






"""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(770)


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 							snake_case (  A__      ,A__=False		):
			UpperCAmelCase_	:					Optional[Any]     	=   model_type
			if use_small:
						key += "_small"
			return os.path.join(A__      ,REMOTE_MODEL_PATHS[key]["file_name"]		)

def 							snake_case (  A__      ,A__		):
			os.makedirs(A__      ,exist_ok=A__		)
			hf_hub_download(repo_id=A__      ,filename=A__      ,local_dir=A__		)

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

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

			UpperCAmelCase_	:					Tuple     	=   ConfigClass(**checkpoint["model_args"]		)
			UpperCAmelCase_	:					Optional[Any]     	=   ModelClass(config=A__		)
			UpperCAmelCase_	:					List[Any]     	=   GenerationConfigClass()

			UpperCAmelCase_	:					Optional[Any]     	=   model_generation_config
			UpperCAmelCase_	:					int     	=   checkpoint["model"]
			# fixup checkpoint
			UpperCAmelCase_	:					List[str]     	=   "_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
									UpperCAmelCase_	:					Union[str, Any]     	=   k[len(A__		) :]
									for old_layer_name in new_layer_name_dict:
												UpperCAmelCase_	:					Dict     	=   new_k.replace(A__      ,new_layer_name_dict[old_layer_name]		)

									UpperCAmelCase_	:					str     	=   state_dict.pop(A__		)

			UpperCAmelCase_	:					List[str]     	=   set(state_dict.keys()		) - set(model.state_dict().keys()		)
			UpperCAmelCase_	:					List[str]     	=   {k for k in extra_keys if not k.endswith(".attn.bias"		)}
			UpperCAmelCase_	:					List[Any]     	=   set(model.state_dict().keys()		) - set(state_dict.keys()		)
			UpperCAmelCase_	:					Optional[Any]     	=   {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__		)
			UpperCAmelCase_	:					Optional[int]     	=   model.num_parameters(exclude_embeddings=A__		)
			UpperCAmelCase_	:					Optional[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 							snake_case (  A__      ,A__=False      ,A__="text"		):
			if model_type not in ("text", "coarse", "fine"):
						raise NotImplementedError()

			UpperCAmelCase_	:					Dict     	=   "cpu"  # do conversion on cpu

			UpperCAmelCase_	:					Union[str, Any]     	=   _get_ckpt_path(A__      ,use_small=A__		)
			UpperCAmelCase_	:					int     	=   _load_model(A__      ,A__      ,model_type=A__      ,use_small=A__		)

			# load bark initial model
			UpperCAmelCase_	:					Optional[Any]     	=   _bark_load_model(A__      ,"cpu"      ,model_type=A__      ,use_small=A__		)

			if model_type == "text":
						UpperCAmelCase_	:					Any     	=   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
			UpperCAmelCase_	:					Union[str, Any]     	=   5
			UpperCAmelCase_	:					str     	=   10

			if model_type in ["text", "coarse"]:
						UpperCAmelCase_	:					Optional[Any]     	=   torch.randint(2_56      ,(batch_size, sequence_length)      ,dtype=torch.int		)
						UpperCAmelCase_	:					int     	=   bark_model(A__		)[0]

						UpperCAmelCase_	:					List[str]     	=   model(A__		)

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

			else:
						UpperCAmelCase_	:					int     	=   3
						UpperCAmelCase_	:					Tuple     	=   8
						UpperCAmelCase_	:					str     	=   torch.randint(2_56      ,(batch_size, sequence_length, n_codes_total)      ,dtype=torch.int		)

						UpperCAmelCase_	:					Any     	=   model(A__      ,A__		)
						UpperCAmelCase_	:					Any     	=   bark_model(A__      ,A__		)

						UpperCAmelCase_	:					Union[str, 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 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__      ,):
			UpperCAmelCase_	:					Union[str, Any]     	=   os.path.join(A__      ,A__		)

			UpperCAmelCase_	:					int     	=   BarkSemanticConfig.from_pretrained(os.path.join(A__      ,"config.json"		)		)
			UpperCAmelCase_	:					List[str]     	=   BarkCoarseConfig.from_pretrained(os.path.join(A__      ,"config.json"		)		)
			UpperCAmelCase_	:					Optional[Any]     	=   BarkFineConfig.from_pretrained(os.path.join(A__      ,"config.json"		)		)
			UpperCAmelCase_	:					Optional[int]     	=   EncodecConfig.from_pretrained("facebook/encodec_24khz"		)

			UpperCAmelCase_	:					List[Any]     	=   BarkSemanticModel.from_pretrained(A__		)
			UpperCAmelCase_	:					List[Any]     	=   BarkCoarseModel.from_pretrained(A__		)
			UpperCAmelCase_	:					List[Any]     	=   BarkFineModel.from_pretrained(A__		)
			UpperCAmelCase_	:					Optional[int]     	=   EncodecModel.from_pretrained("facebook/encodec_24khz"		)

			UpperCAmelCase_	:					List[str]     	=   BarkConfig.from_sub_model_configs(
			    A__      ,A__      ,A__      ,A__		)

			UpperCAmelCase_	:					int     	=   BarkGenerationConfig.from_sub_model_configs(
			    semantic.generation_config      ,coarseAcoustic.generation_config      ,fineAcoustic.generation_config		)

			UpperCAmelCase_	:					str     	=   BarkModel(A__		)

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

			UpperCAmelCase_	:					Union[str, Any]     	=   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)











"""simple docstring"""
from typing import List, Optional, Union

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


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, bool]] = "mean"      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					str = "gelu"      ,      lowerCAmelCase_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

				# Transformer architecture configuration
				UpperCAmelCase_	:					Optional[Any]     	=   input_size * len(self.lags_sequence		) + self._number_of_features
				UpperCAmelCase_	:					List[Any]     	=   d_model
				UpperCAmelCase_	:					List[str]     	=   encoder_attention_heads
				UpperCAmelCase_	:					List[str]     	=   decoder_attention_heads
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_ffn_dim
				UpperCAmelCase_	:					Union[str, Any]     	=   decoder_ffn_dim
				UpperCAmelCase_	:					Tuple     	=   encoder_layers
				UpperCAmelCase_	:					List[Any]     	=   decoder_layers

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

				super().__init__(is_encoder_decoder=lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				return (
				    sum(self.embedding_dimension		)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)



