





'''simple docstring'''

import copy
from typing import Dict

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


lowerCAmelCase__      :   int      	=			logging.get_logger(__name__)


class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	'''semantic'''
					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      Union[str, Any]=10_000		,	UpperCAmelCase_       :      str=True		,	UpperCAmelCase_       :      Optional[int]=768		,	UpperCAmelCase_       :      Optional[int]=False		,	UpperCAmelCase_       :      Union[str, Any]=False		,	UpperCAmelCase_       :      int=False		,	UpperCAmelCase_       :      Any=False		,	UpperCAmelCase_       :      List[Any]=0.7		,	UpperCAmelCase_       :      Dict=True		,	UpperCAmelCase_       :      Union[str, Any]=10_048		,	UpperCAmelCase_       :      List[Any]=129_595		,	UpperCAmelCase_       :      Dict=129_599		,	UpperCAmelCase_       :      Optional[Any]=10_000		,	UpperCAmelCase_       :      List[Any]=256		,	UpperCAmelCase_       :      List[str]=49.9		,	**UpperCAmelCase_       :      Union[str, Any]		,	):







							"""simple docstring"""
							super().__init__(
							    temperature=UpperCAmelCase_		,	do_sample=UpperCAmelCase_		,	eos_token_id=UpperCAmelCase_		,	renormalize_logits=UpperCAmelCase_		,	max_new_tokens=UpperCAmelCase_		,	output_scores=UpperCAmelCase_		,	return_dict_in_generate=UpperCAmelCase_		,	output_hidden_states=UpperCAmelCase_		,	output_attentions=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Dict     =     text_encoding_offset
							__UpperCAmelCase	:							Union[str, Any]     =     text_pad_token
							__UpperCAmelCase	:							int     =     eos_token_id
							__UpperCAmelCase	:							int     =     semantic_infer_token
							__UpperCAmelCase	:							Dict     =     semantic_vocab_size
							__UpperCAmelCase	:							List[Any]     =     max_input_semantic_length
							__UpperCAmelCase	:							Any     =     semantic_rate_hz






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	'''coarse_acoustics'''
					def __init__(   self       :      str		,	UpperCAmelCase_       :      List[Any]=True		,	UpperCAmelCase_       :      int=False		,	UpperCAmelCase_       :      List[Any]=False		,	UpperCAmelCase_       :      Union[str, Any]=False		,	UpperCAmelCase_       :      str=False		,	UpperCAmelCase_       :      Tuple=0.7		,	UpperCAmelCase_       :      Optional[Any]=True		,	UpperCAmelCase_       :      Dict=12_048		,	UpperCAmelCase_       :      Tuple=75		,	UpperCAmelCase_       :      List[str]=2		,	UpperCAmelCase_       :      Any=12_050		,	UpperCAmelCase_       :      int=256		,	UpperCAmelCase_       :      int = 630		,	UpperCAmelCase_       :      int = 60		,	**UpperCAmelCase_       :      Optional[Any]		,	):







							"""simple docstring"""
							super().__init__(
							    temperature=UpperCAmelCase_		,	do_sample=UpperCAmelCase_		,	renormalize_logits=UpperCAmelCase_		,	output_scores=UpperCAmelCase_		,	return_dict_in_generate=UpperCAmelCase_		,	output_hidden_states=UpperCAmelCase_		,	output_attentions=UpperCAmelCase_		,	**UpperCAmelCase_		,	)

							__UpperCAmelCase	:							Optional[Any]     =     coarse_semantic_pad_token
							__UpperCAmelCase	:							Any     =     coarse_rate_hz
							__UpperCAmelCase	:							str     =     n_coarse_codebooks
							__UpperCAmelCase	:							List[str]     =     coarse_infer_token
							__UpperCAmelCase	:							List[str]     =     max_coarse_input_length
							__UpperCAmelCase	:							int     =     max_coarse_history
							__UpperCAmelCase	:							str     =     sliding_window_len






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	'''fine_acoustics'''
					def __init__(   self       :      Tuple		,	UpperCAmelCase_       :      Dict=0.5		,	UpperCAmelCase_       :      str=512		,	UpperCAmelCase_       :      Union[str, Any]=1_024		,	UpperCAmelCase_       :      Optional[Any]=8		,	**UpperCAmelCase_       :      List[str]		,	):







							"""simple docstring"""
							super().__init__(temperature=UpperCAmelCase_		)

							__UpperCAmelCase	:							Any     =     max_fine_history_length
							__UpperCAmelCase	:							List[Any]     =     max_fine_input_length
							__UpperCAmelCase	:							int     =     n_fine_codebooks






class      SCREAMING_SNAKE_CASE__      (   snake_case__		):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE							=	'''bark'''
					SCREAMING_SNAKE_CASE							=	True
					def __init__(   self       :      Optional[int]		,	UpperCAmelCase_       :      Dict = None		,	UpperCAmelCase_       :      Dict = None		,	UpperCAmelCase_       :      Dict = None		,	UpperCAmelCase_       :      Tuple=24_000		,	UpperCAmelCase_       :      Tuple=1_024		,	**UpperCAmelCase_       :      List[str]		,	):







							"""simple docstring"""
							if semantic_config is None:
									__UpperCAmelCase	:							Union[str, Any]     =     {}
									logger.info("semantic_config is None. initializing the semantic model with default values."		)

							if coarse_acoustics_config is None:
									__UpperCAmelCase	:							List[Any]     =     {}
									logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."		)

							if fine_acoustics_config is None:
									__UpperCAmelCase	:							Union[str, Any]     =     {}
									logger.info("fine_acoustics_config is None. initializing the fine model with default values."		)

							__UpperCAmelCase	:							str     =     BarkSemanticGenerationConfig(**UpperCAmelCase_		)
							__UpperCAmelCase	:							List[str]     =     BarkCoarseGenerationConfig(**UpperCAmelCase_		)
							__UpperCAmelCase	:							Union[str, Any]     =     BarkFineGenerationConfig(**UpperCAmelCase_		)

							__UpperCAmelCase	:							List[str]     =     sample_rate
							__UpperCAmelCase	:							Optional[int]     =     codebook_size
					@classmethod
					def 		lowerCamelCase_   (   cls       :      List[str]		,	UpperCAmelCase_       :      BarkSemanticGenerationConfig		,	UpperCAmelCase_       :      BarkCoarseGenerationConfig		,	UpperCAmelCase_       :      BarkFineGenerationConfig		,	**UpperCAmelCase_       :      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()		,	**UpperCAmelCase_		,	)






					def 		lowerCamelCase_   (   self       :      Union[str, Any]		):







							"""simple docstring"""
							__UpperCAmelCase	:							Optional[int]     =     copy.deepcopy(self.__dict__		)

							__UpperCAmelCase	:							str     =     self.semantic_config.to_dict()
							__UpperCAmelCase	:							Dict     =     self.coarse_acoustics_config.to_dict()
							__UpperCAmelCase	:							Tuple     =     self.fine_acoustics_config.to_dict()

							__UpperCAmelCase	:							Dict     =     self.__class__.model_type
							return output











'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




