

"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a       =							logging.get_logger(__name__)

_a       =							{
    'google/realm-cc-news-pretrained-embedder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-encoder': (
        'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-scorer': (
        'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'
    ),
    'google/realm-cc-news-pretrained-openqa': (
        'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'
    ),
    'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json',
    'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json',
    'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json',
    'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json',
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Any   		=	"realm"


 def __init__(			self							:			List[str],					UpperCAmelCase__							:			Union[str, Any]=3_0_5_2_2,					UpperCAmelCase__							:			List[Any]=7_6_8,					UpperCAmelCase__							:			Any=1_2_8,					UpperCAmelCase__							:			List[str]=1_2,					UpperCAmelCase__							:			Tuple=1_2,					UpperCAmelCase__							:			Dict=8,					UpperCAmelCase__							:			Any=3_0_7_2,					UpperCAmelCase__							:			List[Any]="gelu_new",					UpperCAmelCase__							:			str=0.1,					UpperCAmelCase__							:			List[Any]=0.1,					UpperCAmelCase__							:			List[Any]=5_1_2,					UpperCAmelCase__							:			Optional[int]=2,					UpperCAmelCase__							:			Optional[int]=0.02,					UpperCAmelCase__							:			Union[str, Any]=1E-12,					UpperCAmelCase__							:			Dict=2_5_6,					UpperCAmelCase__							:			Optional[int]=1_0,					UpperCAmelCase__							:			Any=1E-3,					UpperCAmelCase__							:			List[str]=5,					UpperCAmelCase__							:			List[Any]=3_2_0,					UpperCAmelCase__							:			Optional[int]=1_3_3_5_3_7_1_8,					UpperCAmelCase__							:			Optional[Any]=5_0_0_0,					UpperCAmelCase__							:			Optional[Any]=1,					UpperCAmelCase__							:			int=0,					UpperCAmelCase__							:			Dict=2,					**UpperCAmelCase__							:			List[str],					):
    super().__init__(pad_token_id=UpperCAmelCase__,					bos_token_id=UpperCAmelCase__,					eos_token_id=UpperCAmelCase__,					**UpperCAmelCase__	)

    # Common config
    __lowercase								=						vocab_size
    __lowercase								=						max_position_embeddings
    __lowercase								=						hidden_size
    __lowercase								=						retriever_proj_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						num_candidates
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						initializer_range
    __lowercase								=						type_vocab_size
    __lowercase								=						layer_norm_eps

    # Reader config
    __lowercase								=						span_hidden_size
    __lowercase								=						max_span_width
    __lowercase								=						reader_layer_norm_eps
    __lowercase								=						reader_beam_size
    __lowercase								=						reader_seq_len

    # Retrieval config
    __lowercase								=						num_block_records
    __lowercase								=						searcher_beam_size







"""simple docstring"""





from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_a       =							'CompVis/stable-diffusion-v1-1'
_a       =							'CompVis/stable-diffusion-v1-2'
_a       =							'CompVis/stable-diffusion-v1-3'
_a       =							'CompVis/stable-diffusion-v1-4'




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 def __init__(			self							:			Dict,					UpperCAmelCase__							:			AutoencoderKL,					UpperCAmelCase__							:			CLIPTextModel,					UpperCAmelCase__							:			CLIPTokenizer,					UpperCAmelCase__							:			UNetaDConditionModel,					UpperCAmelCase__							:			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],					UpperCAmelCase__							:			StableDiffusionSafetyChecker,					UpperCAmelCase__							:			CLIPImageProcessor,					UpperCAmelCase__							:			bool = True,					):
    super()._init_()

    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline(
        vae=UpperCAmelCase__,					text_encoder=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					unet=UpperCAmelCase__,					scheduler=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					feature_extractor=UpperCAmelCase__,					requires_safety_checker=UpperCAmelCase__,					)

    self.register_modules(pipelinea=self.pipea,					pipelinea=self.pipea,					pipelinea=self.pipea,					pipelinea=self.pipea	)


 @property
 def 					_lowercase (			self							:			List[str]	):
    return {k: getattr(self,					UpperCAmelCase__	) for k in self.config.keys() if not k.startswith("_"	)}


 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Union[str, int]] = "auto"	):
    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(UpperCAmelCase__	)


 def 					_lowercase (			self							:			List[str]	):
    self.enable_attention_slicing(UpperCAmelCase__	)


 @torch.no_grad()
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Tuple,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			str,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Any,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Optional[int],					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)



 @torch.no_grad()
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			str,					):
    __lowercase								=						"cuda" if torch.cuda.is_available() else "cpu"
    self.to(UpperCAmelCase__	)

    # Checks if the height and width are divisible by 8 or not
    if height % 8 != 0 or width % 8 != 0:
       raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}."""	)

    # Get first result from Stable Diffusion Checkpoint v1.1
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get first result from Stable Diffusion Checkpoint v1.2
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get first result from Stable Diffusion Checkpoint v1.3
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get first result from Stable Diffusion Checkpoint v1.4
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

    # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
    return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]	)




