



"""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     =    logging.get_logger(__name__)  # pylint: disable=invalid-name
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      Union[str, Any]						=					["""safety_checker""", """feature_extractor"""]






					def __init__(	self:							List[Any]						,					snake_case:							AutoencoderKL						,					snake_case:							CLIPTextModel						,					snake_case:							CLIPTokenizer						,					snake_case:							UNetaDConditionModel						,					snake_case:							Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]						,					snake_case:							StableDiffusionSafetyChecker						,					snake_case:							CLIPImageProcessor						,					snake_case:							bool = True						,					)       ->     List[str]:
											super().__init__()
											if hasattr(scheduler.config						,					"""steps_offset"""						) and scheduler.config.steps_offset != 1:
																	snake_case_			:List[str]						=      (
																	    f"""The configuration file of this scheduler: {scheduler} is outdated.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"""						,					snake_case						,					standard_warn=snake_case						)
																	snake_case_			:Tuple						=      dict(scheduler.config						)
																	snake_case_			:Union[str, Any]						=      1
																	snake_case_			:Dict						=      FrozenDict(snake_case						)

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






					@property
					def        lowerCAmelCase_    (	self:							str						)       ->     Dict[str, Any]:
											return {k: getattr(self						,					snake_case						) for k in self.config.keys() if not k.startswith("""_"""						)}






					def        lowerCAmelCase_    (	self:							Optional[Any]						,					snake_case:							Optional[Union[str, int]] = "auto"						)       ->     Union[str, Any]:
											if slice_size == "auto":
																	# half the attention head size is usually a good trade-off between
																	# speed and memory
																	snake_case_			:str						=      self.unet.config.attention_head_dim // 2
											self.unet.set_attention_slice(snake_case						)






					def        lowerCAmelCase_    (	self:							int						)       ->     Any:
											self.enable_attention_slicing(snake_case						)






					@torch.no_grad()
					def        lowerCAmelCase_    (	self:							Optional[int]						,					snake_case:							Union[str, List[str]]						,					snake_case:							Union[torch.FloatTensor, PIL.Image.Image]						,					snake_case:							Union[torch.FloatTensor, PIL.Image.Image]						,					snake_case:							float = 0.8						,					snake_case:							Optional[int] = 50						,					snake_case:							Optional[float] = 7.5						,					snake_case:							Optional[Union[str, List[str]]] = None						,					snake_case:							Optional[int] = 1						,					snake_case:							Optional[float] = 0.0						,					snake_case:							Optional[torch.Generator] = None						,					snake_case:							Optional[str] = "pil"						,					snake_case:							bool = True						,					snake_case:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,					snake_case:							int = 1						,					)       ->     Optional[Any]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionInpaintPipelineLegacy(**self.components						)(
											    prompt=snake_case						,					image=snake_case						,					mask_image=snake_case						,					strength=snake_case						,					num_inference_steps=snake_case						,					guidance_scale=snake_case						,					negative_prompt=snake_case						,					num_images_per_prompt=snake_case						,					eta=snake_case						,					generator=snake_case						,					output_type=snake_case						,					return_dict=snake_case						,					callback=snake_case						,					)






					@torch.no_grad()
					def        lowerCAmelCase_    (	self:							int						,					snake_case:							Union[str, List[str]]						,					snake_case:							Union[torch.FloatTensor, PIL.Image.Image]						,					snake_case:							float = 0.8						,					snake_case:							Optional[int] = 50						,					snake_case:							Optional[float] = 7.5						,					snake_case:							Optional[Union[str, List[str]]] = None						,					snake_case:							Optional[int] = 1						,					snake_case:							Optional[float] = 0.0						,					snake_case:							Optional[torch.Generator] = None						,					snake_case:							Optional[str] = "pil"						,					snake_case:							bool = True						,					snake_case:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,					snake_case:							int = 1						,					**snake_case:							Dict						,					)       ->     List[str]:
											# For more information on how this function works, please see: https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
											return StableDiffusionImgaImgPipeline(**self.components						)(
											    prompt=snake_case						,					image=snake_case						,					strength=snake_case						,					num_inference_steps=snake_case						,					guidance_scale=snake_case						,					negative_prompt=snake_case						,					num_images_per_prompt=snake_case						,					eta=snake_case						,					generator=snake_case						,					output_type=snake_case						,					return_dict=snake_case						,					callback=snake_case						,					callback_steps=snake_case						,					)






