


'''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				:List[Any]        =      "src/transformers"


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


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase				:Dict        =      re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowerCAmelCase				:List[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.
lowerCAmelCase				:Any        =      re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase				:List[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      lowerCamelCase      (      lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







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





def      lowerCamelCase      (      ):





	"""simple docstring"""







	__magic_name__	:   Optional[Any]					     =					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
	__magic_name__	:   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.
	__magic_name__	:   Union[str, Any]					     =					collections.defaultdict(lowerCamelCase_		)
	__magic_name__	:   Any					     =					collections.defaultdict(lowerCamelCase_		)
	__magic_name__	:   int					     =					collections.defaultdict(lowerCamelCase_		)

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

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

	__magic_name__	:   List[Any]					     =					set(list(pt_models.keys()		) + list(tf_models.keys()		) + list(flax_models.keys()		)		)
	__magic_name__	:   Dict					     =					list(lowerCamelCase_		)
	all_models.sort()

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

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

	__magic_name__	:   Any					     =					[processors[t] for t in all_models]

	return pd.DataFrame(lowerCamelCase_		)





def      lowerCamelCase      (      lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	__magic_name__	:   Union[str, 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__	:   List[str]					     =					[auto_class, f'TF_{auto_class}', f'Flax_{auto_class}']
		# Loop through all three frameworks
		for module, cls, mapping in zip(lowerCamelCase_		,		lowerCamelCase_		,		lowerCamelCase_		):
			# The type of pipeline may not exist in this framework
			if not hasattr(lowerCamelCase_		,		lowerCamelCase_		):
				continue
			# First extract all model_names
			__magic_name__	:   Union[str, Any]					     =					[]
			for name in getattr(lowerCamelCase_		,		lowerCamelCase_		).values():
				if isinstance(lowerCamelCase_		,		lowerCamelCase_		):
					model_names.append(lowerCamelCase_		)
				else:
					model_names.extend(list(lowerCamelCase_		)		)

            # 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      lowerCamelCase      (      lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	__magic_name__	:   Tuple					     =					get_frameworks_table()
	__magic_name__	:   Any					     =					Dataset.from_pandas(lowerCamelCase_		)

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

	# Sort the model classes to avoid some nondeterministic updates to create false update commits.
	__magic_name__	:   int					     =					sorted(table.keys()		)
	__magic_name__	:   Optional[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__	:   int					     =					Dataset.from_pandas(lowerCamelCase_		)

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

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

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





def      lowerCamelCase      (      ):





	"""simple docstring"""







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

	if len(lowerCamelCase_		) > 0:
		__magic_name__	:   Dict					     =					', '.join(lowerCamelCase_		)
		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				:Dict        =      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				:Optional[int]        =      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				:Optional[int]        =      logging.get_logger(__name__)

set_seed(7_7_0)


lowerCAmelCase				:str        =      {
    '''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				:Any        =      {
    '''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				:List[Any]        =      os.path.dirname(os.path.abspath(__file__))
lowerCAmelCase				:List[Any]        =      os.path.join(os.path.expanduser('''~'''), '''.cache''')
lowerCAmelCase				:List[str]        =      os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')





def lowerCamelCase      (      lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]=False		):





	"""simple docstring"""







	__magic_name__	:   str					     =					model_type
	if use_small:
		key += "_small"
	return os.path.join(lowerCAmelCase		,		REMOTE_MODEL_PATHS[key]['file_name']		)





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Optional[Any]		):





	"""simple docstring"""







	os.makedirs(lowerCAmelCase		,		exist_ok=lowerCAmelCase		)
	hf_hub_download(repo_id=lowerCAmelCase		,		filename=lowerCAmelCase		,		local_dir=lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]=False		,		lowerCAmelCase    :     str="text"		):





	"""simple docstring"""







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

	# convert Bark model arguments to HF Bark model arguments
	__magic_name__	:   Optional[Any]					     =					model_args.pop('n_head'		)
	__magic_name__	:   List[str]					     =					model_args.pop('n_embd'		)
	__magic_name__	:   List[Any]					     =					model_args.pop('n_layer'		)

