




'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case				:				Optional[Any]       							=		logging.get_logger(__name__)

_snake_case				:				Union[str, Any]       							=		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class       A   (		_a   ):
							lowercase_          =    'big_bird'







							def __init__(  self	:  Any							,      lowerCAmelCase_	:  Optional[Any]=5_03_58							,      lowerCAmelCase_	:  Optional[Any]=7_68							,      lowerCAmelCase_	:  int=12							,      lowerCAmelCase_	:  Optional[int]=12							,      lowerCAmelCase_	:  List[Any]=30_72							,      lowerCAmelCase_	:  Any="gelu_new"							,      lowerCAmelCase_	:  Any=0.1							,      lowerCAmelCase_	:  Dict=0.1							,      lowerCAmelCase_	:  str=40_96							,      lowerCAmelCase_	:  int=2							,      lowerCAmelCase_	:  Any=0.0_2							,      lowerCAmelCase_	:  int=1e-12							,      lowerCAmelCase_	:  List[str]=True							,      lowerCAmelCase_	:  Optional[Any]=0							,      lowerCAmelCase_	:  List[Any]=1							,      lowerCAmelCase_	:  str=2							,      lowerCAmelCase_	:  Optional[int]=66							,      lowerCAmelCase_	:  List[Any]="block_sparse"							,      lowerCAmelCase_	:  Tuple=True							,      lowerCAmelCase_	:  str=False							,      lowerCAmelCase_	:  str=64							,      lowerCAmelCase_	:  Optional[int]=3							,      lowerCAmelCase_	:  List[str]=None							,      **lowerCAmelCase_	:  Union[str, Any]							,      )      ->							List[Any]:


														"""simple docstring"""


														super().__init__(
														    pad_token_id=lowerCAmelCase_							,      bos_token_id=lowerCAmelCase_							,      eos_token_id=lowerCAmelCase_							,      sep_token_id=lowerCAmelCase_							,      **lowerCAmelCase_							,      )

														_a     					=    vocab_size
														_a     					=    max_position_embeddings
														_a     					=    hidden_size
														_a     					=    num_hidden_layers
														_a     					=    num_attention_heads
														_a     					=    intermediate_size
														_a     					=    hidden_act
														_a     					=    hidden_dropout_prob
														_a     					=    attention_probs_dropout_prob
														_a     					=    initializer_range
														_a     					=    type_vocab_size
														_a     					=    layer_norm_eps
														_a     					=    use_cache

														_a     					=    rescale_embeddings
														_a     					=    attention_type
														_a     					=    use_bias
														_a     					=    block_size
														_a     					=    num_random_blocks
														_a     					=    classifier_dropout



class       A   (		_a   ):







							@property
							def        __lowerCAmelCase      (  self	:  int )      ->							Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														if self.task == "multiple-choice":
																					_a     					=    {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
														else:
																					_a     					=    {0: '''batch''', 1: '''sequence'''}
														return OrderedDict(
														    [
														        ('''input_ids''', dynamic_axis),
														        ('''attention_mask''', dynamic_axis),
														    ] )







'''simple docstring'''


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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

class       A   (		_a   ):







							def __init__(  self	:  Dict							,      lowerCAmelCase_	:  CLIPSegForImageSegmentation							,      lowerCAmelCase_	:  CLIPSegProcessor							,      lowerCAmelCase_	:  AutoencoderKL							,      lowerCAmelCase_	:  CLIPTextModel							,      lowerCAmelCase_	:  CLIPTokenizer							,      lowerCAmelCase_	:  UNetaDConditionModel							,      lowerCAmelCase_	:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,      lowerCAmelCase_	:  StableDiffusionSafetyChecker							,      lowerCAmelCase_	:  CLIPImageProcessor							,      )      ->							int:


