



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()
__snake_case				:		Any										=logging.get_logger(__name__)

set_seed(7_7_0)


__snake_case				:		Tuple										={
    """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""",
}


__snake_case				:		Optional[int]										={
    """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""",
    },
}

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






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Dict       ,lowerCamelCase_		:		Optional[int]=False):





				'''simple docstring'''

				lowerCAmelCase__     :				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_		:		List[Any]):





				'''simple docstring'''

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






def 	lowerCAmelCase__		(    lowerCamelCase_		:		Any       ,lowerCamelCase_		:		Dict       ,lowerCamelCase_		:		Optional[Any]=False       ,lowerCamelCase_		:		Union[str, Any]="text"):





				'''simple docstring'''

				if model_type == "text":
								lowerCAmelCase__     :				Any												=     BarkSemanticModel
								lowerCAmelCase__     :				Optional[Any]												=     BarkSemanticConfig
								lowerCAmelCase__     :				List[str]												=     BarkSemanticGenerationConfig
				elif model_type == "coarse":
								lowerCAmelCase__     :				List[Any]												=     BarkCoarseModel
								lowerCAmelCase__     :				Any												=     BarkCoarseConfig
								lowerCAmelCase__     :				Tuple												=     BarkCoarseGenerationConfig
				elif model_type == "fine":
								lowerCAmelCase__     :				Optional[int]												=     BarkFineModel
								lowerCAmelCase__     :				str												=     BarkFineConfig
								lowerCAmelCase__     :				int												=     BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				lowerCAmelCase__     :				Optional[Any]												=     f"""{model_type}_small""" if use_small else model_type
				lowerCAmelCase__     :				int												=     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'''])
				lowerCAmelCase__     :				Dict												=     torch.load(lowerCamelCase_       ,map_location=lowerCamelCase_)
				# this is a hack
				lowerCAmelCase__     :				Tuple												=     checkpoint['''model_args''']
				if "input_vocab_size" not in model_args:
								lowerCAmelCase__     :				int												=     model_args['''vocab_size''']
								lowerCAmelCase__     :				Optional[int]												=     model_args['''vocab_size''']
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				lowerCAmelCase__     :				str												=     model_args.pop('''n_head''')
				lowerCAmelCase__     :				int												=     model_args.pop('''n_embd''')
				lowerCAmelCase__     :				int												=     model_args.pop('''n_layer''')

				lowerCAmelCase__     :				List[Any]												=     ConfigClass(**checkpoint['''model_args'''])
				lowerCAmelCase__     :				Union[str, Any]												=     ModelClass(config=lowerCamelCase_)
				lowerCAmelCase__     :				List[str]												=     GenerationConfigClass()

				lowerCAmelCase__     :				Optional[Any]												=     model_generation_config
				lowerCAmelCase__     :				List[str]												=     checkpoint['''model''']
				# fixup checkpoint
				lowerCAmelCase__     :				List[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
												lowerCAmelCase__     :				str												=     k[len(lowerCamelCase_) :]
												for old_layer_name in new_layer_name_dict:
																lowerCAmelCase__     :				Any												=     new_k.replace(lowerCamelCase_       ,new_layer_name_dict[old_layer_name])

												lowerCAmelCase__     :				Optional[int]												=     state_dict.pop(lowerCamelCase_)

				lowerCAmelCase__     :				Any												=     set(state_dict.keys()) - set(model.state_dict().keys())
				lowerCAmelCase__     :				Tuple												=     {k for k in extra_keys if not k.endswith('''.attn.bias''')}
				lowerCAmelCase__     :				Tuple												=     set(model.state_dict().keys()) - set(state_dict.keys())
				lowerCAmelCase__     :				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_)
				lowerCAmelCase__     :				Tuple												=     model.num_parameters(exclude_embeddings=lowerCamelCase_)
				lowerCAmelCase__     :				str												=     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_		:		Optional[Any]       ,lowerCamelCase_		:		Union[str, Any]=False       ,lowerCamelCase_		:		Tuple="text"):





				'''simple docstring'''

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

				lowerCAmelCase__     :				Any												=     '''cpu'''  # do conversion on cpu

				lowerCAmelCase__     :				Union[str, Any]												=     _get_ckpt_path(lowerCamelCase_       ,use_small=lowerCamelCase_)
				lowerCAmelCase__     :				List[Any]												=     _load_model(lowerCamelCase_       ,lowerCamelCase_       ,model_type=lowerCamelCase_       ,use_small=lowerCamelCase_)

