





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           MODEL_MAPPING,
           LevitForImageClassification,
           LevitForImageClassificationWithTeacher,
           LevitModel,
       )
       from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import LevitImageProcessor





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.config_class(**self.inputs_dict							)
            self.parent.assertTrue(hasattr(_UpperCAmelCase    ,						"hidden_sizes"							)							)
            self.parent.assertTrue(hasattr(_UpperCAmelCase    ,						"num_attention_heads"							)							)





class 			lowerCAmelCase__:






       '''simple docstring'''


       def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=1_3    ,						__lowerCamelCase=6_4    ,						__lowerCamelCase=3    ,						__lowerCamelCase=3    ,						__lowerCamelCase=2    ,						__lowerCamelCase=1    ,						__lowerCamelCase=1_6    ,						__lowerCamelCase=[1_2_8, 2_5_6, 3_8_4]    ,						__lowerCamelCase=[4, 6, 8]    ,						__lowerCamelCase=[2, 3, 4]    ,						__lowerCamelCase=[1_6, 1_6, 1_6]    ,						__lowerCamelCase=0    ,						__lowerCamelCase=[2, 2, 2]    ,						__lowerCamelCase=[2, 2, 2]    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=True    ,						__lowerCamelCase=True    ,						__lowerCamelCase=2    ,						)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	str						    =							parent
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							batch_size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							image_size
            _SCREAMING_SNAKE_CASE       :	str						    =							num_channels
            _SCREAMING_SNAKE_CASE       :	str						    =							kernel_size
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							stride
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							padding
            _SCREAMING_SNAKE_CASE       :	str						    =							hidden_sizes
            _SCREAMING_SNAKE_CASE       :	Dict						    =							num_attention_heads
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							depths
            _SCREAMING_SNAKE_CASE       :	int						    =							key_dim
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							drop_path_rate
            _SCREAMING_SNAKE_CASE       :	Any						    =							patch_size
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							attention_ratio
            _SCREAMING_SNAKE_CASE       :	str						    =							mlp_ratio
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							initializer_range
            _SCREAMING_SNAKE_CASE       :	Dict						    =							[
                ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
            ]
            _SCREAMING_SNAKE_CASE       :	Any						    =							is_training
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							use_labels
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							num_labels
            _SCREAMING_SNAKE_CASE       :	int						    =							initializer_range




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	str						    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

            _SCREAMING_SNAKE_CASE       :	int						    =							None
            if self.use_labels:
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							ids_tensor([self.batch_size]    ,						self.num_labels							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.get_config()
            return config, pixel_values, labels




       def     UpperCamelCase_			(      self							)			->     List[str]:
            return LevitConfig(
                image_size=self.image_size    ,						num_channels=self.num_channels    ,						kernel_size=self.kernel_size    ,						stride=self.stride    ,						padding=self.padding    ,						patch_size=self.patch_size    ,						hidden_sizes=self.hidden_sizes    ,						num_attention_heads=self.num_attention_heads    ,						depths=self.depths    ,						key_dim=self.key_dim    ,						drop_path_rate=self.drop_path_rate    ,						mlp_ratio=self.mlp_ratio    ,						attention_ratio=self.attention_ratio    ,						initializer_range=self.initializer_range    ,						down_ops=self.down_ops    ,						)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     int:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							LevitModel(config=_UpperCAmelCase							)
            model.to(_UpperCAmelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	str						    =							model(_UpperCAmelCase							)
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							(self.image_size, self.image_size)
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							image_size[0], image_size[1]
            for _ in range(4							):
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
                 _SCREAMING_SNAKE_CASE       :	int						    =							floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
            self.parent.assertEqual(
                result.last_hidden_state.shape    ,						(self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])    ,						)




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							)			->     Dict:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.num_labels
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							LevitForImageClassification(_UpperCAmelCase							)
            model.to(_UpperCAmelCase							)
            model.eval()
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model(_UpperCAmelCase    ,						labels=_UpperCAmelCase							)
            self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)							)







       def     UpperCamelCase_			(      self							)			->     Tuple:
            _SCREAMING_SNAKE_CASE       :	Any						    =							self.prepare_config_and_inputs()
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	int						    =							config_and_inputs
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							{"pixel_values": pixel_values}
            return config, inputs_dict





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






       '''simple docstring'''


       __snake_case 			=    (
           (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
           if is_torch_available()
           else ()
       )
       __snake_case 			=    (
           {
               'feature-extraction': LevitModel,
               'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
           }
           if is_torch_available()
           else {}
       )

