
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


SCREAMING_SNAKE_CASE__				=							logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE__				=							"""
    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       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		str            =				"bark_module"
							lowerCAmelCase__						:		List[Any]            =				["past_key_values"]

							lowerCAmelCase__						:		Optional[int]            =				{
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							    "vocab_size": "input_vocab_size",
							    "window_size": "block_size",
							}





							def __init__(					self    :  List[Any]	,					_UpperCAmelCase    :  Optional[Any]=10_24	,					_UpperCAmelCase    :  Optional[int]=1_00_48	,					_UpperCAmelCase    :  Dict=1_00_48	,					_UpperCAmelCase    :  str=12	,					_UpperCAmelCase    :  str=12	,					_UpperCAmelCase    :  List[str]=7_68	,					_UpperCAmelCase    :  List[str]=0.0	,					_UpperCAmelCase    :  Dict=True	,					_UpperCAmelCase    :  Any=0.02	,					_UpperCAmelCase    :  Optional[Any]=True	,					**_UpperCAmelCase    :  Union[str, Any]	,					)		->						str:



											"""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__(**_UpperCAmelCase							)





							@classmethod
							def      a__					(					cls    :  Any	,					_UpperCAmelCase    :  Union[str, os.PathLike]	,					_UpperCAmelCase    :  Optional[Union[str, os.PathLike]] = None	,					_UpperCAmelCase    :  bool = False	,					_UpperCAmelCase    :  bool = False	,					_UpperCAmelCase    :  Optional[Union[str, bool]] = None	,					_UpperCAmelCase    :  str = "main"	,					**_UpperCAmelCase    :  Any	,					)		->						"PretrainedConfig":



											"""simple docstring"""


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

											cls._set_token_in_kwargs(_UpperCAmelCase	,					_UpperCAmelCase							)

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

											# 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(_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       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, 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       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, 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       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				"fine_acoustics"





							def __init__(					self    :  Tuple	,					_UpperCAmelCase    :  str=True	,					_UpperCAmelCase    :  Optional[Any]=8	,					_UpperCAmelCase    :  str=1	,					**_UpperCAmelCase    :  int							)		->						List[str]:



											"""simple docstring"""


											__lowercase								=   n_codes_total
											__lowercase								=   n_codes_given

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






class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				"bark"
							lowerCAmelCase__						:		Dict            =				True





							def __init__(					self    :  Any	,					_UpperCAmelCase    :  Dict = None	,					_UpperCAmelCase    :  Dict = None	,					_UpperCAmelCase    :  Dict = None	,					_UpperCAmelCase    :  Dict = None	,					_UpperCAmelCase    :  str=0.02	,					**_UpperCAmelCase    :  List[str]	,					)		->						Union[str, Any]:



											"""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(**_UpperCAmelCase							)
											__lowercase								=   BarkCoarseConfig(**_UpperCAmelCase							)
											__lowercase								=   BarkFineConfig(**_UpperCAmelCase							)
											__lowercase								=   AutoConfig.for_model(**_UpperCAmelCase							)

											__lowercase								=   initializer_range

											super().__init__(**_UpperCAmelCase							)





							@classmethod
							def      a__					(					cls    :  List[str]	,					_UpperCAmelCase    :  BarkSemanticConfig	,					_UpperCAmelCase    :  BarkCoarseConfig	,					_UpperCAmelCase    :  BarkFineConfig	,					_UpperCAmelCase    :  AutoConfig	,					**_UpperCAmelCase    :  Optional[int]	,					)		->						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()	,					**_UpperCAmelCase	,					)





							def      a__					(					self    :  Dict							)		->						int:



											"""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







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__				=							{
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__				=							["""DocumentQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageCaptioningTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""ImageSegmentationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""SpeechToTextTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextClassificationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextQuestionAnsweringTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextSummarizationTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TextToSpeechTool"""]
							SCREAMING_SNAKE_CASE__				=							["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							SCREAMING_SNAKE_CASE__				=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





