

'''simple docstring'''

import copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase							=					logging.get_logger(__name__)


UpperCAmelCase							=					{
    '''suno/bark-small''': '''https://huggingface.co/suno/bark-small/resolve/main/config.json''',
    '''suno/bark''': '''https://huggingface.co/suno/bark/resolve/main/config.json''',
}

UpperCAmelCase							=					'''
    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model
    according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)
    architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        block_size (`int`, *optional*, defaults to 1024):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        input_vocab_size (`int`, *optional*, defaults to 10_048):
            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with
            regards to the chosen sub-model.
        output_vocab_size (`int`, *optional*, defaults to 10_048):
            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented
            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought
            with regards to the chosen sub-model.
        num_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the given sub-model.
        num_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer architecture.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        bias (`bool`, *optional*, defaults to `True`):
            Whether or not to use bias in the linear layers and layer norm layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
'''


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"bark_module"
       lowerCAmelCase_				=	["past_key_values"]

       lowerCAmelCase_				=	{
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
           "vocab_size": "input_vocab_size",
           "window_size": "block_size",
       }







       def __init__(							self     :	Dict  , __lowercase     :	Dict=1024  , __lowercase     :	Any=10048  , __lowercase     :	Tuple=10048  , __lowercase     :	Optional[Any]=12  , __lowercase     :	List[Any]=12  , __lowercase     :	Any=768  , __lowercase     :	Union[str, Any]=0.0  , __lowercase     :	Union[str, Any]=True  , __lowercase     :	Optional[int]=0.0_2  , __lowercase     :	Union[str, Any]=True  , **__lowercase     :	Optional[Any]  , ):







            """simple docstring"""







            __lowercase     						=block_size
            __lowercase     						=input_vocab_size
            __lowercase     						=output_vocab_size
            __lowercase     						=num_layers
            __lowercase     						=num_heads
            __lowercase     						=hidden_size
            __lowercase     						=dropout
            __lowercase     						=bias
            __lowercase     						=use_cache
            __lowercase     						=initializer_range

            super().__init__(**__lowercase )







       @classmethod
       def      snake_case				(							cls     :	str  , __lowercase     :	Union[str, os.PathLike]  , __lowercase     :	Optional[Union[str, os.PathLike]] = None  , __lowercase     :	bool = False  , __lowercase     :	bool = False  , __lowercase     :	Optional[Union[str, bool]] = None  , __lowercase     :	str = "main"  , **__lowercase     :	List[Any]  , ):







            """simple docstring"""







            __lowercase     						=cache_dir
            __lowercase     						=force_download
            __lowercase     						=local_files_only
            __lowercase     						=revision

            cls._set_token_in_kwargs(__lowercase  , __lowercase )

            __lowercase						,       __lowercase     						=cls.get_config_dict(__lowercase  , **__lowercase )

            # get the config dict if we are loading from Bark
            if config_dict.get('model_type' ) == "bark":
                 __lowercase     						=config_dict[f'''{cls.model_type}_config''']

            if "model_type" in config_dict and hasattr(cls  , 'model_type' ) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                     f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

            return cls.from_dict(__lowercase  , **__lowercase )






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"				,     model="BarkSemanticModel"				)				,     "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,     )
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"semantic"






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"				,     model="BarkCoarseModel"				)				,     "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,     )
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"coarse_acoustics"






@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"				,     model="BarkFineModel"				)				,     "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,     )
class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"fine_acoustics"







       def __init__(							self     :	Union[str, Any]  , __lowercase     :	Any=True  , __lowercase     :	Optional[Any]=8  , __lowercase     :	str=1  , **__lowercase     :	str ):







            """simple docstring"""







            __lowercase     						=n_codes_total
            __lowercase     						=n_codes_given

            super().__init__(tie_word_embeddings=__lowercase  , **__lowercase )






class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	"bark"
       lowerCAmelCase_				=	True







       def __init__(							self     :	str  , __lowercase     :	Dict = None  , __lowercase     :	Dict = None  , __lowercase     :	Dict = None  , __lowercase     :	Dict = None  , __lowercase     :	List[Any]=0.0_2  , **__lowercase     :	Tuple  , ):







            """simple docstring"""







            if semantic_config is None:
                 __lowercase     						={}
                 logger.info('semantic_config is None. initializing the semantic model with default values.' )

            if coarse_acoustics_config is None:
                 __lowercase     						={}
                 logger.info('coarse_acoustics_config is None. initializing the coarse model with default values.' )

            if fine_acoustics_config is None:
                 __lowercase     						={}
                 logger.info('fine_acoustics_config is None. initializing the fine model with default values.' )

            if codec_config is None:
                 __lowercase     						={}
                 logger.info('codec_config is None. initializing the codec model with default values.' )

