





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


SCREAMING_SNAKE_CASE_   :      List[str]			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      Optional[int]			=   {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''switch_transformers'''
	__UpperCamelCase   =		['''past_key_values''']
	__UpperCamelCase   =		{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




	def __init__(						self							:     Dict					,      __lowerCamelCase							:     Optional[int]=32_128					,      __lowerCamelCase							:     List[str]=768					,      __lowerCamelCase							:     Optional[Any]=64					,      __lowerCamelCase							:     Optional[Any]=2_048					,      __lowerCamelCase							:     Any=64					,      __lowerCamelCase							:     Tuple=12					,      __lowerCamelCase							:     Optional[Any]=3					,      __lowerCamelCase							:     Dict=12					,      __lowerCamelCase							:     Tuple=3					,      __lowerCamelCase							:     Optional[Any]=12					,      __lowerCamelCase							:     int=8					,      __lowerCamelCase							:     int=False					,      __lowerCamelCase							:     Dict=0.01					,      __lowerCamelCase							:     Any="float32"					,      __lowerCamelCase							:     List[str]=False					,      __lowerCamelCase							:     Optional[int]=32					,      __lowerCamelCase							:     List[Any]=128					,      __lowerCamelCase							:     Tuple=0.1					,      __lowerCamelCase							:     List[Any]=1E-6					,      __lowerCamelCase							:     Optional[Any]=0.001					,      __lowerCamelCase							:     str=0.001					,      __lowerCamelCase							:     Optional[Any]=1.0					,      __lowerCamelCase							:     int="relu"					,      __lowerCamelCase							:     Tuple=True					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Union[str, Any]=True					,      __lowerCamelCase							:     Any=0					,      __lowerCamelCase							:     Dict=1					,      **__lowerCamelCase							:     Optional[int]					,      )       -> int:

			'''simple docstring'''




			__lowercase											=     vocab_size
			__lowercase											=     d_model
			__lowercase											=     d_kv
			__lowercase											=     d_ff

			__lowercase											=     num_sparse_encoder_layers

			__lowercase											=     num_layers
			__lowercase											=     (
			    num_decoder_layers if num_decoder_layers is not None else self.num_layers
			)  # default = symmetry
			__lowercase											=     num_sparse_decoder_layers

			# This tells us, each how many encoder layer we'll have to set a sparse layer.
			if self.num_sparse_encoder_layers > 0:
					__lowercase											=     self.num_layers // self.num_sparse_encoder_layers
			else:
					__lowercase											=     self.num_layers  # HACK: this will create 0 sparse layers

			# This tells us, each how many encoder layer we'll have to set a sparse layer.
			if self.num_sparse_decoder_layers > 0:
					__lowercase											=     self.num_decoder_layers // self.num_sparse_decoder_layers
			else:
					__lowercase											=     self.num_decoder_layers  # HACK: this will create 0 sparse layers

			__lowercase											=     num_heads
			__lowercase											=     num_experts
			__lowercase											=     expert_capacity
			__lowercase											=     router_bias
			__lowercase											=     router_jitter_noise
			if router_dtype not in ["float32", "float16", "bfloat16"]:
					raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"   )
			__lowercase											=     router_dtype

			__lowercase											=     router_ignore_padding_tokens
			__lowercase											=     relative_attention_num_buckets
			__lowercase											=     relative_attention_max_distance

			__lowercase											=     dropout_rate
			__lowercase											=     layer_norm_epsilon
			__lowercase											=     initializer_factor
			__lowercase											=     feed_forward_proj
			__lowercase											=     use_cache
			__lowercase											=     add_router_probs

			__lowercase											=     router_z_loss_coef
			__lowercase											=     router_aux_loss_coef

			__lowercase											=     self.feed_forward_proj.split('-'   )
			__lowercase											=     act_info[-1]
			__lowercase											=     act_info[0] == 'gated'

			if len(__lowerCamelCase   ) > 1 and act_info[0] != "gated" or len(__lowerCamelCase   ) > 2:
					raise ValueError(
					    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
					    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
					    '\'gated-gelu\' or \'relu\''   )

