype=torch.int )
								lowercase    :      Optional[Any]       = bark_model(_A )[0]

								lowercase    :      Tuple       = model(_A )

								# take last logits
								lowercase    :      int       = output_new_model_total.logits[:, [-1], :]

				else:
								lowercase    :      Dict       = 3
								lowercase    :      Any       = 8
								lowercase    :      str       = torch.randint(256  ,				(batch_size, sequence_length, n_codes_total)  ,				dtype=torch.int )

								lowercase    :      str       = model(_A  ,				_A )
								lowercase    :      List[Any]       = bark_model(_A  ,				_A )

								lowercase    :      Any       = output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError("""initial and new outputs don't have the same shape""" )
				if (output_new_model - output_old_model).abs().max().item() > 1e-3:
								raise ValueError("""initial and new outputs are not equal""" )

				Path(_A ).mkdir(exist_ok=_A )
				model.save_pretrained(_A )


def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A  ,				_A  ,				_A  ,				)     ->       Optional[int]:
				lowercase    :      str       = os.path.join(_A  ,				_A )

				lowercase    :      Dict       = BarkSemanticConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkCoarseConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = BarkFineConfig.from_pretrained(os.path.join(_A  ,				"""config.json""" ) )
				lowercase    :      List[Any]       = EncodecConfig.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      Any       = BarkSemanticModel.from_pretrained(_A )
				lowercase    :      str       = BarkCoarseModel.from_pretrained(_A )
				lowercase    :      int       = BarkFineModel.from_pretrained(_A )
				lowercase    :      Optional[Any]       = EncodecModel.from_pretrained("""facebook/encodec_24khz""" )

				lowercase    :      int       = BarkConfig.from_sub_model_configs(
				    _A  ,				_A  ,				_A  ,				_A )

				lowercase    :      Dict       = BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config  ,				coarseAcoustic.generation_config  ,				fineAcoustic.generation_config )

				lowercase    :      str       = BarkModel(_A )

				lowercase    :      int       = semantic
				lowercase    :      List[Any]       = coarseAcoustic
				lowercase    :      Union[str, Any]       = fineAcoustic
				lowercase    :      Any       = codec

				lowercase    :      int       = bark_generation_config

				Path(_A ).mkdir(exist_ok=_A )
				bark.save_pretrained(_A  ,				repo_id=_A  ,				push_to_hub=_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser()
				# Required parameters

				parser.add_argument('model_type', type=str, help='text, coarse or fine.')
				parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

				_lowerCAmelCase            =	parser.parse_args()

				load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