            __lowercase     						=BarkSemanticConfig(**__lowercase )
            __lowercase     						=BarkCoarseConfig(**__lowercase )
            __lowercase     						=BarkFineConfig(**__lowercase )
            __lowercase     						=AutoConfig.for_model(**__lowercase )

            __lowercase     						=initializer_range

            super().__init__(**__lowercase )







       @classmethod
       def      snake_case				(							cls     :	List[str]  , __lowercase     :	BarkSemanticConfig  , __lowercase     :	BarkCoarseConfig  , __lowercase     :	BarkFineConfig  , __lowercase     :	AutoConfig  , **__lowercase     :	List[Any]  , ):







            """simple docstring"""







            return cls(
                semantic_config=semantic_config.to_dict()  , coarse_acoustics_config=coarse_acoustics_config.to_dict()  , fine_acoustics_config=fine_acoustics_config.to_dict()  , codec_config=codec_config.to_dict()  , **__lowercase  , )







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=copy.deepcopy(self.__dict__ )

            __lowercase     						=self.semantic_config.to_dict()
            __lowercase     						=self.coarse_acoustics_config.to_dict()
            __lowercase     						=self.fine_acoustics_config.to_dict()
            __lowercase     						=self.codec_config.to_dict()

            __lowercase     						=self.__class__.model_type
            return output





'''simple docstring'''

import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.modeling_flax_pytorch_utils import (
           convert_pytorch_state_dict_to_flax,
           load_flax_weights_in_pytorch_model,
       )
       from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
       import torch


class 					lowerCAmelCase       :







       def __init__(							self     :	Tuple  , __lowercase     :	Optional[int]  , __lowercase     :	Union[str, Any]=14  , __lowercase     :	Any=7  , __lowercase     :	List[str]=True  , __lowercase     :	Dict=True  , __lowercase     :	Tuple=False  , __lowercase     :	Tuple=True  , __lowercase     :	Optional[Any]=99  , __lowercase     :	List[Any]=32  , __lowercase     :	Any=4  , __lowercase     :	str=4  , __lowercase     :	Tuple=[[["global", "local"], 2]]  , __lowercase     :	List[str]=37  , __lowercase     :	Tuple="gelu"  , __lowercase     :	Optional[int]=0.1  , __lowercase     :	List[Any]=0.1  , __lowercase     :	int=512  , __lowercase     :	str=7  , __lowercase     :	int=0.0_2  , ):







            """simple docstring"""







            __lowercase     						=parent
            __lowercase     						=batch_size
            __lowercase     						=seq_length
            __lowercase     						=is_training
            __lowercase     						=use_input_mask
            __lowercase     						=use_token_type_ids
            __lowercase     						=use_labels
            __lowercase     						=vocab_size
            __lowercase     						=hidden_size
            __lowercase     						=num_hidden_layers
            __lowercase     						=num_attention_heads
            __lowercase     						=attention_types
            __lowercase     						=intermediate_size
            __lowercase     						=hidden_act
            __lowercase     						=hidden_dropout_prob
            __lowercase     						=attention_probs_dropout_prob
            __lowercase     						=max_position_embeddings
            __lowercase     						=window_size
            __lowercase     						=initializer_range
            __lowercase     						=None
            __lowercase     						=vocab_size - 1
            __lowercase     						=vocab_size - 1
            __lowercase     						=vocab_size - 1







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            __lowercase     						=ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size )

            __lowercase     						=None
            if self.use_input_mask:
                 __lowercase     						=random_attention_mask([self.batch_size, self.seq_length] )

            __lowercase     						=GPTNeoConfig(
                vocab_size=self.vocab_size  , hidden_size=self.hidden_size  , num_layers=self.num_hidden_layers  , num_heads=self.num_attention_heads  , max_position_embeddings=self.max_position_embeddings  , use_cache=__lowercase  , bos_token_id=self.bos_token_id  , eos_token_id=self.eos_token_id  , pad_token_id=self.pad_token_id  , window_size=self.window_size  , attention_types=self.attention_types  , )

            return (config, input_ids, input_mask)







       def      snake_case				(							self     :	Tuple ):







            """simple docstring"""