			# for backwards compatibility
			if feed_forward_proj == "gated-gelu":
					__lowercase											=     'gelu_new'

			super().__init__(
			    pad_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      is_encoder_decoder=__lowerCamelCase					,      **__lowerCamelCase					,      )











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()
SCREAMING_SNAKE_CASE_   :      Dict			=   logging.get_logger(__name__)

set_seed(770)


SCREAMING_SNAKE_CASE_   :      List[Any]			=   {
    '''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''',
}


SCREAMING_SNAKE_CASE_   :      Optional[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''',
    },
}

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



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False							)		->    Union[str, Any]:
		__lowercase											=     model_type
		if use_small:
				key += "_small"
		return os.path.join(snake_case						,   REMOTE_MODEL_PATHS[key]['file_name']							)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    List[str]:
		os.makedirs(snake_case						,   exist_ok=snake_case							)
		hf_hub_download(repo_id=snake_case						,   filename=snake_case						,   local_dir=snake_case							)



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

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

		__lowercase											=     ConfigClass(**checkpoint['model_args']							)
		__lowercase											=     ModelClass(config=snake_case							)
		__lowercase											=     GenerationConfigClass()

		__lowercase											=     model_generation_config
		__lowercase											=     checkpoint['model']
		# fixup checkpoint
		__lowercase											=     '_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
						__lowercase											=     k[len(snake_case							) :]
						for old_layer_name in new_layer_name_dict:
								__lowercase											=     new_k.replace(snake_case						,   new_layer_name_dict[old_layer_name]							)

						__lowercase											=     state_dict.pop(snake_case							)

		__lowercase											=     set(state_dict.keys()							) - set(model.state_dict().keys()							)
		__lowercase											=     {k for k in extra_keys if not k.endswith('.attn.bias'							)}
		__lowercase											=     set(model.state_dict().keys()							) - set(state_dict.keys()							)
		__lowercase											=     {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							)
		__lowercase											=     model.num_parameters(exclude_embeddings=snake_case							)
		__lowercase											=     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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case=False						,   snake_case="text"							)		->    Tuple:
		if model_type not in ("text", "coarse", "fine"):
				raise NotImplementedError()

		__lowercase											=     'cpu'  # do conversion on cpu

		__lowercase											=     _get_ckpt_path(snake_case						,   use_small=snake_case							)
		__lowercase											=     _load_model(snake_case						,   snake_case						,   model_type=snake_case						,   use_small=snake_case							)

		# load bark initial model
		__lowercase											=     _bark_load_model(snake_case						,   'cpu'						,   model_type=snake_case						,   use_small=snake_case							)

		if model_type == "text":
				__lowercase											=     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
		__lowercase											=     5
		__lowercase											=     10

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

				__lowercase											=     model(snake_case							)

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

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

				__lowercase											=     model(snake_case						,   snake_case							)
				__lowercase											=     bark_model(snake_case						,   snake_case							)

				__lowercase											=     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  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   snake_case						,   )		->    List[str]:
		__lowercase											=     os.path.join(snake_case						,   snake_case							)

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

		__lowercase											=     BarkSemanticModel.from_pretrained(snake_case							)
		__lowercase											=     BarkCoarseModel.from_pretrained(snake_case							)
		__lowercase											=     BarkFineModel.from_pretrained(snake_case							)
		__lowercase											=     EncodecModel.from_pretrained('facebook/encodec_24khz'							)

		__lowercase											=     BarkConfig.from_sub_model_configs(
		    snake_case						,   snake_case						,   snake_case						,   snake_case							)

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

		__lowercase											=     BarkModel(snake_case							)

		__lowercase											=     semantic
		__lowercase											=     coarseAcoustic
		__lowercase											=     fineAcoustic
		__lowercase											=     codec

		__lowercase											=     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__":
		SCREAMING_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.''')

		SCREAMING_SNAKE_CASE_   :      Optional[int]			=   parser.parse_args()

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




