




'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


A							:      Optional[Any] 	=       logging.get_logger(__name__)  # pylint: disable=invalid-name






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					["""safety_checker""", """feature_extractor"""]

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = True					,		)    ->					List[str]:

           """simple docstring"""



           super().__init__()
           if hasattr(scheduler.config					,		"""steps_offset"""     ) and scheduler.config.steps_offset != 1:
               __lowercase							=						(
                   F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
                   F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
                   """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
                   """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
                   """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
                   """ file"""
               )
               deprecate("""steps_offset!=1"""					,		"""1.0.0"""					,		lowerCamelCase__					,		standard_warn=lowerCamelCase__     )
               __lowercase							=						dict(scheduler.config     )
               __lowercase							=						1
               __lowercase							=						FrozenDict(lowerCamelCase__     )

           self.register_modules(
               vae=lowerCamelCase__					,		text_encoder=lowerCamelCase__					,		tokenizer=lowerCamelCase__					,		unet=lowerCamelCase__					,		scheduler=lowerCamelCase__					,		safety_checker=lowerCamelCase__					,		feature_extractor=lowerCamelCase__					,		)
           self.register_to_config(requires_safety_checker=lowerCamelCase__     )

       @property
       def     snake_case__			(    self     )    ->					Dict[str, Any]:

           """simple docstring"""



           return {k: getattr(self					,		lowerCamelCase__     ) for k in self.config.keys() if not k.startswith("""_"""     )}

       def     snake_case__			(    self					,		lowerCamelCase__ = "auto"     )    ->					List[Any]:

           """simple docstring"""



           if slice_size == "auto":
               # half the attention head size is usually a good trade-off between
               # speed and memory
               __lowercase							=						self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           self.enable_attention_slicing(lowerCamelCase__     )

       @torch.no_grad()
       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = 0.8					,		lowerCamelCase__ = 50					,		lowerCamelCase__ = 7.5					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 1					,		lowerCamelCase__ = 0.0					,		lowerCamelCase__ = None					,		lowerCamelCase__ = "pil"					,		lowerCamelCase__ = True					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 1					,		)    ->					Any:

           """simple docstring"""



           return StableDiffusionInpaintPipelineLegacy(**self.components     )(
               prompt=lowerCamelCase__					,		image=lowerCamelCase__					,		mask_image=lowerCamelCase__					,		strength=lowerCamelCase__					,		num_inference_steps=lowerCamelCase__					,		guidance_scale=lowerCamelCase__					,		negative_prompt=lowerCamelCase__					,		num_images_per_prompt=lowerCamelCase__					,		eta=lowerCamelCase__					,		generator=lowerCamelCase__					,		output_type=lowerCamelCase__					,		return_dict=lowerCamelCase__					,		callback=lowerCamelCase__					,		)

       @torch.no_grad()
       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = 0.8					,		lowerCamelCase__ = 50					,		lowerCamelCase__ = 7.5					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 1					,		lowerCamelCase__ = 0.0					,		lowerCamelCase__ = None					,		lowerCamelCase__ = "pil"					,		lowerCamelCase__ = True					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 1					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           return StableDiffusionImgaImgPipeline(**self.components     )(
               prompt=lowerCamelCase__					,		image=lowerCamelCase__					,		strength=lowerCamelCase__					,		num_inference_steps=lowerCamelCase__					,		guidance_scale=lowerCamelCase__					,		negative_prompt=lowerCamelCase__					,		num_images_per_prompt=lowerCamelCase__					,		eta=lowerCamelCase__					,		generator=lowerCamelCase__					,		output_type=lowerCamelCase__					,		return_dict=lowerCamelCase__					,		callback=lowerCamelCase__					,		callback_steps=lowerCamelCase__					,		)




       @torch.no_grad()
       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__ = 512					,		lowerCamelCase__ = 512					,		lowerCamelCase__ = 50					,		lowerCamelCase__ = 7.5					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 1					,		lowerCamelCase__ = 0.0					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = "pil"					,		lowerCamelCase__ = True					,		lowerCamelCase__ = None					,		lowerCamelCase__ = 1					,		)    ->					Optional[int]:

           """simple docstring"""



           return StableDiffusionPipeline(**self.components     )(
               prompt=lowerCamelCase__					,		height=lowerCamelCase__					,		width=lowerCamelCase__					,		num_inference_steps=lowerCamelCase__					,		guidance_scale=lowerCamelCase__					,		negative_prompt=lowerCamelCase__					,		num_images_per_prompt=lowerCamelCase__					,		eta=lowerCamelCase__					,		generator=lowerCamelCase__					,		latents=lowerCamelCase__					,		output_type=lowerCamelCase__					,		return_dict=lowerCamelCase__					,		callback=lowerCamelCase__					,		callback_steps=lowerCamelCase__					,		)










'''simple docstring'''
import copy
from typing import Dict

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


A							:      Union[str, Any] 	=       logging.get_logger(__name__)






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



           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__					,		)

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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



           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__					,		)

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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     Dict    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """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."""     )

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

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, 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()					,		**lowerCamelCase__					,		)




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """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.__class__.model_type
           return output




