




"""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


lowercase__  		=		logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			def __init__(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   lowercase						,   ):
							super().__init__()

							if hasattr(scheduler.config						,   'steps_offset'						) and scheduler.config.steps_offset != 1:
											_lowerCamelCase  :    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											    F'''The configuration file of this scheduler: {scheduler} is outdated.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'						,   lowercase						,   standard_warn=lowercase						)
											_lowerCamelCase  :    Dict					=					dict(scheduler.config						)
											_lowerCamelCase  :    Optional[Any]					=					1
											_lowerCamelCase  :    List[str]					=					FrozenDict(lowercase						)

							if hasattr(scheduler.config						,   'skip_prk_steps'						) and scheduler.config.skip_prk_steps is False:
											_lowerCamelCase  :    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'						,   lowercase						,   standard_warn=lowercase						)
											_lowerCamelCase  :    List[str]					=					dict(scheduler.config						)
											_lowerCamelCase  :    Tuple					=					True
											_lowerCamelCase  :    List[str]					=					FrozenDict(lowercase						)

							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=lowercase						,   segmentation_processor=lowercase						,   vae=lowercase						,   text_encoder=lowercase						,   tokenizer=lowercase						,   unet=lowercase						,   scheduler=lowercase						,   safety_checker=lowercase						,   feature_extractor=lowercase						,   )
			def       A_					(						self						,   lowercase = "auto"						):
							if slice_size == "auto":
											# half the attention head size is usually a good trade-off between
											# speed and memory
											_lowerCamelCase  :    Optional[int]					=					self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(lowercase						)
			def       A_					(						self						):
							self.enable_attention_slicing(lowercase						)
			def       A_					(						self						):
							if is_accelerate_available():
											from accelerate import cpu_offload
							else:
											raise ImportError('Please install accelerate via `pip install accelerate`'						)

							_lowerCamelCase  :    Optional[Any]					=					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(lowercase						,   lowercase						)
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def       A_					(						self						):
							if self.device != torch.device('meta'						) or not hasattr(self.unet						,   '_hf_hook'						):
											return self.device
							for module in self.unet.modules():
											if (
											    hasattr(lowercase						,   '_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						,   lowercase						,   lowercase						,   lowercase						,   lowercase = 512						,   lowercase = 512						,   lowercase = 50						,   lowercase = 7.5						,   lowercase = None						,   lowercase = 1						,   lowercase = 0.0						,   lowercase = None						,   lowercase = None						,   lowercase = "pil"						,   lowercase = True						,   lowercase = None						,   lowercase = 1						,   **lowercase						,   ):
							_lowerCamelCase  :    List[str]					=					self.segmentation_processor(
							    text=[text]						,   images=[image]						,   padding='max_length'						,   return_tensors='pt'						).to(self.device						)
							_lowerCamelCase  :    Dict					=					self.segmentation_model(**lowercase						)
							_lowerCamelCase  :    Dict					=					torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
							_lowerCamelCase  :    int					=					self.numpy_to_pil(lowercase						)[0].resize(image.size						)

							# Run inpainting pipeline with the generated mask
							_lowerCamelCase  :    Union[str, 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=lowercase						,   image=lowercase						,   mask_image=lowercase						,   height=lowercase						,   width=lowercase						,   num_inference_steps=lowercase						,   guidance_scale=lowercase						,   negative_prompt=lowercase						,   num_images_per_prompt=lowercase						,   eta=lowercase						,   generator=lowercase						,   latents=lowercase						,   output_type=lowercase						,   return_dict=lowercase						,   callback=lowercase						,   callback_steps=lowercase						,   )





"""simple docstring"""






from typing import Mapping

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """umt5"""
			lowerCamelCase__   =    ["""past_key_values"""]
			def __init__(						self						,   lowercase=250112						,   lowercase=512						,   lowercase=64						,   lowercase=1024						,   lowercase=8						,   lowercase=None						,   lowercase=6						,   lowercase=32						,   lowercase=128						,   lowercase=0.1						,   lowercase=1E-6						,   lowercase=1.0						,   lowercase="gated-gelu"						,   lowercase=True						,   lowercase=True						,   lowercase="T5Tokenizer"						,   lowercase=True						,   lowercase=0						,   lowercase=1						,   lowercase=0						,   **lowercase						,   ):
							super().__init__(
							    is_encoder_decoder=lowercase						,   tokenizer_class=lowercase						,   tie_word_embeddings=lowercase						,   pad_token_id=lowercase						,   eos_token_id=lowercase						,   decoder_start_token_id=lowercase						,   **lowercase						,   )
							_lowerCamelCase  :    Union[str, Any]					=					vocab_size
							_lowerCamelCase  :    Optional[Any]					=					d_model
							_lowerCamelCase  :    Optional[Any]					=					d_kv
							_lowerCamelCase  :    Optional[int]					=					d_ff
							_lowerCamelCase  :    List[str]					=					num_layers
							_lowerCamelCase  :    Union[str, Any]					=					(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							_lowerCamelCase  :    List[str]					=					num_heads
							_lowerCamelCase  :    int					=					relative_attention_num_buckets
							_lowerCamelCase  :    Union[str, Any]					=					relative_attention_max_distance
							_lowerCamelCase  :    int					=					dropout_rate
							_lowerCamelCase  :    List[Any]					=					layer_norm_epsilon
							_lowerCamelCase  :    Union[str, Any]					=					initializer_factor
							_lowerCamelCase  :    List[Any]					=					feed_forward_proj
							_lowerCamelCase  :    Dict					=					use_cache

							_lowerCamelCase  :    Dict					=					self.feed_forward_proj.split('-'						)
							_lowerCamelCase  :    List[str]					=					act_info[-1]
							_lowerCamelCase  :    int					=					act_info[0] == 'gated'

							if len(lowercase						) > 1 and act_info[0] != "gated" or len(lowercase						) > 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\''						)

							if feed_forward_proj == "gated-gelu":
											_lowerCamelCase  :    Optional[int]					=					'gelu_new'
			@property
			def       A_					(						self						):
							return self.d_model
			@property
			def       A_					(						self						):
							return self.num_heads






			@property
			def       A_					(						self						):
							return self.num_layers






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
			def       A_					(						self						):
							_lowerCamelCase  :    Tuple					=					{
							    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
							    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
							}
							if self.use_past:
											_lowerCamelCase  :    Optional[int]					=					'past_encoder_sequence + sequence'
											_lowerCamelCase  :    int					=					{0: 'batch'}
											_lowerCamelCase  :    List[str]					=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
											_lowerCamelCase  :    Dict					=					{0: 'batch', 1: 'decoder_sequence'}
											_lowerCamelCase  :    str					=					{0: 'batch', 1: 'decoder_sequence'}

							if self.use_past:
											self.fill_with_past_key_values_(lowercase						,   direction='inputs'						)

							return common_inputs
			@property
			# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
			def       A_					(						self						):
							return 13






			@property
			def       A_					(						self						):
							return 5E-4