	__magic_name__	:   Optional[Any]					     =					ConfigClass(**checkpoint['model_args']		)
	__magic_name__	:   Any					     =					ModelClass(config=lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					GenerationConfigClass()

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

			__magic_name__	:   Union[str, Any]					     =					state_dict.pop(lowerCAmelCase		)

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

	return model





def lowerCamelCase      (      lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]=False		,		lowerCAmelCase    :     Tuple="text"		):





	"""simple docstring"""







	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	__magic_name__	:   List[str]					     =					'cpu'  # do conversion on cpu

	__magic_name__	:   int					     =					_get_ckpt_path(lowerCAmelCase		,		use_small=lowerCAmelCase		)
	__magic_name__	:   Any					     =					_load_model(lowerCAmelCase		,		lowerCAmelCase		,		model_type=lowerCAmelCase		,		use_small=lowerCAmelCase		)

	# load bark initial model
	__magic_name__	:   List[str]					     =					_bark_load_model(lowerCAmelCase		,		'cpu'		,		model_type=lowerCAmelCase		,		use_small=lowerCAmelCase		)

	if model_type == "text":
		__magic_name__	:   int					     =					bark_model['model']

	if model.num_parameters(exclude_embeddings=lowerCAmelCase		) != 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
	__magic_name__	:   Union[str, Any]					     =					5
	__magic_name__	:   Optional[int]					     =					10

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

		__magic_name__	:   Optional[int]					     =					model(lowerCAmelCase		)

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

	else:
		__magic_name__	:   Tuple					     =					3
		__magic_name__	:   List[str]					     =					8
		__magic_name__	:   List[str]					     =					torch.randint(256		,		(batch_size, sequence_length, n_codes_total)		,		dtype=torch.int		)

		__magic_name__	:   str					     =					model(lowerCAmelCase		,		lowerCAmelCase		)
		__magic_name__	:   Tuple					     =					bark_model(lowerCAmelCase		,		lowerCAmelCase		)

		__magic_name__	:   Tuple					     =					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(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)
	model.save_pretrained(lowerCAmelCase		)





def lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     List[str]		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     str		,		):





	"""simple docstring"""







	__magic_name__	:   Union[str, Any]					     =					os.path.join(lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   Dict					     =					BarkSemanticConfig.from_pretrained(os.path.join(lowerCAmelCase		,		'config.json'		)		)
	__magic_name__	:   str					     =					BarkCoarseConfig.from_pretrained(os.path.join(lowerCAmelCase		,		'config.json'		)		)
	__magic_name__	:   int					     =					BarkFineConfig.from_pretrained(os.path.join(lowerCAmelCase		,		'config.json'		)		)
	__magic_name__	:   List[Any]					     =					EncodecConfig.from_pretrained('facebook/encodec_24khz'		)

	__magic_name__	:   Optional[int]					     =					BarkSemanticModel.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   Dict					     =					BarkCoarseModel.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   List[str]					     =					BarkFineModel.from_pretrained(lowerCAmelCase		)
	__magic_name__	:   Optional[Any]					     =					EncodecModel.from_pretrained('facebook/encodec_24khz'		)

	__magic_name__	:   Dict					     =					BarkConfig.from_sub_model_configs(
	    lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		,		lowerCAmelCase		)

	__magic_name__	:   List[Any]					     =					BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config		,		coarseAcoustic.generation_config		,		fineAcoustic.generation_config		)

	__magic_name__	:   int					     =					BarkModel(lowerCAmelCase		)

	__magic_name__	:   List[str]					     =					semantic
	__magic_name__	:   Optional[int]					     =					coarseAcoustic
	__magic_name__	:   List[str]					     =					fineAcoustic
	__magic_name__	:   int					     =					codec

	__magic_name__	:   Union[str, Any]					     =					bark_generation_config

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


if __name__ == "__main__":
							lowerCAmelCase				:Optional[Any]        =      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				:Union[str, Any]        =      parser.parse_args()

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