
'''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:  Union[str, Any]								=			logging.get_logger(__name__)  # pylint: disable=invalid-name


class  _UpperCAmelCase ( UpperCamelCase_    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__()

       if hasattr(scheduler.config							,		"""steps_offset""" ) and scheduler.config.steps_offset != 1:
         a_      :	Optional[int]      =							(
             f'''The configuration file of this scheduler: {scheduler} is outdated.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      =							dict(scheduler.config )
         a_      :	Union[str, Any]      =							1
         a_      :	int      =							FrozenDict(lowerCAmelCase_ )

       if hasattr(scheduler.config							,		"""skip_prk_steps""" ) and scheduler.config.skip_prk_steps is False:
         a_      :	Any      =							(
             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_      :	str      =							dict(scheduler.config )
         a_      :	Union[str, Any]      =							True
         a_      :	str      =							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							,		lowerCAmelCase_ = "auto" ):

       '''simple docstring'''

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


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       self.enable_attention_slicing(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

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

       a_      :	List[str]      =							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 ):

       '''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							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = 5_12							,		lowerCAmelCase_ = 5_12							,		lowerCAmelCase_ = 50							,		lowerCAmelCase_ = 7.5							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 1							,		lowerCAmelCase_ = 0.0							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = "pil"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							self.segmentation_processor(
           text=[text]							,		images=[image]							,		padding="""max_length"""							,		return_tensors="""pt""" ).to(self.device )
       a_      :	Optional[Any]      =							self.segmentation_model(**lowerCAmelCase_ )
       a_      :	int      =							torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
       a_      :	Optional[Any]      =							self.numpy_to_pil(lowerCAmelCase_ )[0].resize(image.size )

       # Run inpainting pipeline with the generated mask
       a_      :	Any      =							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_							,		)


'''simple docstring'''

from typing import Mapping

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


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


