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 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    str			      = 'bark_module'
   UpperCamelCase_					:    str			      = ['past_key_values']

   UpperCamelCase_					:    Tuple			      = {
       'num_attention_heads': 'num_heads',
       'num_hidden_layers': 'num_layers',
       'vocab_size': 'input_vocab_size',
       'window_size': 'block_size',
   }
   def __init__(     self				,  lowerCAmelCase_=10_24				,  lowerCAmelCase_=1_00_48				,  lowerCAmelCase_=1_00_48				,  lowerCAmelCase_=12				,  lowerCAmelCase_=12				,  lowerCAmelCase_=7_68				,  lowerCAmelCase_=0.0				,  lowerCAmelCase_=True				,  lowerCAmelCase_=0.02				,  lowerCAmelCase_=True				,  **lowerCAmelCase_				,  )						->Optional[int]:
        a	:     int						      =    block_size
        a	:     Dict						      =    input_vocab_size
        a	:     str						      =    output_vocab_size
        a	:     str						      =    num_layers
        a	:     Union[str, Any]						      =    num_heads
        a	:     Dict						      =    hidden_size
        a	:     Any						      =    dropout
        a	:     str						      =    bias
        a	:     str						      =    use_cache
        a	:     List[str]						      =    initializer_range

        super().__init__(**_UpperCAmelCase     )


   @classmethod
   def _lowercase(     cls				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = "main"				,  **lowerCAmelCase_				,  )						->Optional[Any]:
        a	:     Union[str, Any]						      =    cache_dir
        a	:     Tuple						      =    force_download
        a	:     Dict						      =    local_files_only
        a	:     int						      =    revision

        cls._set_token_in_kwargs(_UpperCAmelCase				,  _UpperCAmelCase     )

        a	:     Optional[int]						      =    cls.get_config_dict(_UpperCAmelCase				,  **_UpperCAmelCase     )

        # get the config dict if we are loading from Bark
        if config_dict.get('''model_type'''     ) == "bark":
             a	:     Optional[int]						      =    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(_UpperCAmelCase				,  **_UpperCAmelCase     )






@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 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    Optional[Any]			      = '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 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    Optional[Any]			      = '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 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    str			      = 'fine_acoustics'
   def __init__(     self				,  lowerCAmelCase_=True				,  lowerCAmelCase_=8				,  lowerCAmelCase_=1				,  **lowerCAmelCase_     )						->int:
        a	:     Dict						      =    n_codes_total
        a	:     int						      =    n_codes_given

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






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    Optional[Any]			      = 'bark'
   UpperCamelCase_					:    Optional[int]			      = True
   def __init__(     self				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_=0.02				,  **lowerCAmelCase_				,  )						->Any:
        if semantic_config is None:
             a	:     Union[str, Any]						      =    {}
             logger.info('''semantic_config is None. initializing the semantic model with default values.'''     )

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

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

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

        a	:     Optional[Any]						      =    BarkSemanticConfig(**_UpperCAmelCase     )
        a	:     Optional[Any]						      =    BarkCoarseConfig(**_UpperCAmelCase     )
        a	:     List[Any]						      =    BarkFineConfig(**_UpperCAmelCase     )
        a	:     List[str]						      =    AutoConfig.for_model(**_UpperCAmelCase     )

        a	:     List[Any]						      =    initializer_range

        super().__init__(**_UpperCAmelCase     )
   @classmethod
   def _lowercase(     cls				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  **lowerCAmelCase_				,  )						->Optional[int]:
        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()				,  **_UpperCAmelCase				,  )


   def _lowercase(     self     )						->List[str]:
        a	:     str						      =    copy.deepcopy(self.__dict__     )

        a	:     str						      =    self.semantic_config.to_dict()
        a	:     Any						      =    self.coarse_acoustics_config.to_dict()
        a	:     Any						      =    self.fine_acoustics_config.to_dict()
        a	:     str						      =    self.codec_config.to_dict()

        a	:     Dict						      =    self.__class__.model_type
        return output






'''simple docstring'''






import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig
def 				_lowerCamelCase(	UpperCamelCase__     :			List[str]     ,							UpperCamelCase__     :			Optional[Any]     ,							UpperCamelCase__     :			List[str]=0		)		->	Optional[int]:
    # Format the message.
    if name is None:
        A							:							Dict				=    None
    else:
        A							:							Optional[Any]				=    '''.''' * max(0     ,							spaces - 2		) + '''# {:''' + str(50 - spaces		) + '''s}'''
        A							:							Tuple				=    fmt.format(UpperCamelCase__		)

    # Print and recurse (if needed).
    if isinstance(UpperCamelCase__     ,							UpperCamelCase__		):
        if msg is not None:
            print(UpperCamelCase__		)
        for k in val.keys():
            recursive_print(UpperCamelCase__     ,							val[k]     ,							spaces + 2		)
    elif isinstance(UpperCamelCase__     ,							torch.Tensor		):
        print(UpperCamelCase__     ,							''':'''     ,							val.size()		)
    else:
        print(UpperCamelCase__     ,							''':'''     ,							UpperCamelCase__		)
def 				_lowerCamelCase(	UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[Any]     ,							UpperCamelCase__     :			Dict     ,							UpperCamelCase__     :			List[str]		)		->	Dict:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A							:							int				=    param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A							:							List[str]				=    (num_heads, num_splits, hidden_size) + input_shape[1:]
    A							:							Optional[int]				=    param.view(*UpperCamelCase__		)
    A							:							Optional[Any]				=    param.transpose(0     ,							1		).contiguous()
    A							:							Dict				=    param.view(*UpperCamelCase__		)
    return param
def 				_lowerCamelCase(	UpperCamelCase__     :			Any     ,							UpperCamelCase__     :			Tuple		)		->	Optional[int]:
    A							:							int				=    config.n_positions
    A							:							Dict				=    config.n_layer
    A							:							List[str]				=    config.vocab_size
    A							:							Optional[Any]				=    config.n_head
    A							:							Union[str, Any]				=    config.n_embd // config.n_head

