

"""simple docstring"""




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

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


if TYPE_CHECKING:
						from ... import FeatureExtractionMixin, TensorType

A_							:			int											= logging.get_logger(__name__)

A_							:			List[Any]											= {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		Union[str, Any]							=							'imagegpt'
			lowerCamelCase__   :		Tuple							=							['past_key_values']
			lowerCamelCase__   :		Optional[int]							=							{
			    'hidden_size': 'n_embd',
			    'max_position_embeddings': 'n_positions',
			    'num_attention_heads': 'n_head',
			    'num_hidden_layers': 'n_layer',
			}

			def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]=5_1_2 + 1    ,				__UpperCAmelCase				:						int=3_2 * 3_2    ,				__UpperCAmelCase				:						List[Any]=5_1_2    ,				__UpperCAmelCase				:						Any=2_4    ,				__UpperCAmelCase				:						List[Any]=8    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						int="quick_gelu"    ,				__UpperCAmelCase				:						str=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=0.1    ,				__UpperCAmelCase				:						List[str]=1e-5    ,				__UpperCAmelCase				:						Optional[Any]=0.02    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						str=True    ,				__UpperCAmelCase				:						Tuple=False    ,				__UpperCAmelCase				:						List[Any]=False    ,				__UpperCAmelCase				:						str=False    ,				**__UpperCAmelCase				:						Any    ,				)   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      n_positions
						SCREAMING_SNAKE_CASE__								=      n_embd
						SCREAMING_SNAKE_CASE__								=      n_layer
						SCREAMING_SNAKE_CASE__								=      n_head
						SCREAMING_SNAKE_CASE__								=      n_inner
						SCREAMING_SNAKE_CASE__								=      activation_function
						SCREAMING_SNAKE_CASE__								=      resid_pdrop
						SCREAMING_SNAKE_CASE__								=      embd_pdrop
						SCREAMING_SNAKE_CASE__								=      attn_pdrop
						SCREAMING_SNAKE_CASE__								=      layer_norm_epsilon
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scale_attn_weights
						SCREAMING_SNAKE_CASE__								=      use_cache
						SCREAMING_SNAKE_CASE__								=      scale_attn_by_inverse_layer_idx
						SCREAMING_SNAKE_CASE__								=      reorder_and_upcast_attn
						SCREAMING_SNAKE_CASE__								=      tie_word_embeddings

						super().__init__(tie_word_embeddings=__UpperCAmelCase    ,				**__UpperCAmelCase       )





class    lowerCamelCase						(A__			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
						    ]       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						"FeatureExtractionMixin"    ,				__UpperCAmelCase				:						int = 1    ,				__UpperCAmelCase				:						int = -1    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						Optional["TensorType"] = None    ,				__UpperCAmelCase				:						int = 3    ,				__UpperCAmelCase				:						int = 3_2    ,				__UpperCAmelCase				:						int = 3_2    ,				)   ->    Mapping[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self._generate_dummy_images(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      dict(preprocessor(images=__UpperCAmelCase    ,				return_tensors=__UpperCAmelCase       )       )

						return inputs





"""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


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

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

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

						SCREAMING_SNAKE_CASE__								=      [np.array(i.resize((w, h)		,				resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      np.array(snake_case__    ).astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      image.transpose(0		,				3		,				1		,				2    )
						SCREAMING_SNAKE_CASE__								=      2.0 * image - 1.0
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(image[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return image

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if isinstance(snake_case__		,				torch.Tensor    ):
						return mask
			elif isinstance(snake_case__		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__								=      [mask]

			if isinstance(mask[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      mask[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						SCREAMING_SNAKE_CASE__								=      [np.array(m.convert("""L"""    ).resize((w, h)		,				resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      mask.astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      0
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(mask[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return mask





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		UNetaDModel
			lowerCamelCase__   :		RePaintScheduler

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Dict       )   ->    Tuple:
						super().__init__()
						self.register_modules(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )





			@torch.no_grad()
			def __call__(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						int = 2_5_0    ,				__UpperCAmelCase				:						float = 0.0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				__UpperCAmelCase				:						Optional[str] = "pil"    ,				__UpperCAmelCase				:						bool = True    ,				)   ->    Union[ImagePipelineOutput, Tuple]:
						SCREAMING_SNAKE_CASE__								=      image

						SCREAMING_SNAKE_CASE__								=      _preprocess_image(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      original_image.to(device=self.device    ,				dtype=self.unet.dtype       )
						SCREAMING_SNAKE_CASE__								=      _preprocess_mask(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      mask_image.to(device=self.device    ,				dtype=self.unet.dtype       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape[0]

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

						SCREAMING_SNAKE_CASE__								=      original_image.shape
						SCREAMING_SNAKE_CASE__								=      randn_tensor(__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				device=self.device    ,				dtype=self.unet.dtype       )

						# set step values
						self.scheduler.set_timesteps(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.device       )
						SCREAMING_SNAKE_CASE__								=      eta

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

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

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__UpperCAmelCase       )


