





import copy
from typing import Dict

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


__lowerCAmelCase 	=      logging.get_logger(__name__)




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "semantic"




					def __init__(    self     ,   __lowerCAmelCase=10_000     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=768     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=0.7     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=10_048     ,   __lowerCAmelCase=129_595     ,   __lowerCAmelCase=129_599     ,   __lowerCAmelCase=10_000     ,   __lowerCAmelCase=256     ,   __lowerCAmelCase=4_9.9     ,   **__lowerCAmelCase     ,   )    ->	Dict:
							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 	_UpperCAmelCase						( _lowercase       ):
					__A        = "coarse_acoustics"




					def __init__(    self     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=False     ,   __lowerCAmelCase=0.7     ,   __lowerCAmelCase=True     ,   __lowerCAmelCase=12_048     ,   __lowerCAmelCase=75     ,   __lowerCAmelCase=2     ,   __lowerCAmelCase=12_050     ,   __lowerCAmelCase=256     ,   __lowerCAmelCase = 630     ,   __lowerCAmelCase = 60     ,   **__lowerCAmelCase     ,   )    ->	Optional[int]:
							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 	_UpperCAmelCase						( _lowercase       ):
					__A        = "fine_acoustics"




					def __init__(    self     ,   __lowerCAmelCase=0.5     ,   __lowerCAmelCase=512     ,   __lowerCAmelCase=1_024     ,   __lowerCAmelCase=8     ,   **__lowerCAmelCase     ,   )    ->	Optional[Any]:
							super().__init__(temperature=__lowerCAmelCase							)

							UpperCamelCase__								=						max_fine_history_length
							UpperCamelCase__								=						max_fine_input_length
							UpperCamelCase__								=						n_fine_codebooks




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "bark"
					__A        = True




					def __init__(    self     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase=24_000     ,   __lowerCAmelCase=1_024     ,   **__lowerCAmelCase     ,   )    ->	List[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 						snake_case_		(    cls     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   **__lowerCAmelCase     ,   )    ->	str:
							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 						snake_case_		(    self							)    ->	Optional[int]:
							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












from __future__ import annotations

def      a    (   _lowercase :  float | int			,    _lowercase :  float | int			,    _lowercase :  float | int			,    )  ->list[float | int]:
		if not all((nth_term, start_term_a, common_ratio_r)							):
				return []
		UpperCamelCase__								=						[]
		UpperCamelCase__								=						1
		UpperCamelCase__								=						common_ratio_r
		for _ in range(int(_lowercase							)							):
				if not series:
						series.append(_lowercase							)
				else:
						power += 1
						series.append(float(start_term_a * multiple							)							)
						UpperCamelCase__								=						pow(float(_lowercase							)			,    _lowercase							)
		return series


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__lowerCAmelCase 	=      float(input("Enter the last number (n term) of the Geometric Series"))
				__lowerCAmelCase 	=      float(input("Enter the starting term (a) of the Geometric Series"))
				__lowerCAmelCase 	=      float(
				    input("Enter the common ratio between two terms (r) of the Geometric Series")
				)
				print("Formula of Geometric Series => a + ar + ar^2 ... +ar^n")
				print(geometric_series(nth_term, start_term_a, common_ratio_r))