					@torch.no_grad()
					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Union[str, List[str]]						,					snake_case:							int = 512						,					snake_case:							int = 512						,					snake_case:							int = 50						,					snake_case:							float = 7.5						,					snake_case:							Optional[Union[str, List[str]]] = None						,					snake_case:							Optional[int] = 1						,					snake_case:							float = 0.0						,					snake_case:							Optional[torch.Generator] = None						,					snake_case:							Optional[torch.FloatTensor] = None						,					snake_case:							Optional[str] = "pil"						,					snake_case:							bool = True						,					snake_case:							Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,					snake_case:							int = 1						,					)       ->     Any:
											# For more information on how this function https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
											return StableDiffusionPipeline(**self.components						)(
											    prompt=snake_case						,					height=snake_case						,					width=snake_case						,					num_inference_steps=snake_case						,					guidance_scale=snake_case						,					negative_prompt=snake_case						,					num_images_per_prompt=snake_case						,					eta=snake_case						,					generator=snake_case						,					latents=snake_case						,					output_type=snake_case						,					return_dict=snake_case						,					callback=snake_case						,					callback_steps=snake_case						,					)





"""simple docstring"""
from typing import Mapping

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


__a     =    logging.get_logger(__name__)

__a     =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[Any]						=					"""longt5"""
					_A		:      Dict						=					["""past_key_values"""]
					_A		:      int						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






					def __init__(	self:							str						,					snake_case:							List[Any]=32_128						,					snake_case:							Optional[int]=512						,					snake_case:							Union[str, Any]=64						,					snake_case:							str=2_048						,					snake_case:							Any=6						,					snake_case:							Optional[Any]=None						,					snake_case:							str=8						,					snake_case:							Tuple=127						,					snake_case:							Any=16						,					snake_case:							int=32						,					snake_case:							int=128						,					snake_case:							int=0.1						,					snake_case:							Optional[int]=1E-6						,					snake_case:							List[Any]=1.0						,					snake_case:							Optional[int]="relu"						,					snake_case:							str=True						,					snake_case:							Optional[int]="local"						,					snake_case:							Any=True						,					snake_case:							Tuple=0						,					snake_case:							Any=1						,					**snake_case:							Any						,					)       ->     List[str]:
											snake_case_			:Any						=      vocab_size
											snake_case_			:List[Any]						=      d_model
											snake_case_			:Any						=      d_kv
											snake_case_			:int						=      d_ff
											snake_case_			:Tuple						=      num_layers
											# default = symmetry
											snake_case_			:str						=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											snake_case_			:Any						=      num_heads
											snake_case_			:str						=      local_radius
											snake_case_			:Optional[int]						=      global_block_size
											snake_case_			:Union[str, Any]						=      relative_attention_num_buckets
											snake_case_			:List[str]						=      relative_attention_max_distance
											snake_case_			:Dict						=      dropout_rate
											snake_case_			:str						=      layer_norm_epsilon
											snake_case_			:str						=      initializer_factor
											snake_case_			:Any						=      feed_forward_proj
											snake_case_			:List[str]						=      encoder_attention_type
											snake_case_			:List[str]						=      use_cache

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

											if len(snake_case						) > 1 and act_info[0] != "gated" or len(snake_case						) > 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":
																	snake_case_			:Optional[int]						=      """gelu_new"""

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

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							Dict						)       ->     Mapping[str, Mapping[int, str]]:
											snake_case_			:Dict						=      {
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																	snake_case_			:List[Any]						=      """past_encoder_sequence + sequence"""
																	snake_case_			:List[Any]						=      {0: """batch"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																	snake_case_			:List[str]						=      {0: """batch""", 1: """decoder_sequence"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """decoder_sequence"""}

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

											return common_inputs






					@property
					def        lowerCAmelCase_    (	self:							List[str]						)       ->     int:
											return 13
