



import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  str=13						, __UpperCamelCase      :  Optional[int]=7						, __UpperCamelCase      :  Union[str, Any]=True						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  List[str]=99						, __UpperCamelCase      :  Optional[int]=32						, __UpperCamelCase      :  List[Any]=5						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  Any=37						, __UpperCamelCase      :  Tuple="gelu"						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  int=512						, __UpperCamelCase      :  Union[str, Any]=16						, __UpperCamelCase      :  Union[str, Any]=2						, __UpperCamelCase      :  List[Any]=0.0_2						, __UpperCamelCase      :  int=4						, )		->     Union[str, Any]:
											A      						=    parent
											A      						=    batch_size
											A      						=    seq_length
											A      						=    is_training
											A      						=    use_attention_mask
											A      						=    use_token_type_ids
											A      						=    use_labels
											A      						=    vocab_size
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    intermediate_size
											A      						=    hidden_act
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    max_position_embeddings
											A      						=    type_vocab_size
											A      						=    type_sequence_label_size
											A      						=    initializer_range
											A      						=    num_choices


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[int]:
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size       )

											A      						=    None
											if self.use_attention_mask:
																		A      						=    random_attention_mask([self.batch_size, self.seq_length]       )

											A      						=    None
											if self.use_token_type_ids:
																		A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size       )

											A      						=    RobertaPreLayerNormConfig(
											    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, type_vocab_size=self.type_vocab_size						, is_decoder=__UpperCamelCase						, initializer_range=self.initializer_range						, )

											return config, input_ids, token_type_ids, attention_mask


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[Any]:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A						,   A      						=    config_and_inputs
											A      						=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A						,   A      						=    config_and_inputs

											A      						=    True
											A      						=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, vocab_size=2       )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Tuple								=     True

				A_   :     Union[str, Any]								=     (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[str]:
											A      						=    FlaxRobertaPreLayerNormModelTester(self       )


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[Any]:
											for model_class_name in self.all_model_classes:
																		A      						=    model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )
																		A      						=    model(np.ones((1, 1)       )       )
																		self.assertIsNotNone(__UpperCamelCase       )





@require_flax
class        lowerCAmelCase__					(  unittest.TestCase       ):


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )

											A      						=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						, dtype=jnp.intaa       )
											A      						=    model(__UpperCamelCase       )[0]
											A      						=    [1, 11, 50_265]
											self.assertEqual(list(output.shape       )						, __UpperCamelCase       )
											# compare the actual values for a slice.
											A      						=    np.array(
											    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]						, dtype=np.floataa       )
											self.assertTrue(np.allclose(output[:, :3, :3]						, __UpperCamelCase						, atol=1e-4       )       )


				@slow
				def __UpperCamelCase						(							self      :  Dict       )		->     List[str]:
											A      						=    FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )

											A      						=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						, dtype=jnp.intaa       )
											A      						=    model(__UpperCamelCase       )[0]
											# compare the actual values for a slice.
											A      						=    np.array(
											    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]						, dtype=np.floataa       )
											self.assertTrue(np.allclose(output[:, :3, :3]						, __UpperCamelCase						, atol=1e-4       )       )







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

set_seed(770)


_UpperCamelCase			:	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',
}


_UpperCamelCase			:	Tuple								={
    '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',
    },
}

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



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



def  a__ (__lowercase							:str					,  __lowercase							:Optional[Any]   )		-> Dict:
				os.makedirs(__lowercase					,  exist_ok=__lowercase   )
				hf_hub_download(repo_id=__lowercase					,  filename=__lowercase					,  local_dir=__lowercase   )



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

				# convert Bark model arguments to HF Bark model arguments
				_A	:				List[str]   =      model_args.pop('''n_head'''   )
				_A	:				int   =      model_args.pop('''n_embd'''   )
				_A	:				Tuple   =      model_args.pop('''n_layer'''   )

				_A	:				Dict   =      ConfigClass(**checkpoint['''model_args''']   )
				_A	:				Optional[Any]   =      ModelClass(config=__lowercase   )
				_A	:				Optional[Any]   =      GenerationConfigClass()

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

												_A	:				str   =      state_dict.pop(__lowercase   )

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

				return model



def  a__ (__lowercase							:Union[str, Any]					,  __lowercase							:Any=False					,  __lowercase							:Optional[int]="text"   )		-> Dict:
				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

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

				_A	:				Tuple   =      _get_ckpt_path(__lowercase					,  use_small=__lowercase   )
				_A	:				Any   =      _load_model(__lowercase					,  __lowercase					,  model_type=__lowercase					,  use_small=__lowercase   )

				# load bark initial model
				_A	:				Union[str, Any]   =      _bark_load_model(__lowercase					,  '''cpu'''					,  model_type=__lowercase					,  use_small=__lowercase   )

				if model_type == "text":
								_A	:				Any   =      bark_model['''model''']

				if model.num_parameters(exclude_embeddings=__lowercase   ) != 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	:				List[str]   =      5
				_A	:				List[str]   =      10

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

								_A	:				Optional[Any]   =      model(__lowercase   )

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

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

								_A	:				List[str]   =      model(__lowercase					,  __lowercase   )
								_A	:				Dict   =      bark_model(__lowercase					,  __lowercase   )

								_A	:				Optional[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(__lowercase   ).mkdir(exist_ok=__lowercase   )
				model.save_pretrained(__lowercase   )



def  a__ (__lowercase							:Dict					,  __lowercase							:List[str]					,  __lowercase							:Union[str, Any]					,  __lowercase							:Dict					,  __lowercase							:List[Any]					,  __lowercase							:Optional[int]					,  )		-> Union[str, Any]:
				_A	:				List[Any]   =      os.path.join(__lowercase					,  __lowercase   )

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

				_A	:				str   =      BarkSemanticModel.from_pretrained(__lowercase   )
				_A	:				Tuple   =      BarkCoarseModel.from_pretrained(__lowercase   )
				_A	:				List[str]   =      BarkFineModel.from_pretrained(__lowercase   )
				_A	:				Union[str, Any]   =      EncodecModel.from_pretrained('''facebook/encodec_24khz'''   )

				_A	:				Tuple   =      BarkConfig.from_sub_model_configs(
				    __lowercase					,  __lowercase					,  __lowercase					,  __lowercase   )

				_A	:				Tuple   =      BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config					,  coarseAcoustic.generation_config					,  fineAcoustic.generation_config   )

				_A	:				Tuple   =      BarkModel(__lowercase   )

				_A	:				Optional[Any]   =      semantic
				_A	:				List[Any]   =      coarseAcoustic
				_A	:				int   =      fineAcoustic
				_A	:				List[Any]   =      codec

				_A	:				Optional[Any]   =      bark_generation_config

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


if __name__ == "__main__":
	_UpperCamelCase			:	str								=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.')

	_UpperCamelCase			:	Dict								=parser.parse_args()

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



