"""simple docstring"""






import copy
from typing import Dict

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


lowerCAmelCase__  :  Dict											=     logging.get_logger(__name__)
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"semantic"







					def __init__(    self       :     Optional[int]   ,lowerCamelCase__       :     Tuple=10_000   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     Union[str, Any]=768   ,lowerCamelCase__       :     Dict=False   ,lowerCamelCase__       :     Optional[int]=False   ,lowerCamelCase__       :     Any=False   ,lowerCamelCase__       :     str=False   ,lowerCamelCase__       :     List[Any]=0.7   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     Dict=10_048   ,lowerCamelCase__       :     List[str]=129_595   ,lowerCamelCase__       :     List[str]=129_599   ,lowerCamelCase__       :     Any=10_000   ,lowerCamelCase__       :     int=256   ,lowerCamelCase__       :     List[str]=4_9.9   ,**lowerCamelCase__       :     str   ,):
									super().__init__(
									    temperature=lowerCamelCase__   ,do_sample=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,renormalize_logits=lowerCamelCase__   ,max_new_tokens=lowerCamelCase__   ,output_scores=lowerCamelCase__   ,return_dict_in_generate=lowerCamelCase__   ,output_hidden_states=lowerCamelCase__   ,output_attentions=lowerCamelCase__   ,**lowerCamelCase__   ,)

									UpperCAmelCase__					=							text_encoding_offset
									UpperCAmelCase__					=							text_pad_token
									UpperCAmelCase__					=							eos_token_id
									UpperCAmelCase__					=							semantic_infer_token
									UpperCAmelCase__					=							semantic_vocab_size
									UpperCAmelCase__					=							max_input_semantic_length
									UpperCAmelCase__					=							semantic_rate_hz






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"coarse_acoustics"







					def __init__(    self       :     Optional[Any]   ,lowerCamelCase__       :     int=True   ,lowerCamelCase__       :     List[str]=False   ,lowerCamelCase__       :     Union[str, Any]=False   ,lowerCamelCase__       :     Any=False   ,lowerCamelCase__       :     Dict=False   ,lowerCamelCase__       :     Optional[Any]=0.7   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     Tuple=12_048   ,lowerCamelCase__       :     Optional[Any]=75   ,lowerCamelCase__       :     List[Any]=2   ,lowerCamelCase__       :     List[str]=12_050   ,lowerCamelCase__       :     Optional[int]=256   ,lowerCamelCase__       :     int = 630   ,lowerCamelCase__       :     int = 60   ,**lowerCamelCase__       :     Dict   ,):
									super().__init__(
									    temperature=lowerCamelCase__   ,do_sample=lowerCamelCase__   ,renormalize_logits=lowerCamelCase__   ,output_scores=lowerCamelCase__   ,return_dict_in_generate=lowerCamelCase__   ,output_hidden_states=lowerCamelCase__   ,output_attentions=lowerCamelCase__   ,**lowerCamelCase__   ,)

									UpperCAmelCase__					=							coarse_semantic_pad_token
									UpperCAmelCase__					=							coarse_rate_hz
									UpperCAmelCase__					=							n_coarse_codebooks
									UpperCAmelCase__					=							coarse_infer_token
									UpperCAmelCase__					=							max_coarse_input_length
									UpperCAmelCase__					=							max_coarse_history
									UpperCAmelCase__					=							sliding_window_len






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"fine_acoustics"







					def __init__(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Any=0.5   ,lowerCamelCase__       :     List[Any]=512   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[Any]=8   ,**lowerCamelCase__       :     int   ,):
									super().__init__(temperature=lowerCamelCase__      )

									UpperCAmelCase__					=							max_fine_history_length
									UpperCAmelCase__					=							max_fine_input_length
									UpperCAmelCase__					=							n_fine_codebooks






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"bark"
					snake_case__       =			True







					def __init__(    self       :     str   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     Dict = None   ,lowerCamelCase__       :     Union[str, Any]=24_000   ,lowerCamelCase__       :     Optional[int]=1_024   ,**lowerCamelCase__       :     Optional[Any]   ,):
									if semantic_config is None:
													UpperCAmelCase__					=							{}
													logger.info('semantic_config is None. initializing the semantic model with default values.'      )

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

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

									UpperCAmelCase__					=							BarkSemanticGenerationConfig(**lowerCamelCase__      )
									UpperCAmelCase__					=							BarkCoarseGenerationConfig(**lowerCamelCase__      )
									UpperCAmelCase__					=							BarkFineGenerationConfig(**lowerCamelCase__      )

									UpperCAmelCase__					=							sample_rate
									UpperCAmelCase__					=							codebook_size







					@classmethod
					def 		__lowerCAmelCase						(    cls       :     int   ,lowerCamelCase__       :     BarkSemanticGenerationConfig   ,lowerCamelCase__       :     BarkCoarseGenerationConfig   ,lowerCamelCase__       :     BarkFineGenerationConfig   ,**lowerCamelCase__       :     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()   ,**lowerCamelCase__   ,)







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							copy.deepcopy(self.__dict__      )

									UpperCAmelCase__					=							self.semantic_config.to_dict()
									UpperCAmelCase__					=							self.coarse_acoustics_config.to_dict()
									UpperCAmelCase__					=							self.fine_acoustics_config.to_dict()

									UpperCAmelCase__					=							self.__class__.model_type
									return output




"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowerCAmelCase__  :  List[Any]											=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class     snake_case	(			unittest.TestCase				,   __UpperCAmelCase    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							load_tool('summarization'      )
									self.tool.setup()
									UpperCAmelCase__					=							load_tool('summarization'   ,remote=lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)