            __lowercase     						=self.prepare_config_and_inputs()
            __lowercase						,       __lowercase						,       __lowercase     						=config_and_inputs
            __lowercase     						={'input_ids': input_ids, 'attention_mask': attention_mask}
            return config, inputs_dict







       def      snake_case				(							self     :	Tuple  , __lowercase     :	str  , __lowercase     :	Union[str, Any]  , __lowercase     :	Dict  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=20
            __lowercase     						=model_class_name(__lowercase )

            __lowercase     						=model.init_cache(input_ids.shape[0]  , __lowercase )
            __lowercase     						=jnp.ones((input_ids.shape[0], max_decoder_length)  , dtype='i4' )

            __lowercase     						=jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1 )[None, :]  , (input_ids.shape[0], input_ids.shape[-1] - 1) )
            __lowercase     						=model(
                input_ids[:, :-1]  , attention_mask=__lowercase  , past_key_values=__lowercase  , position_ids=__lowercase  , )

            __lowercase     						=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  , dtype='i4' )
            __lowercase     						=model(
                input_ids[:, -1:]  , attention_mask=__lowercase  , past_key_values=outputs_cache.past_key_values  , position_ids=__lowercase  , )

            __lowercase     						=model(__lowercase )

            __lowercase     						=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
            self.parent.assertTrue(diff < 1E-3  , msg=f'''Max diff is {diff}''' )







       def      snake_case				(							self     :	Any  , __lowercase     :	List[str]  , __lowercase     :	Dict  , __lowercase     :	Dict  , __lowercase     :	Optional[Any] ):







            """simple docstring"""







            __lowercase     						=20
            __lowercase     						=model_class_name(__lowercase )

            __lowercase     						=jnp.concatenate(
                [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]  , axis=-1  , )

            __lowercase     						=model.init_cache(input_ids.shape[0]  , __lowercase )
            __lowercase     						=jnp.broadcast_to(
                jnp.arange(input_ids.shape[-1] - 1 )[None, :]  , (input_ids.shape[0], input_ids.shape[-1] - 1) )

            __lowercase     						=model(
                input_ids[:, :-1]  , attention_mask=__lowercase  , past_key_values=__lowercase  , position_ids=__lowercase  , )
            __lowercase     						=jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]  , dtype='i4' )
            __lowercase     						=model(
                input_ids[:, -1:]  , past_key_values=outputs_cache.past_key_values  , attention_mask=__lowercase  , position_ids=__lowercase  , )

            __lowercase     						=model(__lowercase  , attention_mask=__lowercase )

            __lowercase     						=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
            self.parent.assertTrue(diff < 1E-3  , msg=f'''Max diff is {diff}''' )






@require_flax
class 					lowerCAmelCase       ( A				,     A				,     unittest.TestCase				):
       lowerCAmelCase_				=	(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
       lowerCAmelCase_				=	(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=FlaxGPTNeoModelTester(self )







       def      snake_case				(							self     :	Union[str, Any] ):







            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowercase						,       __lowercase						,       __lowercase     						=self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward(__lowercase  , __lowercase  , __lowercase  , __lowercase )







       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowercase						,       __lowercase						,       __lowercase     						=self.model_tester.prepare_config_and_inputs()
                 self.model_tester.check_use_cache_forward_with_attn_mask(
                     __lowercase  , __lowercase  , __lowercase  , __lowercase )







       @slow
       def      snake_case				(							self     :	List[str] ):







            """simple docstring"""







            __lowercase     						=GPTaTokenizer.from_pretrained('gpt2'  , pad_token='<|endoftext|>'  , padding_side='left' )
            __lowercase     						=tokenizer(['Hello this is a long string', 'Hey']  , return_tensors='np'  , padding=__lowercase  , truncation=__lowercase )

            __lowercase     						=FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M' )
            __lowercase     						=False
            __lowercase     						=model.config.eos_token_id

            __lowercase     						=jax.jit(model.generate )

            __lowercase     						=jit_generate(
                inputs['input_ids']  , attention_mask=inputs['attention_mask']  , pad_token_id=tokenizer.pad_token_id ).sequences

            __lowercase     						=tokenizer.batch_decode(__lowercase  , skip_special_tokens=__lowercase )

            __lowercase     						=[
                'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
                'Hey, I\'m a little late to the party. I\'m going to',
            ]

            self.assertListEqual(__lowercase  , __lowercase )







       @is_pt_flax_cross_test
       def      snake_case				(							self     :	int ):







            """simple docstring"""