				# load bark initial model
				lowerCAmelCase__     :				Dict												=     _bark_load_model(lowerCamelCase_       ,'''cpu'''       ,model_type=lowerCamelCase_       ,use_small=lowerCamelCase_)

				if model_type == "text":
								lowerCAmelCase__     :				List[Any]												=     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
				lowerCAmelCase__     :				Dict												=     5
				lowerCAmelCase__     :				str												=     10

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

								lowerCAmelCase__     :				Tuple												=     model(lowerCamelCase_)

								# take last logits
								lowerCAmelCase__     :				Optional[Any]												=     output_new_model_total.logits[:, [-1], :]

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

								lowerCAmelCase__     :				Tuple												=     model(lowerCamelCase_       ,lowerCamelCase_)
								lowerCAmelCase__     :				Any												=     bark_model(lowerCamelCase_       ,lowerCamelCase_)

								lowerCAmelCase__     :				str												=     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_		:		Tuple       ,lowerCamelCase_		:		Optional[int]       ,lowerCamelCase_		:		Optional[Any]       ,lowerCamelCase_		:		Optional[Any]       ,lowerCamelCase_		:		Any       ,lowerCamelCase_		:		Any       ,):





				'''simple docstring'''

				lowerCAmelCase__     :				List[Any]												=     os.path.join(lowerCamelCase_       ,lowerCamelCase_)

				lowerCAmelCase__     :				List[Any]												=     BarkSemanticConfig.from_pretrained(os.path.join(lowerCamelCase_       ,'''config.json'''))
				lowerCAmelCase__     :				List[str]												=     BarkCoarseConfig.from_pretrained(os.path.join(lowerCamelCase_       ,'''config.json'''))
				lowerCAmelCase__     :				Union[str, Any]												=     BarkFineConfig.from_pretrained(os.path.join(lowerCamelCase_       ,'''config.json'''))
				lowerCAmelCase__     :				Optional[int]												=     EncodecConfig.from_pretrained('''facebook/encodec_24khz''')

				lowerCAmelCase__     :				Any												=     BarkSemanticModel.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__     :				Dict												=     BarkCoarseModel.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__     :				Tuple												=     BarkFineModel.from_pretrained(lowerCamelCase_)
				lowerCAmelCase__     :				Optional[int]												=     EncodecModel.from_pretrained('''facebook/encodec_24khz''')

				lowerCAmelCase__     :				Any												=     BarkConfig.from_sub_model_configs(
				    lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_)

				lowerCAmelCase__     :				Optional[int]												=     BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config       ,coarseAcoustic.generation_config       ,fineAcoustic.generation_config)

				lowerCAmelCase__     :				List[str]												=     BarkModel(lowerCamelCase_)

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

				lowerCAmelCase__     :				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__":
							__snake_case				:		Optional[int]										=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.')

							__snake_case				:		List[str]										=parser.parse_args()

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


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__a  :     List[Any]       =  logging.getLogger(__name__)





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							argparse.ArgumentParser(
				    description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''							)
				parser.add_argument(
				    '''--dataset_name'''							,			type=lowercase							,			default='''wikitext'''							,			help='''Name of the training. Explore datasets at: hf.co/datasets.'''							,			)
				parser.add_argument(
				    '''--dataset_config'''							,			type=lowercase							,			default='''wikitext-103-raw-v1'''							,			help='''Configuration name of the dataset.'''							)
				parser.add_argument(
				    '''--tokenizer_name_or_path'''							,			type=lowercase							,			default='''sayakpaul/unigram-tokenizer-wikitext'''							,			help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''							,			)
				parser.add_argument(
				    '''--shard_size'''							,			type=lowercase							,			default=1000							,			help='''Number of entries to go in a single shard.'''							,			)
				parser.add_argument('''--split'''							,			type=lowercase							,			default='''train'''							,			choices=['''train''', '''test''', '''validation''']							)
				parser.add_argument(
				    '''--limit'''							,			default=lowercase							,			type=lowercase							,			help='''Limit the number of shards (used for debugging).'''							,			)
				parser.add_argument(
				    '''--max_length'''							,			type=lowercase							,			default=512							,			help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
				    ''' sequence length that is a multiple of 8.'''							,			)
				parser.add_argument(
				    '''--output_dir'''							,			default='''tf-tpu'''							,			type=lowercase							,			help='''Output directory where the TFRecord shards will be saved. If the'''
				    ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
				    ''' shards will be directly saved to a Google Cloud Storage bucket.'''							,			)

