






# 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,
)


__lowerCamelCase						= {
    '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:
						__lowerCamelCase						= ['DocumentQuestionAnsweringTool']
						__lowerCamelCase						= ['ImageCaptioningTool']
						__lowerCamelCase						= ['ImageQuestionAnsweringTool']
						__lowerCamelCase						= ['ImageSegmentationTool']
						__lowerCamelCase						= ['SpeechToTextTool']
						__lowerCamelCase						= ['TextClassificationTool']
						__lowerCamelCase						= ['TextQuestionAnsweringTool']
						__lowerCamelCase						= ['TextSummarizationTool']
						__lowerCamelCase						= ['TextToSpeechTool']
						__lowerCamelCase						= ['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

						__lowerCamelCase						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''semantic'''




						def __init__(    self		,	lowercase=10_000		,	lowercase=True		,	lowercase=768		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=10_048		,	lowercase=129_595		,	lowercase=129_599		,	lowercase=10_000		,	lowercase=256		,	lowercase=49.9		,	**lowercase		,	)			->						int:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	eos_token_id=lowercase		,	renormalize_logits=lowercase		,	max_new_tokens=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    Optional[int]			=						text_encoding_offset
													_a  :    str			=						text_pad_token
													_a  :    List[str]			=						eos_token_id
													_a  :    Tuple			=						semantic_infer_token
													_a  :    int			=						semantic_vocab_size
													_a  :    List[str]			=						max_input_semantic_length
													_a  :    Union[str, Any]			=						semantic_rate_hz

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''coarse_acoustics'''




						def __init__(    self		,	lowercase=True		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=12_048		,	lowercase=75		,	lowercase=2		,	lowercase=12_050		,	lowercase=256		,	lowercase = 630		,	lowercase = 60		,	**lowercase		,	)			->						List[Any]:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	renormalize_logits=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    str			=						coarse_semantic_pad_token
													_a  :    Any			=						coarse_rate_hz
													_a  :    Dict			=						n_coarse_codebooks
													_a  :    str			=						coarse_infer_token
													_a  :    List[Any]			=						max_coarse_input_length
													_a  :    Union[str, Any]			=						max_coarse_history
													_a  :    Any			=						sliding_window_len

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''fine_acoustics'''




						def __init__(    self		,	lowercase=0.5		,	lowercase=512		,	lowercase=1_024		,	lowercase=8		,	**lowercase		,	)			->						str:
													super().__init__(temperature=lowercase       )

													_a  :    Tuple			=						max_fine_history_length
													_a  :    Tuple			=						max_fine_input_length
													_a  :    Any			=						n_fine_codebooks

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''bark'''
						lowercase									=  True




						def __init__(    self		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase=24_000		,	lowercase=1_024		,	**lowercase		,	)			->						List[str]:
													if semantic_config is None:
																				_a  :    str			=						{}
																				logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

													if coarse_acoustics_config is None:
																				_a  :    Any			=						{}
																				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.'''       )

													_a  :    List[Any]			=						BarkSemanticGenerationConfig(**lowercase       )
													_a  :    Union[str, Any]			=						BarkCoarseGenerationConfig(**lowercase       )
													_a  :    str			=						BarkFineGenerationConfig(**lowercase       )

													_a  :    List[str]			=						sample_rate
													_a  :    str			=						codebook_size




						@classmethod
						def      snake_case__(    cls		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						List[Any]:
													return cls(
													    semantic_config=semantic_config.to_dict()		,	coarse_acoustics_config=coarse_acoustics_config.to_dict()		,	fine_acoustics_config=fine_acoustics_config.to_dict()		,	**lowercase		,	)







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Any			=						copy.deepcopy(self.__dict__       )

													_a  :    str			=						self.semantic_config.to_dict()
													_a  :    Any			=						self.coarse_acoustics_config.to_dict()
													_a  :    Union[str, Any]			=						self.fine_acoustics_config.to_dict()

													_a  :    List[str]			=						self.__class__.model_type
													return output