       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False
       __snake_case 			=    False




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							LevitModelTester(self							)
            _SCREAMING_SNAKE_CASE       :	str						    =							ConfigTester(self    ,						config_class=_UpperCAmelCase    ,						has_text_modality=_UpperCAmelCase    ,						hidden_size=3_7							)




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            self.create_and_test_config_common_properties()
            self.config_tester.create_and_test_config_to_json_string()
            self.config_tester.create_and_test_config_to_json_file()
            self.config_tester.create_and_test_config_from_and_save_pretrained()
            self.config_tester.create_and_test_config_with_num_labels()
            self.config_tester.check_config_can_be_init_without_params()
            self.config_tester.check_config_arguments_init()




       def     UpperCamelCase_			(      self							)			->     Any:
            return




       @unittest.skip(reason="Levit does not use inputs_embeds"							)
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:
            pass




       @unittest.skip(reason="Levit does not support input and output embeddings"							)
       def     UpperCamelCase_			(      self							)			->     str:
            pass




       @unittest.skip(reason="Levit does not output attentions"							)
       def     UpperCamelCase_			(      self							)			->     Dict:
            pass




       def     UpperCamelCase_			(      self							)			->     Optional[int]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							model_class(_UpperCAmelCase							)
                 _SCREAMING_SNAKE_CASE       :	Any						    =							inspect.signature(model.forward							)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							[*signature.parameters.keys()]

                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							["pixel_values"]
                 self.assertListEqual(arg_names[:1]    ,						_UpperCAmelCase							)




       def     UpperCamelCase_			(      self							)			->     Optional[Any]:




            def check_hidden_states_output(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							model_class(_UpperCAmelCase							)
                 model.to(_UpperCAmelCase							)
                 model.eval()

                 with torch.no_grad():
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							model(**self._prepare_for_class(_UpperCAmelCase    ,						_UpperCAmelCase							)							)

                 _SCREAMING_SNAKE_CASE       :	Any						    =							outputs.hidden_states

                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							len(self.model_tester.depths							) + 1
                 self.assertEqual(len(_UpperCAmelCase							)    ,						_UpperCAmelCase							)

                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							(self.model_tester.image_size, self.model_tester.image_size)
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Dict						    =							image_size[0], image_size[1]
                 for _ in range(4							):
                      _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							floor(
                          (
                              (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                              / self.model_tester.stride
                          )
                          + 1							)
                      _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							floor(
                          (
                              (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                              / self.model_tester.stride
                          )
                          + 1							)
                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-2:]							)    ,						[
                         height * width,
                         self.model_tester.hidden_sizes[0],
                     ]    ,						)

            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							True
                 check_hidden_states_output(_UpperCAmelCase    ,						_UpperCAmelCase    ,						_UpperCAmelCase							)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _SCREAMING_SNAKE_CASE       :	List[str]						    =							True

                 check_hidden_states_output(_UpperCAmelCase    ,						_UpperCAmelCase    ,						_UpperCAmelCase							)




       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
       def     UpperCamelCase_			(      self							)			->     List[Any]:
            pass




       def     UpperCamelCase_			(      self    ,						__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase=False							)			->     Optional[Any]:
            _SCREAMING_SNAKE_CASE       :	Dict						    =							super()._prepare_for_class(_UpperCAmelCase    ,						_UpperCAmelCase    ,						return_labels=_UpperCAmelCase							)

            if return_labels:
                 if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                      del inputs_dict["labels"]

            return inputs_dict




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




       def     UpperCamelCase_			(      self							)			->     Any:
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase							)




       def     UpperCamelCase_			(      self							)			->     Any:




            if not self.model_tester.is_training:
                 return

            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[Any]						    =							self.model_tester.prepare_config_and_inputs_for_common()
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							True

            for model_class in self.all_model_classes:
                 # LevitForImageClassificationWithTeacher supports inference-only
                 if (
                     model_class in get_values(_UpperCAmelCase							)
                     or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                 ):
                      continue
                 _SCREAMING_SNAKE_CASE       :	Tuple						    =							model_class(_UpperCAmelCase							)
                 model.to(_UpperCAmelCase							)
                 model.train()
                 _SCREAMING_SNAKE_CASE       :	List[Any]						    =							self._prepare_for_class(_UpperCAmelCase    ,						_UpperCAmelCase    ,						return_labels=_UpperCAmelCase							)
                 _SCREAMING_SNAKE_CASE       :	int						    =							model(**_UpperCAmelCase							).loss
                 loss.backward()




