
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


__magic_name__           =					logging.get_logger(__name__)


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

__magic_name__           =					'''\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'''




class    _SCREAMING_SNAKE_CASE					( _lowerCAmelCase	):
						_A  :						Dict      		=       "bark_module"
						_A  :						Optional[int]      		=       ["past_key_values"]

						_A  :						str      		=       {
						    "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=0.0_2						,		lowerCamelCase=True						,		**lowerCamelCase						,		):
										snake_case__  =			block_size
										snake_case__  =			input_vocab_size
										snake_case__  =			output_vocab_size
										snake_case__  =			num_layers
										snake_case__  =			num_heads
										snake_case__  =			hidden_size
										snake_case__  =			dropout
										snake_case__  =			bias
										snake_case__  =			use_cache
										snake_case__  =			initializer_range

										super().__init__(**_lowerCamelCase	)



						@classmethod
						def A_     (     cls						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = False						,		lowerCamelCase = False						,		lowerCamelCase = None						,		lowerCamelCase = "main"						,		**lowerCamelCase						,		):
										snake_case__  =			cache_dir
										snake_case__  =			force_download
										snake_case__  =			local_files_only
										snake_case__  =			revision

										cls._set_token_in_kwargs(_lowerCamelCase						,		_lowerCamelCase	)

										snake_case__  ,   snake_case__  =			cls.get_config_dict(_lowerCamelCase						,		**_lowerCamelCase	)

										# get the config dict if we are loading from Bark
										if config_dict.get("model_type"	) == "bark":
														snake_case__  =			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(_lowerCamelCase						,		**_lowerCamelCase	)





@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					( _lowerCAmelCase	):
						_A  :						List[str]      		=       "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					( _lowerCAmelCase	):
						_A  :						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					( _lowerCAmelCase	):
						_A  :						Any      		=       "fine_acoustics"
						def __init__(     self						,		lowerCamelCase=True						,		lowerCamelCase=8						,		lowerCamelCase=1						,		**lowerCamelCase	):
										snake_case__  =			n_codes_total
										snake_case__  =			n_codes_given

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





class    _SCREAMING_SNAKE_CASE					( _lowerCAmelCase	):
						_A  :						Dict      		=       "bark"
						_A  :						int      		=       True
						def __init__(     self						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase = None						,		lowerCamelCase=0.0_2						,		**lowerCamelCase						,		):
										if semantic_config is None:
														snake_case__  =			{}
														logger.info("semantic_config is None. initializing the semantic model with default values."	)

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

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

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

										snake_case__  =			BarkSemanticConfig(**_lowerCamelCase	)
										snake_case__  =			BarkCoarseConfig(**_lowerCamelCase	)
										snake_case__  =			BarkFineConfig(**_lowerCamelCase	)
										snake_case__  =			AutoConfig.for_model(**_lowerCamelCase	)

										snake_case__  =			initializer_range

										super().__init__(**_lowerCamelCase	)
						@classmethod
						def A_     (     cls						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		**lowerCamelCase						,		):
										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()						,		**_lowerCamelCase						,		)



						def A_     (     self	):
										snake_case__  =			copy.deepcopy(self.__dict__	)

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

										snake_case__  =			self.__class__.model_type
										return output





from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      @abstractmethod
      def __lt__(     self						,		lowerCamelCase	):
          pass
      @abstractmethod
      def __gt__(     self						,		lowerCamelCase	):
          pass



      @abstractmethod
      def __eq__(     self						,		lowerCamelCase	):
          pass





__magic_name__           =					TypeVar('''T''', bound=Comparable)




class    _SCREAMING_SNAKE_CASE					( Generic[T]	):
      def __init__(     self	):
          snake_case__  =			[]
          snake_case__  =			0
      def __repr__(     self	):
          return str(self.h	)
      def A_     (     self						,		lowerCamelCase	):
          if child_idx > 0:
              return (child_idx - 1) // 2
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 1
          if left_child_index < self.heap_size:
              return left_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			2 * parent_idx + 2
          if right_child_index < self.heap_size:
              return right_child_index
          return None
      def A_     (     self						,		lowerCamelCase	):
          if index < self.heap_size:
              snake_case__  =			index
              snake_case__  =			self.left_child_idx(lowerCamelCase	)
              snake_case__  =			self.right_child_idx(lowerCamelCase	)
              # check which child is larger than its parent
              if left_child is not None and self.h[left_child] > self.h[violation]:
                  snake_case__  =			left_child
              if right_child is not None and self.h[right_child] > self.h[violation]:
                  snake_case__  =			right_child
              # if violation indeed exists
              if violation != index:
                  # swap to fix the violation
                  snake_case__  ,   snake_case__  =			self.h[index], self.h[violation]
                  # fix the subsequent violation recursively if any
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self						,		lowerCamelCase	):
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			len(self.h	)
          if self.heap_size > 1:
              # max_heapify from right to left but exclude leaves (last level)
              for i in range(self.heap_size // 2 - 1						,		-1						,		-1	):
                  self.max_heapify(lowerCamelCase	)
      def A_     (     self	):
          if self.heap_size >= 2:
              snake_case__  =			self.h[0]
              snake_case__  =			self.h.pop(-1	)
              self.heap_size -= 1
              self.max_heapify(0	)
              return me
          elif self.heap_size == 1:
              self.heap_size -= 1
              return self.h.pop(-1	)
          else:
              raise Exception("Empty heap"	)
      def A_     (     self						,		lowerCamelCase	):
          self.h.append(lowerCamelCase	)
          snake_case__  =			(self.heap_size - 1) // 2
          self.heap_size += 1
          while idx >= 0:
              self.max_heapify(lowerCamelCase	)
              snake_case__  =			(idx - 1) // 2



      def A_     (     self	):
          snake_case__  =			self.heap_size
          for j in range(size - 1						,		0						,		-1	):
              snake_case__  ,   snake_case__  =			self.h[j], self.h[0]
              self.heap_size -= 1
              self.max_heapify(0	)
          snake_case__  =			size


if __name__ == "__main__":
     import doctest

     # run doc test
     doctest.testmod()

     # demo
     for unsorted in [
         [0],
         [2],
         [3, 5],
         [5, 3],
         [5, 5],
         [0, 0, 0, 0],
         [1, 1, 1, 1],
         [2, 2, 3, 5],
         [0, 2, 2, 3, 5],
         [2, 5, 3, 0, 2, 3, 0, 3],
         [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
         [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
         [-45, -2, -5],
     ]:
          print(F'''unsorted array: {unsorted}''')

          __magic_name__           =					Heap()
          heap.build_max_heap(unsorted)
          print(F'''after build heap: {heap}''')

          print(F'''max value: {heap.extract_max()}''')
          print(F'''after max value removed: {heap}''')

          heap.insert(100)
          print(F'''after new value 100 inserted: {heap}''')

          heap.heap_sort()
          print(F'''heap-sorted array: {heap}\n''')