            __lowercase						,       __lowercase     						=self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                      # prepare inputs
                      __lowercase     						=self._prepare_for_class(__lowercase  , __lowercase )
                      __lowercase     						={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      __lowercase     						=model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      __lowercase     						=getattr(__lowercase  , __lowercase )

                      __lowercase						,       __lowercase     						=pt_inputs['input_ids'].shape
                      __lowercase     						=np.random.randint(0  , seq_length - 1  , size=(batch_size,) )
                      for batch_idx, start_index in enumerate(__lowercase ):
                           __lowercase     						=0
                           __lowercase     						=1
                           __lowercase     						=0
                           __lowercase     						=1
                      __lowercase     						=pt_model_class(__lowercase ).eval()
                      __lowercase     						=model_class(__lowercase  , dtype=jnp.floataa )

                      __lowercase     						=convert_pytorch_state_dict_to_flax(pt_model.state_dict()  , __lowercase )
                      __lowercase     						=fx_state

                      with torch.no_grad():
                           __lowercase     						=pt_model(**__lowercase ).to_tuple()

                      __lowercase     						=fx_model(**__lowercase ).to_tuple()
                      self.assertEqual(len(__lowercase )  , len(__lowercase )  , 'Output lengths differ between Flax and PyTorch' )
                      for fx_output, pt_output in zip(__lowercase  , __lowercase ):
                           self.assert_almost_equals(fx_output[:, -1]  , pt_output[:, -1].numpy()  , 4E-2 )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                           pt_model.save_pretrained(__lowercase )
                           __lowercase     						=model_class.from_pretrained(__lowercase  , from_pt=__lowercase )

                      __lowercase     						=fx_model_loaded(**__lowercase ).to_tuple()
                      self.assertEqual(
                          len(__lowercase )  , len(__lowercase )  , 'Output lengths differ between Flax and PyTorch' )
                      for fx_output_loaded, pt_output in zip(__lowercase  , __lowercase ):
                           self.assert_almost_equals(fx_output_loaded[:, -1]  , pt_output[:, -1].numpy()  , 4E-2 )







       @is_pt_flax_cross_test
       def      snake_case				(							self     :	str ):







            """simple docstring"""







            __lowercase						,       __lowercase     						=self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__ ):
                      # prepare inputs
                      __lowercase     						=self._prepare_for_class(__lowercase  , __lowercase )
                      __lowercase     						={k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      __lowercase     						=model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      __lowercase     						=getattr(__lowercase  , __lowercase )

                      __lowercase     						=pt_model_class(__lowercase ).eval()
                      __lowercase     						=model_class(__lowercase  , dtype=jnp.floataa )

                      __lowercase     						=load_flax_weights_in_pytorch_model(__lowercase  , fx_model.params )
                      __lowercase						,       __lowercase     						=pt_inputs['input_ids'].shape
                      __lowercase     						=np.random.randint(0  , seq_length - 1  , size=(batch_size,) )
                      for batch_idx, start_index in enumerate(__lowercase ):
                           __lowercase     						=0
                           __lowercase     						=1
                           __lowercase     						=0
                           __lowercase     						=1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                           __lowercase     						=pt_model(**__lowercase ).to_tuple()

                      __lowercase     						=fx_model(**__lowercase ).to_tuple()
                      self.assertEqual(len(__lowercase )  , len(__lowercase )  , 'Output lengths differ between Flax and PyTorch' )
                      for fx_output, pt_output in zip(__lowercase  , __lowercase ):
                           self.assert_almost_equals(fx_output[:, -1]  , pt_output[:, -1].numpy()  , 4E-2 )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                           fx_model.save_pretrained(__lowercase )
                           __lowercase     						=pt_model_class.from_pretrained(__lowercase  , from_flax=__lowercase )

                      with torch.no_grad():
                           __lowercase     						=pt_model_loaded(**__lowercase ).to_tuple()

                      self.assertEqual(
                          len(__lowercase )  , len(__lowercase )  , 'Output lengths differ between Flax and PyTorch' )
                      for fx_output, pt_output in zip(__lowercase  , __lowercase ):
                           self.assert_almost_equals(fx_output[:, -1]  , pt_output[:, -1].numpy()  , 4E-2 )







       @slow
       def      snake_case				(							self     :	Optional[int] ):







            """simple docstring"""







            for model_class_name in self.all_model_classes:
                 __lowercase     						=model_class_name.from_pretrained('EleutherAI/gpt-neo-125M' )
                 __lowercase     						=model(np.ones((1, 1) ) )
                 self.assertIsNotNone(__lowercase )


