





import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=1_3    ,						__lowerCamelCase=7    ,						__lowerCamelCase=True    ,						__lowerCamelCase=True    ,						__lowerCamelCase=False    ,						__lowerCamelCase=True    ,						__lowerCamelCase=9_9    ,						__lowerCamelCase=3_2    ,						__lowerCamelCase=5    ,						__lowerCamelCase=4    ,						__lowerCamelCase=3_7    ,						__lowerCamelCase="gelu"    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=5_1_2    ,						__lowerCamelCase=1_6    ,						__lowerCamelCase=2    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=3    ,						__lowerCamelCase=4    ,						__lowerCamelCase=None    ,						)			->     List[Any]:
            _SCREAMING_SNAKE_CASE       :	int						    =							parent
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							seq_length
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							is_training
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							use_input_mask
            _SCREAMING_SNAKE_CASE       :	int						    =							use_token_type_ids
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							use_labels
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							vocab_size
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							hidden_size
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							num_hidden_layers
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							num_attention_heads
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							intermediate_size
            _SCREAMING_SNAKE_CASE       :	int						    =							hidden_act
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							hidden_dropout_prob
            _SCREAMING_SNAKE_CASE       :	str						    =							attention_probs_dropout_prob
            _SCREAMING_SNAKE_CASE       :	str						    =							max_position_embeddings
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							type_vocab_size
            _SCREAMING_SNAKE_CASE       :	Any						    =							type_sequence_label_size
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							initializer_range
            _SCREAMING_SNAKE_CASE       :	int						    =							num_labels
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							num_choices
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							scope




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							None
            if self.use_input_mask:
                 _SCREAMING_SNAKE_CASE       :	Any						    =							random_attention_mask([self.batch_size, self.seq_length]							)

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

            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							None
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							None
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							None
            if self.use_labels:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							ids_tensor([self.batch_size]    ,						self.type_sequence_label_size							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor([self.batch_size, self.seq_length]    ,						self.num_labels							)
                 _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor([self.batch_size]    ,						self.num_choices							)

            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            return OpenLlamaConfig(
                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=__lowerCamelCase    ,						initializer_range=self.initializer_range    ,						use_stable_embedding=__lowerCamelCase    ,						)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							OpenLlamaModel(config=__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model(__lowerCamelCase    ,						attention_mask=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							model(__lowerCamelCase							)
            self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							True
            _SCREAMING_SNAKE_CASE       :	int						    =							OpenLlamaModel(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(
                __lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						encoder_hidden_states=__lowerCamelCase    ,						encoder_attention_mask=__lowerCamelCase    ,						)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model(
                __lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						encoder_hidden_states=__lowerCamelCase    ,						)
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							model(__lowerCamelCase    ,						attention_mask=__lowerCamelCase							)
            self.parent.assertEqual(result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length, self.hidden_size)							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Any						    =							OpenLlamaForCausalLM(config=__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model(__lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						labels=__lowerCamelCase							)
            self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.seq_length, self.vocab_size)							)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						)			->     Any:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							True
            _SCREAMING_SNAKE_CASE       :	str						    =							True
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							OpenLlamaForCausalLM(config=__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()

            # first forward pass
            _SCREAMING_SNAKE_CASE       :	Dict						    =							model(
                __lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						encoder_hidden_states=__lowerCamelCase    ,						encoder_attention_mask=__lowerCamelCase    ,						use_cache=__lowerCamelCase    ,						)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor((self.batch_size, 3)    ,						config.vocab_size							)
            _SCREAMING_SNAKE_CASE       :	Any						    =							ids_tensor((self.batch_size, 3)    ,						vocab_size=2							)

            # append to next input_ids and
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							torch.cat([input_ids, next_tokens]    ,						dim=-1							)
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							torch.cat([input_mask, next_mask]    ,						dim=-1							)

            _SCREAMING_SNAKE_CASE       :	Dict						    =							model(
                __lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						encoder_hidden_states=__lowerCamelCase    ,						encoder_attention_mask=__lowerCamelCase    ,						output_hidden_states=__lowerCamelCase    ,						)["hidden_states"][0]
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model(
                __lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						encoder_hidden_states=__lowerCamelCase    ,						encoder_attention_mask=__lowerCamelCase    ,						past_key_values=__lowerCamelCase    ,						output_hidden_states=__lowerCamelCase    ,						)["hidden_states"][0]

