
"""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()
_lowercase							      = logging.get_logger(__name__)

set_seed(7_70)


_lowercase							      = {
    '''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''',
}


_lowercase							      = {
    '''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''',
    },
}

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






def 					_snake_case (						snake_case__      :   Tuple		,					snake_case__      :   List[str]=False   ):
	A        =      model_type
	if use_small:
		key += "_small"
	return os.path.join(snake_case__		,					REMOTE_MODEL_PATHS[key]['file_name']   )






def 					_snake_case (						snake_case__      :   Tuple		,					snake_case__      :   Tuple   ):
	os.makedirs(snake_case__		,					exist_ok=snake_case__   )
	hf_hub_download(repo_id=snake_case__		,					filename=snake_case__		,					local_dir=snake_case__   )






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

	# convert Bark model arguments to HF Bark model arguments
	A        =      model_args.pop('n_head'   )
	A        =      model_args.pop('n_embd'   )
	A        =      model_args.pop('n_layer'   )

	A        =      ConfigClass(**checkpoint['model_args']   )
	A        =      ModelClass(config=snake_case__   )
	A        =      GenerationConfigClass()

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

			A        =      state_dict.pop(snake_case__   )

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

	return model






def 					_snake_case (						snake_case__      :   List[Any]		,					snake_case__      :   Optional[Any]=False		,					snake_case__      :   List[Any]="text"   ):
	if model_type not in ("text", "coarse", "fine"):
		raise NotImplementedError()

	A        =      'cpu'  # do conversion on cpu

	A        =      _get_ckpt_path(snake_case__		,					use_small=snake_case__   )
	A        =      _load_model(snake_case__		,					snake_case__		,					model_type=snake_case__		,					use_small=snake_case__   )

	# load bark initial model
	A        =      _bark_load_model(snake_case__		,					'cpu'		,					model_type=snake_case__		,					use_small=snake_case__   )

	if model_type == "text":
		A        =      bark_model['model']

	if model.num_parameters(exclude_embeddings=snake_case__   ) != 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
	A        =      5
	A        =      10

	if model_type in ["text", "coarse"]:
		A        =      torch.randint(256		,					(batch_size, sequence_length)		,					dtype=torch.int   )
		A        =      bark_model(snake_case__   )[0]

		A        =      model(snake_case__   )

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

	else:
		A        =      3
		A        =      8
		A        =      torch.randint(256		,					(batch_size, sequence_length, n_codes_total)		,					dtype=torch.int   )

		A        =      model(snake_case__		,					snake_case__   )
		A        =      bark_model(snake_case__		,					snake_case__   )

		A        =      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(snake_case__   ).mkdir(exist_ok=snake_case__   )
	model.save_pretrained(snake_case__   )






def 					_snake_case (						snake_case__      :   List[Any]		,					snake_case__      :   List[str]		,					snake_case__      :   Tuple		,					snake_case__      :   int		,					snake_case__      :   int		,					snake_case__      :   Union[str, Any]		,					):
	A        =      os.path.join(snake_case__		,					snake_case__   )

	A        =      BarkSemanticConfig.from_pretrained(os.path.join(snake_case__		,					'config.json'   )   )
	A        =      BarkCoarseConfig.from_pretrained(os.path.join(snake_case__		,					'config.json'   )   )
	A        =      BarkFineConfig.from_pretrained(os.path.join(snake_case__		,					'config.json'   )   )
	A        =      EncodecConfig.from_pretrained('facebook/encodec_24khz'   )

	A        =      BarkSemanticModel.from_pretrained(snake_case__   )
	A        =      BarkCoarseModel.from_pretrained(snake_case__   )
	A        =      BarkFineModel.from_pretrained(snake_case__   )
	A        =      EncodecModel.from_pretrained('facebook/encodec_24khz'   )

	A        =      BarkConfig.from_sub_model_configs(
	    snake_case__		,					snake_case__		,					snake_case__		,					snake_case__   )

	A        =      BarkGenerationConfig.from_sub_model_configs(
	    semantic.generation_config		,					coarseAcoustic.generation_config		,					fineAcoustic.generation_config   )

	A        =      BarkModel(snake_case__   )

	A        =      semantic
	A        =      coarseAcoustic
	A        =      fineAcoustic
	A        =      codec

	A        =      bark_generation_config

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


if __name__ == "__main__":
		_lowercase							      = 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.''')

		_lowercase							      = parser.parse_args()

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




"""simple docstring"""







import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCAmelCase_   						=   version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
							from nltk import word_tokenize


lowerCAmelCase_   						=   '\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

lowerCAmelCase_   						=   '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

lowerCAmelCase_   						=   '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION    )
class        __A       (	datasets.Metric    ):







		'''simple docstring'''





		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		str:






				"""simple docstring"""



				return datasets.MetricInfo(
				    description=_DESCRIPTION			,citation=_CITATION			,inputs_description=_KWARGS_DESCRIPTION			,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''string'''			,id='''sequence'''						),
				            '''references''': datasets.Value('''string'''			,id='''sequence'''						),
				        }						)			,codebase_urls=['''https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py''']			,reference_urls=[
				        '''https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score''',
				        '''https://en.wikipedia.org/wiki/METEOR''',
				    ]			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  Dict						)					->		Dict:






				"""simple docstring"""



				import nltk

				nltk.download('''wordnet'''						)
				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						nltk.download('''punkt'''						)
				if NLTK_VERSION >= version.Version('''3.6.6'''						):
						nltk.download('''omw-1.4'''						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Dict			,_snake_case						:  List[str]			,_snake_case						:  Tuple=0.9			,_snake_case						:  Optional[int]=3			,_snake_case						:  Union[str, Any]=0.5						)					->		List[str]:






				"""simple docstring"""



				if NLTK_VERSION >= version.Version('''3.6.5'''						):
						lowercase__   :    int				=			[
						    meteor_score.single_meteor_score(
						        word_tokenize(_snake_case						)			,word_tokenize(_snake_case						)			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]
				else:
						lowercase__   :    Tuple				=			[
						    meteor_score.single_meteor_score(_snake_case			,_snake_case			,alpha=_snake_case			,beta=_snake_case			,gamma=_snake_case						)
						    for ref, pred in zip(_snake_case			,_snake_case						)
						]

				return {"meteor": np.mean(_snake_case						)}
