






'''simple docstring'''







from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCamelCase     :   Union[str, Any]	      =		transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)
def     A__      (					__lowerCAmelCase		:      Optional[int]    ):
						if isinstance(__lowerCAmelCase ,     torch.Tensor    ):
												return image
						elif isinstance(__lowerCAmelCase ,     PIL.Image.Image    ):
												lowerCamelCase__								=	[image]

						lowerCamelCase__								=	[trans(img.convert("""RGB"""    )    ) for img in image]
						lowerCamelCase__								=	torch.stack(__lowerCAmelCase    )
						return image





class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





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

								# make sure scheduler can always be converted to DDIM
								lowerCamelCase__								=	DDIMScheduler.from_config(scheduler.config							)

								self.register_modules(unet=_lowerCAmelCase ,scheduler=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
								if strength < 0 or strength > 1:
														raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								# get the original timestep using init_timestep
								lowerCamelCase__								=	min(int(num_inference_steps * strength							) ,_lowerCAmelCase							)

								lowerCamelCase__								=	max(num_inference_steps - init_timestep ,0							)
								lowerCamelCase__								=	self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None							):
								if not isinstance(_lowerCAmelCase ,(torch.Tensor, PIL.Image.Image, list)							):
														raise ValueError(
														    F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase							)}'''							)

								lowerCamelCase__								=	image.to(device=_lowerCAmelCase ,dtype=_lowerCAmelCase							)

								if isinstance(_lowerCAmelCase ,_lowerCAmelCase							) and len(_lowerCAmelCase							) != batch_size:
														raise ValueError(
														    F'''You have passed a list of generators of length {len(_lowerCAmelCase							)}, but requested an effective batch'''
														    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''							)

								lowerCamelCase__								=	init_latents.shape
								lowerCamelCase__								=	randn_tensor(_lowerCAmelCase ,generator=_lowerCAmelCase ,device=_lowerCAmelCase ,dtype=_lowerCAmelCase							)

								# get latents
								print("""add noise to latents at timestep""" ,_lowerCAmelCase							)
								lowerCamelCase__								=	self.scheduler.add_noise(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	init_latents

								return latents






		@torch.no_grad()
		def __call__(   self ,_lowerCAmelCase = None ,_lowerCAmelCase = 0.8 ,_lowerCAmelCase = 1 ,_lowerCAmelCase = None ,_lowerCAmelCase = 0.0 ,_lowerCAmelCase = 50 ,_lowerCAmelCase = None ,_lowerCAmelCase = "pil" ,_lowerCAmelCase = True ,):
								self.check_inputs(_lowerCAmelCase							)

								# 2. Preprocess image
								lowerCamelCase__								=	preprocess(_lowerCAmelCase							)

								# 3. set timesteps
								self.scheduler.set_timesteps(_lowerCAmelCase ,device=self.device							)
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_timesteps(_lowerCAmelCase ,_lowerCAmelCase ,self.device							)
								lowerCamelCase__								=	timesteps[:1].repeat(_lowerCAmelCase							)

								# 4. Prepare latent variables
								lowerCamelCase__								=	self.prepare_latents(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,self.unet.dtype ,self.device ,_lowerCAmelCase							)
								lowerCamelCase__								=	latents

								# 5. Denoising loop
								for t in self.progress_bar(_lowerCAmelCase							):
														# 1. predict noise model_output
														lowerCamelCase__								=	self.unet(_lowerCAmelCase ,_lowerCAmelCase							).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														lowerCamelCase__								=	self.scheduler.step(
														    _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,eta=_lowerCAmelCase ,use_clipped_model_output=_lowerCAmelCase ,generator=_lowerCAmelCase ,).prev_sample

								lowerCamelCase__								=	(image / 2 + 0.5).clamp(0 ,1							)
								lowerCamelCase__								=	image.cpu().permute(0 ,2 ,3 ,1							).numpy()
								if output_type == "pil":
														lowerCamelCase__								=	self.numpy_to_pil(_lowerCAmelCase							)

								if not return_dict:
														return (image, latent_timestep.item())

								return ImagePipelineOutput(images=_lowerCAmelCase							)








from typing import Mapping

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


_lowerCAmelCase				:   Optional[int]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   List[str]						       =						{
    '''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        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''umt5'''
     __UpperCamelCase            =  ['''past_key_values''']



     def __init__(				self							:int      ,   snake_case							:Optional[Any]=250_112      ,   snake_case							:Optional[int]=512      ,   snake_case							:Any=64      ,   snake_case							:Union[str, Any]=1_024      ,   snake_case							:Tuple=8      ,   snake_case							:Optional[int]=None      ,   snake_case							:Union[str, Any]=6      ,   snake_case							:List[Any]=32      ,   snake_case							:Dict=128      ,   snake_case							:List[str]=0.1      ,   snake_case							:List[Any]=1e-6      ,   snake_case							:Dict=1.0      ,   snake_case							:Union[str, Any]="gated-gelu"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Any=True      ,   snake_case							:List[str]="T5Tokenizer"      ,   snake_case							:Union[str, Any]=True      ,   snake_case							:Union[str, Any]=0      ,   snake_case							:List[Any]=1      ,   snake_case							:List[Any]=0      ,   **snake_case							:Any      ,   ):





        '''simple docstring'''




        super().__init__(
            is_encoder_decoder=snake_case      ,   tokenizer_class=snake_case      ,   tie_word_embeddings=snake_case      ,   pad_token_id=snake_case      ,   eos_token_id=snake_case      ,   decoder_start_token_id=snake_case      ,   **snake_case      ,   )
        A_       :					Union[str, Any]        =      vocab_size
        A_       :					Tuple        =      d_model
        A_       :					List[str]        =      d_kv
        A_       :					Union[str, Any]        =      d_ff
        A_       :					Any        =      num_layers
        A_       :					Optional[Any]        =      (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        A_       :					List[Any]        =      num_heads
        A_       :					List[str]        =      relative_attention_num_buckets
        A_       :					Dict        =      relative_attention_max_distance
        A_       :					Optional[Any]        =      dropout_rate
        A_       :					Any        =      layer_norm_epsilon
        A_       :					List[Any]        =      initializer_factor
        A_       :					Any        =      feed_forward_proj
        A_       :					Optional[Any]        =      use_cache

        A_       :					int        =      self.feed_forward_proj.split("-"	)
        A_       :					Any        =      act_info[-1]
        A_       :					Tuple        =      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'"	)

        if feed_forward_proj == "gated-gelu":
           A_       :					Optional[Any]        =      "gelu_new"



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.d_model



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:str	):





        '''simple docstring'''




        return self.num_heads





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple	):





        '''simple docstring'''




        return self.num_layers




class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




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

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

        return common_inputs



     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        return 13





     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[str]	):





        '''simple docstring'''




        return 5e-4
