





'''simple docstring'''

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase				:      List[Any]						      =logging.get_logger(__name__)  # pylint: disable=invalid-name





def 		UpperCamelCase							(				_lowerCamelCase   :   Union[List, PIL.Image.Image, torch.Tensor]			):
    warnings.warn(
        "The preprocess method is deprecated and will be removed in a future version. Please"
        " use VaeImageProcessor.preprocess instead"		,			_lowerCamelCase		,			)
    if isinstance(_lowerCamelCase		,			torch.Tensor			):
        return image
    elif isinstance(_lowerCamelCase		,			PIL.Image.Image			):
        A__							=     [image]

    if isinstance(image[0]		,			PIL.Image.Image			):
        A__,      A__							=     image[0].size
        A__,      A__							=     (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        A__							=     [np.array(i.resize((w, h)		,			resample=PIL_INTERPOLATION["lanczos"]			)			)[None, :] for i in image]
        A__							=     np.concatenate(_lowerCamelCase		,			axis=0			)
        A__							=     np.array(_lowerCamelCase			).astype(np.floataa			) / 2_5_5.0
        A__							=     image.transpose(0		,			3		,			1		,			2			)
        A__							=     2.0 * image - 1.0
        A__							=     torch.from_numpy(_lowerCamelCase			)
    elif isinstance(image[0]		,			torch.Tensor			):
        A__							=     torch.cat(_lowerCamelCase		,			dim=0			)
    return image





def 		UpperCamelCase							(				_lowerCamelCase   :   Union[List, PIL.Image.Image, torch.Tensor]			):
    if isinstance(_lowerCamelCase		,			torch.Tensor			):
        return mask
    elif isinstance(_lowerCamelCase		,			PIL.Image.Image			):
        A__							=     [mask]

    if isinstance(mask[0]		,			PIL.Image.Image			):
        A__,      A__							=     mask[0].size
        A__,      A__							=     (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
        A__							=     [np.array(m.convert("L"			).resize((w, h)		,			resample=PIL_INTERPOLATION["nearest"]			)			)[None, :] for m in mask]
        A__							=     np.concatenate(_lowerCamelCase		,			axis=0			)
        A__							=     mask.astype(np.floataa			) / 2_5_5.0
        A__							=     0
        A__							=     1
        A__							=     torch.from_numpy(_lowerCamelCase			)
    elif isinstance(mask[0]		,			torch.Tensor			):
        A__							=     torch.cat(_lowerCamelCase		,			dim=0			)
    return mask






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							42
 __lowercase  						=							42
 def __init__(							self :Dict		,			lowercase_ :str		,			lowercase_ :Optional[Any]				)->				Optional[Any]:
     super().__init__()
     self.register_modules(unet=lowercase_		,			scheduler=lowercase_				)



 @torch.no_grad()
 def __call__(							self :List[str]		,			lowercase_ :Union[torch.Tensor, PIL.Image.Image]		,			lowercase_ :Union[torch.Tensor, PIL.Image.Image]		,			lowercase_ :int = 2_50		,			lowercase_ :float = 0.0		,			lowercase_ :int = 10		,			lowercase_ :int = 10		,			lowercase_ :Optional[Union[torch.Generator, List[torch.Generator]]] = None		,			lowercase_ :Optional[str] = "pil"		,			lowercase_ :bool = True		,			)->				Union[ImagePipelineOutput, Tuple]:
     A__							=     image

     A__							=     _preprocess_image(lowercase_				)
     A__							=     original_image.to(device=self.device		,			dtype=self.unet.dtype				)
     A__							=     _preprocess_mask(lowercase_				)
     A__							=     mask_image.to(device=self.device		,			dtype=self.unet.dtype				)

     A__							=     original_image.shape[0]

     # sample gaussian noise to begin the loop
     if isinstance(lowercase_		,			lowercase_				) and len(lowercase_				) != batch_size:
         raise ValueError(
             F"You have passed a list of generators of length {len(lowercase_				)}, but requested an effective batch"
             F" size of {batch_size}. Make sure the batch size matches the length of the generators."				)

     A__							=     original_image.shape
     A__							=     randn_tensor(lowercase_		,			generator=lowercase_		,			device=self.device		,			dtype=self.unet.dtype				)

     # set step values
     self.scheduler.set_timesteps(lowercase_		,			lowercase_		,			lowercase_		,			self.device				)
     A__							=     eta

     A__							=     self.scheduler.timesteps[0] + 1
     A__							=     generator[0] if isinstance(lowercase_		,			lowercase_				) else generator
     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
         if t < t_last:
             # predict the noise residual
             A__							=     self.unet(lowercase_		,			lowercase_				).sample
             # compute previous image: x_t -> x_t-1
             A__							=     self.scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				).prev_sample

         else:
             # compute the reverse: x_t-1 -> x_t
             A__							=     self.scheduler.undo_step(lowercase_		,			lowercase_		,			lowercase_				)
         A__							=     t

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

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=lowercase_				)










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
         A__							=     {0: "batch", 1: "choice", 2: "sequence"}
     else:
         A__							=     {0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
         ]				)