            # select random slice
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							ids_tensor((1,)    ,						output_from_past.shape[-1]							).item()
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							output_from_no_past[:, -3:, random_slice_idx].detach()
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-3							)							)







       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	str						    =							self.prepare_config_and_inputs()
            (
                (
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			(
                _SCREAMING_SNAKE_CASE
            )    ,			
            )       :	Optional[int]						    =							config_and_inputs
            _SCREAMING_SNAKE_CASE       :	Any						    =							{"input_ids": input_ids, "attention_mask": input_mask}
            return config, inputs_dict





@require_torch
class 			lowerCAmelCase__( __lowercase  ,     __lowercase  ,     __lowercase  ,     unittest.TestCase    ):






       '''simple docstring'''


       __snake_case 			=    (
           (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
       )
       __snake_case 			=    (OpenLlamaForCausalLM,) if is_torch_available() else ()
       __snake_case 			=    (
           {
               'feature-extraction': OpenLlamaModel,
               'text-classification': OpenLlamaForSequenceClassification,
               'text-generation': OpenLlamaForCausalLM,
               'zero-shot': OpenLlamaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __snake_case 			=    False
       __snake_case 			=    False




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							OpenLlamaModelTester(self							)
            _SCREAMING_SNAKE_CASE       :	str						    =							ConfigTester(self    ,						config_class=__lowerCamelCase    ,						hidden_size=3_7							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            self.config_tester.run_common_tests()




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 _SCREAMING_SNAKE_CASE       :	Dict						    =							type
                 self.model_tester.create_and_check_model(*__lowerCamelCase							)




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Any						    =							self.model_tester.prepare_config_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							3
            _SCREAMING_SNAKE_CASE       :	int						    =							input_dict["input_ids"]
            _SCREAMING_SNAKE_CASE       :	int						    =							input_ids.ne(1							).to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size							)
            _SCREAMING_SNAKE_CASE       :	int						    =							OpenLlamaForSequenceClassification(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	int						    =							model(__lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						labels=__lowerCamelCase							)
            self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.model_tester.prepare_config_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							3
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							"single_label_classification"
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							input_dict["input_ids"]
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							input_ids.ne(1							).to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							ids_tensor([self.model_tester.batch_size]    ,						self.model_tester.type_sequence_label_size							)
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							OpenLlamaForSequenceClassification(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model(__lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						labels=__lowerCamelCase							)
            self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)							)




       def     UpperCamelCase_			(      self							)			->     List[str]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.model_tester.prepare_config_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							3
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							"multi_label_classification"
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							input_dict["input_ids"]
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							input_ids.ne(1							).to(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE       :	str						    =							ids_tensor(
                [self.model_tester.batch_size, config.num_labels]    ,						self.model_tester.type_sequence_label_size							).to(torch.float							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							OpenLlamaForSequenceClassification(__lowerCamelCase							)
            model.to(__lowerCamelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	Any						    =							model(__lowerCamelCase    ,						attention_mask=__lowerCamelCase    ,						labels=__lowerCamelCase							)
            self.assertEqual(result.logits.shape    ,						(self.model_tester.batch_size, self.model_tester.num_labels)							)




       @unittest.skip("Open-Llama buffers include complex numbers, which breaks this test"							)
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            pass







       @parameterized.expand([("linear",), ("dynamic",)]							)
       def     UpperCamelCase_			(      self    ,						__lowerCamelCase							)			->     str:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							self.model_tester.prepare_config_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	Dict						    =							ids_tensor([1, 1_0]    ,						config.vocab_size							)
            _SCREAMING_SNAKE_CASE       :	int						    =							ids_tensor([1, int(config.max_position_embeddings * 1.5							)]    ,						config.vocab_size							)

            set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							OpenLlamaModel(__lowerCamelCase							)
            original_model.to(__lowerCamelCase							)
            original_model.eval()
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							original_model(__lowerCamelCase							).last_hidden_state
            _SCREAMING_SNAKE_CASE       :	int						    =							original_model(__lowerCamelCase							).last_hidden_state

            set_seed(4_2							)  # Fixed seed at init time so the two models get the same random weights
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							{"type": scaling_type, "factor": 10.0}
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							OpenLlamaModel(__lowerCamelCase							)
            scaled_model.to(__lowerCamelCase							)
            scaled_model.eval()
            _SCREAMING_SNAKE_CASE       :	int						    =							scaled_model(__lowerCamelCase							).last_hidden_state
            _SCREAMING_SNAKE_CASE       :	str						    =							scaled_model(__lowerCamelCase							).last_hidden_state

            # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
            # maximum sequence length, so the outputs for the short input should match.
            if scaling_type == "dynamic":
                 self.assertTrue(torch.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-5							)							)
            else:
                 self.assertFalse(torch.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-5							)							)

            # The output should be different for long inputs
            self.assertFalse(torch.allclose(__lowerCamelCase    ,						__lowerCamelCase    ,						atol=1E-5							)							)






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__      =logging.get_logger(__name__)

set_seed(770)


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






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase=False	):
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model_type
     if use_small:
          key += "_small"
     return os.path.join(__lowerCamelCase,       REMOTE_MODEL_PATHS[key]["file_name"]	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase	):
     os.makedirs(__lowerCamelCase,       exist_ok=__lowerCamelCase	)
     hf_hub_download(repo_id=__lowerCamelCase,       filename=__lowerCamelCase,       local_dir=__lowerCamelCase	)






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

     # convert Bark model arguments to HF Bark model arguments
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model_args.pop("n_head"	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							model_args.pop("n_embd"	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							model_args.pop("n_layer"	)

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							ConfigClass(**checkpoint["model_args"]	)
     _SCREAMING_SNAKE_CASE       :	int						    =							ModelClass(config=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							GenerationConfigClass()

     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model_generation_config
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							checkpoint["model"]
     # fixup checkpoint
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							"_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
               _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							k[len(__lowerCamelCase	) :]
               for old_layer_name in new_layer_name_dict:
                    _SCREAMING_SNAKE_CASE       :	Tuple						    =							new_k.replace(__lowerCamelCase,       new_layer_name_dict[old_layer_name]	)

               _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							state_dict.pop(__lowerCamelCase	)

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

     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							"cpu"  # do conversion on cpu

     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							_get_ckpt_path(__lowerCamelCase,       use_small=__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							_load_model(__lowerCamelCase,       __lowerCamelCase,       model_type=__lowerCamelCase,       use_small=__lowerCamelCase	)

     # load bark initial model
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							_bark_load_model(__lowerCamelCase,       "cpu",       model_type=__lowerCamelCase,       use_small=__lowerCamelCase	)

     if model_type == "text":
          _SCREAMING_SNAKE_CASE       :	str						    =							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
     _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							5
     _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							10

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

          _SCREAMING_SNAKE_CASE       :	Any						    =							model(__lowerCamelCase	)

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

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

          _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model(__lowerCamelCase,       __lowerCamelCase	)
          _SCREAMING_SNAKE_CASE       :	Dict						    =							bark_model(__lowerCamelCase,       __lowerCamelCase	)

          _SCREAMING_SNAKE_CASE       :	List[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(__lowerCamelCase	).mkdir(exist_ok=__lowerCamelCase	)
     model.save_pretrained(__lowerCamelCase	)






def     lowerCamelCase__			(__lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       ):
     _SCREAMING_SNAKE_CASE       :	Dict						    =							os.path.join(__lowerCamelCase,       __lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	int						    =							BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase,       "config.json"	)	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase,       "config.json"	)	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase,       "config.json"	)	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							EncodecConfig.from_pretrained("facebook/encodec_24khz"	)

     _SCREAMING_SNAKE_CASE       :	int						    =							BarkSemanticModel.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							BarkCoarseModel.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							BarkFineModel.from_pretrained(__lowerCamelCase	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							EncodecModel.from_pretrained("facebook/encodec_24khz"	)

     _SCREAMING_SNAKE_CASE       :	Any						    =							BarkConfig.from_sub_model_configs(
         __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase,       __lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							BarkGenerationConfig.from_sub_model_configs(
         semantic.generation_config,       coarseAcoustic.generation_config,       fineAcoustic.generation_config	)

     _SCREAMING_SNAKE_CASE       :	str						    =							BarkModel(__lowerCamelCase	)

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							semantic
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							coarseAcoustic
     _SCREAMING_SNAKE_CASE       :	List[str]						    =							fineAcoustic
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							codec

     _SCREAMING_SNAKE_CASE       :	Tuple						    =							bark_generation_config

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


if __name__ == "__main__":
       UpperCamelCase__      =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__      =parser.parse_args()

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