														"""simple docstring"""


														super().__init__()

														if hasattr(scheduler.config							,      '''steps_offset''' ) and scheduler.config.steps_offset != 1:
																					_a     					=    (
																					    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_ )
																					_a     					=    dict(scheduler.config )
																					_a     					=    1
																					_a     					=    FrozenDict(lowerCAmelCase_ )

														if hasattr(scheduler.config							,      '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
																					_a     					=    (
																					    F'The configuration file of this scheduler: {scheduler} has not set the configuration'
																					    ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
																					    ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead 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('''skip_prk_steps not set'''							,      '''1.0.0'''							,      lowerCAmelCase_							,      standard_warn=lowerCAmelCase_ )
																					_a     					=    dict(scheduler.config )
																					_a     					=    True
																					_a     					=    FrozenDict(lowerCAmelCase_ )

														if safety_checker is None:
																					logger.warning(
																					    F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
																					    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
																					    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
																					    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
																					    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
																					    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )

														self.register_modules(
														    segmentation_model=lowerCAmelCase_							,      segmentation_processor=lowerCAmelCase_							,      vae=lowerCAmelCase_							,      text_encoder=lowerCAmelCase_							,      tokenizer=lowerCAmelCase_							,      unet=lowerCAmelCase_							,      scheduler=lowerCAmelCase_							,      safety_checker=lowerCAmelCase_							,      feature_extractor=lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Optional[Union[str, int]] = "auto" )      ->							Any:


														"""simple docstring"""


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







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


														"""simple docstring"""


														self.enable_attention_slicing(lowerCAmelCase_ )







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


														"""simple docstring"""


														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError('''Please install accelerate via `pip install accelerate`''' )

														_a     					=    torch.device('''cuda''' )

														for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowerCAmelCase_							,      lowerCAmelCase_ )







							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""simple docstring"""


														if self.device != torch.device('''meta''' ) or not hasattr(self.unet							,      '''_hf_hook''' ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(lowerCAmelCase_							,      '''_hf_hook''' )
																					    and hasattr(module._hf_hook							,      '''execution_device''' )
																					    and module._hf_hook.execution_device is not None
																					):
																												return torch.device(module._hf_hook.execution_device )
														return self.device







							@torch.no_grad()
							def __call__(  self	:  List[Any]							,      lowerCAmelCase_	:  Union[str, List[str]]							,      lowerCAmelCase_	:  Union[torch.FloatTensor, PIL.Image.Image]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  int = 5_12							,      lowerCAmelCase_	:  int = 5_12							,      lowerCAmelCase_	:  int = 50							,      lowerCAmelCase_	:  float = 7.5							,      lowerCAmelCase_	:  Optional[Union[str, List[str]]] = None							,      lowerCAmelCase_	:  Optional[int] = 1							,      lowerCAmelCase_	:  float = 0.0							,      lowerCAmelCase_	:  Optional[torch.Generator] = None							,      lowerCAmelCase_	:  Optional[torch.FloatTensor] = None							,      lowerCAmelCase_	:  Optional[str] = "pil"							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,      lowerCAmelCase_	:  int = 1							,      **lowerCAmelCase_	:  Any							,      )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.segmentation_processor(
														    text=[text]							,      images=[image]							,      padding='''max_length'''							,      return_tensors='''pt''' ).to(self.device )
														_a     					=    self.segmentation_model(**lowerCAmelCase_ )
														_a     					=    torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
														_a     					=    self.numpy_to_pil(lowerCAmelCase_ )[0].resize(image.size )

														# Run inpainting pipeline with the generated mask
														_a     					=    StableDiffusionInpaintPipeline(
														    vae=self.vae							,      text_encoder=self.text_encoder							,      tokenizer=self.tokenizer							,      unet=self.unet							,      scheduler=self.scheduler							,      safety_checker=self.safety_checker							,      feature_extractor=self.feature_extractor							,      )
														return inpainting_pipeline(
														    prompt=lowerCAmelCase_							,      image=lowerCAmelCase_							,      mask_image=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_							,      )