    A							:							Optional[int]				=    sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
    A							:							Any				=    {
        '''transformer.wte.weight''': word_embeddings,
        '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
        '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
        '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
    }

    A							:							Optional[int]				=    '''model.language_model.encoder.layers.'''
    for i in range(UpperCamelCase__		):
        A							:							Dict				=    torch.tril(torch.ones((n_positions, n_positions)     ,							dtype=torch.bool		)		)
        A							:							Any				=    causal_mask.view(1     ,							1     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    causal_mask
        A							:							List[str]				=    torch.tensor(-1e4     ,							dtype=torch.bfloataa		)

        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.weight''']
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.input_layernorm.bias''']

        A							:							Any				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.weight''']
        A							:							List[Any]				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala.transpose(0     ,							1		).contiguous()

        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.self_attention.query_key_value.bias''']
        A							:							Any				=    fix_query_key_value_ordering(UpperCamelCase__     ,							3     ,							UpperCamelCase__     ,							UpperCamelCase__		)
        A							:							List[Any]				=    vala

        A							:							Tuple				=    sd_megatron[F'''{pf}{i}.self_attention.dense.weight'''].transpose(
            0     ,							1		)
        A							:							Optional[int]				=    sd_megatron[F'''{pf}{i}.self_attention.dense.bias''']
        A							:							Any				=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.weight''']
        A							:							Any				=    sd_megatron[F'''{pf}{i}.post_attention_layernorm.bias''']
        A							:							str				=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0     ,							1		)
        A							:							List[Any]				=    sd_megatron[F'''{pf}{i}.mlp.dense_h_to_4h.bias''']
        A							:							Tuple				=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
            0     ,							1		)
        A							:							Union[str, Any]				=    sd_megatron[F'''{pf}{i}.mlp.dense_4h_to_h.bias''']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A							:							Optional[int]				=    word_embeddings

    return sd_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			Union[str, Any]     ,							UpperCamelCase__     :			Optional[Any]		)		->	Union[str, Any]:
    A							:							List[str]				=    64_000
    A							:							str				=    config_megatron['''encoder_seq_length''']
    A							:							Union[str, Any]				=    config_megatron['''hidden_size''']
    A							:							Tuple				=    config_megatron['''num_layers''']
    A							:							List[str]				=    config_megatron['''num_attention_heads''']
    A							:							List[Any]				=    config_megatron['''ffn_hidden_size''']
    A							:							List[str]				=    '''gelu'''
    A							:							int				=    0.1
    A							:							Union[str, Any]				=    0.1
    A							:							Optional[int]				=    0.1
    A							:							Optional[int]				=    config_megatron['''layernorm_epsilon''']  # 1e-5
    A							:							Tuple				=    config_megatron['''init_method_std''']  # 0.02
    A							:							Any				=    config_megatron['''apply_query_key_layer_scaling''']  # True
    A							:							Tuple				=    True
    A							:							str				=    True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4_096:
        A							:							Tuple				=    1  # <|endoftext|>
        A							:							Any				=    1  # <|endoftext|>
        A							:							List[str]				=    0  # <unk>
    else:
        A							:							Any				=    2  # <s>
        A							:							str				=    3  # <|endoftext|>
        A							:							Tuple				=    0  # <pad>

    return config_hf
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	Tuple:
    print(UpperCamelCase__		)

    A							:							Tuple				=    args.checkpoint_path
    A							:							Dict				=    args.save_path
    if isfile(UpperCamelCase__		):
        raise FileNotFoundError(F'''ERROR! could not find file {checkpoint_path}'''		)

    # Load the model.
    A							:							str				=    torch.load(UpperCamelCase__     ,							map_location='''cpu'''		)

    # Load the config.
    A							:							List[str]				=    checkpoint['''hyper_parameters''']['''cfg''']
    A							:							Tuple				=    GPTaConfig()
    A							:							int				=    copy_config(config_hf=UpperCamelCase__     ,							config_megatron=UpperCamelCase__		)
    A							:							Any				=    ['''GPT2LMHeadModel''']

    A							:							Optional[int]				=    checkpoint['''state_dict''']

    # Convert.
    print('''Converting'''		)
    A							:							Any				=    convert_megatron_checkpoint(UpperCamelCase__     ,							UpperCamelCase__		)

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(UpperCamelCase__     ,							UpperCamelCase__		)

    A							:							Union[str, Any]				=    '''GPTSw3Tokenizer'''

    # Store the config to file.
    print('''Saving config'''		)
    config_hf.save_pretrained(UpperCamelCase__		)

    # Store the state_dict to file.
    A							:							int				=    os.path.join(UpperCamelCase__     ,							'''pytorch_model.bin'''		)
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''		)
    torch.save(UpperCamelCase__     ,							UpperCamelCase__		)


if __name__ == "__main__":
     snake_case_     			=						argparse.ArgumentParser()
     parser.add_argument(
         """--checkpoint_path""",
         type=str,
         required=True,
         help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
     )
     parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
     parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
     snake_case_     			=						parser.parse_args()
     main(_args)