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	str						    =							self.model_tester.prepare_config_and_inputs_for_common()
            if not self.model_tester.is_training:
                 return

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							False
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							True

            for model_class in self.all_model_classes:
                 if model_class in get_values(_UpperCAmelCase							) or not model_class.supports_gradient_checkpointing:
                      continue
                 # LevitForImageClassificationWithTeacher supports inference-only
                 if model_class.__name__ == "LevitForImageClassificationWithTeacher":
                      continue
                 _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							model_class(_UpperCAmelCase							)
                 model.gradient_checkpointing_enable()
                 model.to(_UpperCAmelCase							)
                 model.train()
                 _SCREAMING_SNAKE_CASE       :	Any						    =							self._prepare_for_class(_UpperCAmelCase    ,						_UpperCAmelCase    ,						return_labels=_UpperCAmelCase							)
                 _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model(**_UpperCAmelCase							).loss
                 loss.backward()




       def     UpperCamelCase_			(      self							)			->     Union[str, Any]:
            _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	Any						    =							self.model_tester.prepare_config_and_inputs_for_common()

            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							[
                {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
                {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
                {"title": "regression", "num_labels": 1, "dtype": torch.float},
            ]

            for model_class in self.all_model_classes:
                 if (
                     model_class
                     not in [
                         *get_values(_UpperCAmelCase							),
                     ]
                     or model_class.__name__ == "LevitForImageClassificationWithTeacher"
                 ):
                      continue

                 for problem_type in problem_types:
                      with self.subTest(msg=F"""Testing {model_class} with {problem_type['title']}"""							):
                           _SCREAMING_SNAKE_CASE       :	List[str]						    =							problem_type["title"]
                           _SCREAMING_SNAKE_CASE       :	int						    =							problem_type["num_labels"]

                           _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							model_class(_UpperCAmelCase							)
                           model.to(_UpperCAmelCase							)
                           model.train()

                           _SCREAMING_SNAKE_CASE       :	Any						    =							self._prepare_for_class(_UpperCAmelCase    ,						_UpperCAmelCase    ,						return_labels=_UpperCAmelCase							)

                           if problem_type["num_labels"] > 1:
                                _SCREAMING_SNAKE_CASE       :	Tuple						    =							inputs["labels"].unsqueeze(1							).repeat(1    ,						problem_type["num_labels"]							)

                           _SCREAMING_SNAKE_CASE       :	List[Any]						    =							inputs["labels"].to(problem_type["dtype"]							)

                           # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                           # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                           # they have the same size." which is a symptom something in wrong for the regression problem.
                           # See https://github.com/huggingface/transformers/issues/11780
                           with warnings.catch_warnings(record=_UpperCAmelCase							) as warning_list:
                                _SCREAMING_SNAKE_CASE       :	Dict						    =							model(**_UpperCAmelCase							).loss
                           for w in warning_list:
                                if "Using a target size that is different to the input size" in str(w.message							):
                                     raise ValueError(
                                         F"""Something is going wrong in the regression problem: intercepted {w.message}"""							)

                           loss.backward()







       @slow
       def     UpperCamelCase_			(      self							)			->     Optional[Any]:




            for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _SCREAMING_SNAKE_CASE       :	str						    =							LevitModel.from_pretrained(_UpperCAmelCase							)
                 self.assertIsNotNone(_UpperCAmelCase							)






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
     return image





@require_torch
@require_vision
class 			lowerCAmelCase__( unittest.TestCase    ):






       '''simple docstring'''


       @cached_property
       def     UpperCamelCase_			(      self							)			->     Any:
            return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)







       @slow
       def     UpperCamelCase_			(      self							)			->     int:
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
                _UpperCAmelCase							)

            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.default_image_processor
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							prepare_img()
            _SCREAMING_SNAKE_CASE       :	int						    =							image_processor(images=_UpperCAmelCase    ,						return_tensors="pt"							).to(_UpperCAmelCase							)

            # forward pass
            with torch.no_grad():
                 _SCREAMING_SNAKE_CASE       :	int						    =							model(**_UpperCAmelCase							)

            # verify the logits
            _SCREAMING_SNAKE_CASE       :	int						    =							torch.Size((1, 1_0_0_0)							)
            self.assertEqual(outputs.logits.shape    ,						_UpperCAmelCase							)

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							torch.tensor([1.0448, -0.3745, -1.8317]							).to(_UpperCAmelCase							)

            self.assertTrue(torch.allclose(outputs.logits[0, :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__      =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)