				__lowercase 			=							parser.parse_args()
				return args





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				def fn(lowercase							):
								return tokenizer(examples['''text''']							)

				return fn





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				for i in range(len(tokenized_data['''input_ids''']							)							):
								__lowercase 			=							{
								    '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]							)							),
								    '''attention_mask''': tf.train.Feature(
								        intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]							)							),
								}
								__lowercase 			=							tf.train.Features(feature=lowercase							)
								__lowercase 			=							tf.train.Example(features=lowercase							)
								__lowercase 			=							example.SerializeToString()
								records.append(lowercase							)
				return records





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							datasets.load_dataset(args.dataset_name							,			args.dataset_config							,			split=args.split							)

				if args.limit is not None:
								__lowercase 			=							min(len(lowercase							)							,			args.limit							)
								__lowercase 			=							dataset.select(range(lowercase							)							)
								print(F"Limiting the dataset to {args.limit} entries."							)

				__lowercase 			=							AutoTokenizer.from_pretrained(args.tokenizer_name_or_path							)

				# Handle output directory creation.
				# For serializing into a Google Cloud Storage Bucket, one needs to first
				# create a bucket.
				if "gs" not in args.output_dir:
								if not os.path.exists(args.output_dir							):
												os.makedirs(args.output_dir							)
								__lowercase 			=							os.path.join(args.output_dir							,			args.split							)
								if not os.path.exists(lowercase							):
												os.makedirs(lowercase							)
				else:
								__lowercase 			=							os.path.join(args.output_dir							,			args.split							)

				# Tokenize the whole dataset at once.
				__lowercase 			=							tokenize_function(lowercase							)
				__lowercase 			=							dataset.map(lowercase							,			batched=lowercase							,			num_proc=4							,			remove_columns=['''text''']							)

				# We need to concatenate all our texts together, and then split the result
				# into chunks of a fixed size, which we will call block_size. To do this, we
				# will use the map method again, with the option batched=True. When we use batched=True,
				# the function we pass to map() will be passed multiple inputs at once, allowing us
				# to group them into more or fewer examples than we had in the input.
				# This allows us to create our new fixed-length samples. The advantage of this
				# method is that we don't lose a whole lot of content from the dataset compared to the
				# case where we simply tokenize with a pre-defined max_length.

				def group_texts(lowercase							):
								# Concatenate all texts.
								__lowercase 			=							{k: sum(examples[k]							,			[]							) for k in examples.keys()}
								__lowercase 			=							len(concatenated_examples[list(examples.keys()							)[0]]							)
								# We drop the small remainder, though you could add padding instead if the model supports it
								# In this, as in all things, we advise you to follow your heart 🫀
								__lowercase 			=							(total_length // args.max_length) * args.max_length
								# Split by chunks of max_len.
								__lowercase 			=							{
								    k: [t[i : i + args.max_length] for i in range(0							,			lowercase							,			args.max_length							)]
								    for k, t in concatenated_examples.items()
								}
								return result

				__lowercase 			=							dataset_tokenized.map(lowercase							,			batched=lowercase							,			batch_size=1000							,			num_proc=4							)

				__lowercase 			=							0
				__lowercase 			=							0
				for shard in range(0							,			len(lowercase							)							,			args.shard_size							):
								__lowercase 			=							grouped_dataset[shard : shard + args.shard_size]
								__lowercase 			=							len(dataset_snapshot['''input_ids''']							)
								__lowercase 			=							os.path.join(lowercase							,			F"dataset-{shard_count}-{records_containing}.tfrecord"							)
								__lowercase 			=							get_serialized_examples(lowercase							)

								with tf.io.TFRecordWriter(lowercase							) as out_file:
												for i in range(len(lowercase							)							):
																__lowercase 			=							serialized_examples[i]
																out_file.write(lowercase							)
												print('''Wrote file {} containing {} records'''.format(lowercase							,			lowercase							)							)

								shard_count += 1
								total_records += records_containing

				with open(F"split-{args.split}-records-count.txt"							,			'''w'''							) as f:
								print(F"Total {args.split} records: {total_records}"							,			file=lowercase							)


if __name__ == "__main__":
		__a  :     Optional[Any]       =  parse_args()
		main(args)