





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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





class 					A_					(   UpperCAmelCase						):







       """simple docstring"""





       def __init__(     self						:						str    ,__A						:						AutoencoderKL    ,__A						:						CLIPTextModel    ,__A						:						CLIPTokenizer    ,__A						:						UNetaDConditionModel    ,__A						:						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]    ,__A						:						StableDiffusionSafetyChecker    ,__A						:						CLIPImageProcessor    ,)    ->       Dict:
         super().__init__()
         self.register_modules(
             vae=__A    ,text_encoder=__A    ,tokenizer=__A    ,unet=__A    ,scheduler=__A    ,safety_checker=__A    ,feature_extractor=__A    ,)







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						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
           _lowercase						    =			self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]	)    ->       Dict:
         self.enable_attention_slicing(__A	)







       @torch.no_grad()
       def __call__(     self						:						int    ,__A						:						Union[str, List[str]]    ,__A						:						int = 512    ,__A						:						int = 512    ,__A						:						int = 50    ,__A						:						float = 7.5    ,__A						:						Optional[Union[str, List[str]]] = None    ,__A						:						Optional[int] = 1    ,__A						:						float = 0.0    ,__A						:						Optional[torch.Generator] = None    ,__A						:						Optional[torch.FloatTensor] = None    ,__A						:						Optional[str] = "pil"    ,__A						:						bool = True    ,__A						:						Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,__A						:						int = 1    ,__A						:						Optional[torch.FloatTensor] = None    ,**__A						:						Dict    ,)    ->       Optional[Any]:

         if isinstance(__A    ,__A	):
           _lowercase						    =			1
         elif isinstance(__A    ,__A	):
           _lowercase						    =			len(__A	)
         else:
           raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__A	)}"""	)

         if height % 8 != 0 or width % 8 != 0:
           raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""	)

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(__A    ,__A	) or callback_steps <= 0)
         ):
           raise ValueError(
               F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
               F""" {type(__A	)}."""	)

         # get prompt text embeddings
         _lowercase						    =			self.tokenizer(
             __A    ,padding='max_length'    ,max_length=self.tokenizer.model_max_length    ,return_tensors='pt'    ,)
         _lowercase						    =			text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
           _lowercase						    =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]	)
           logger.warning(
               'The following part of your input was truncated because CLIP can only handle sequences up to'
               F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""	)
           _lowercase						    =			text_input_ids[:, : self.tokenizer.model_max_length]

         if text_embeddings is None:
           _lowercase						    =			self.text_encoder(text_input_ids.to(self.device	)	)[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         _lowercase    ,		_lowercase    ,		_lowercase						    =			text_embeddings.shape
         _lowercase						    =			text_embeddings.repeat(1    ,__A    ,1	)
         _lowercase						    =			text_embeddings.view(bs_embed * num_images_per_prompt    ,__A    ,-1	)

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         _lowercase						    =			guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
           _lowercase						    =			42
           if negative_prompt is None:
             _lowercase						    =			['']
           elif type(__A	) is not type(__A	):
             raise TypeError(
                 F"""`negative_prompt` should be the same type to `prompt`, but got {type(__A	)} !="""
                 F""" {type(__A	)}."""	)
           elif isinstance(__A    ,__A	):
             _lowercase						    =			[negative_prompt]
           elif batch_size != len(__A	):
             raise ValueError(
                 F"""`negative_prompt`: {negative_prompt} has batch size {len(__A	)}, but `prompt`:"""
                 F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                 ' the batch size of `prompt`.'	)
           else:
             _lowercase						    =			negative_prompt

           _lowercase						    =			text_input_ids.shape[-1]
           _lowercase						    =			self.tokenizer(
               __A    ,padding='max_length'    ,max_length=__A    ,truncation=__A    ,return_tensors='pt'    ,)
           _lowercase						    =			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
           _lowercase						    =			uncond_embeddings.shape[1]
           _lowercase						    =			uncond_embeddings.repeat(__A    ,__A    ,1	)
           _lowercase						    =			uncond_embeddings.view(batch_size * num_images_per_prompt    ,__A    ,-1	)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           _lowercase						    =			torch.cat([uncond_embeddings, text_embeddings]	)

         # get the initial random noise unless the user supplied it

         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
         _lowercase						    =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         _lowercase						    =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
         _lowercase						    =			text_embeddings.dtype
         if latents is None:
           if self.device.type == "mps":
             # randn does not exist on mps
             _lowercase						    =			torch.randn(
                 __A    ,generator=__A    ,device='cpu'    ,dtype=__A	).to(self.device	)
             _lowercase						    =			torch.randn(__A    ,generator=__A    ,device='cpu'    ,dtype=__A	).to(
                 self.device	)
           else:
             _lowercase						    =			torch.randn(
                 __A    ,generator=__A    ,device=self.device    ,dtype=__A	)
             _lowercase						    =			torch.randn(__A    ,generator=__A    ,device=self.device    ,dtype=__A	)
         else:
           if latents_reference.shape != latents_shape:
             raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""	)
           _lowercase						    =			latents_reference.to(self.device	)
           _lowercase						    =			latents.to(self.device	)

         # This is the key part of the pipeline where we
         # try to ensure that the generated images w/ the same seed
         # but different sizes actually result in similar images
         _lowercase						    =			(latents_shape[3] - latents_shape_reference[3]) // 2
         _lowercase						    =			(latents_shape[2] - latents_shape_reference[2]) // 2
         _lowercase						    =			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         _lowercase						    =			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         _lowercase						    =			0 if dx < 0 else dx
         _lowercase						    =			0 if dy < 0 else dy
         _lowercase						    =			max(-dx    ,0	)
         _lowercase						    =			max(-dy    ,0	)
         # import pdb
         # pdb.set_trace()
         _lowercase						    =			latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(__A	)

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         _lowercase						    =			self.scheduler.timesteps.to(self.device	)

         # scale the initial noise by the standard deviation required by the scheduler
         _lowercase						    =			latents * self.scheduler.init_noise_sigma

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
         # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
         # and should be between [0, 1]
         _lowercase						    =			'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
         _lowercase						    =			{}
         if accepts_eta:
           _lowercase						    =			eta

         for i, t in enumerate(self.progress_bar(__A	)	):
           # expand the latents if we are doing classifier free guidance
           _lowercase						    =			torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
           _lowercase						    =			self.scheduler.scale_model_input(__A    ,__A	)

           # predict the noise residual
           _lowercase						    =			self.unet(__A    ,__A    ,encoder_hidden_states=__A	).sample

           # perform guidance
           if do_classifier_free_guidance:
             _lowercase    ,		_lowercase						    =			noise_pred.chunk(2	)
             _lowercase						    =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

           # compute the previous noisy sample x_t -> x_t-1
           _lowercase						    =			self.scheduler.step(__A    ,__A    ,__A    ,**__A	).prev_sample

           # call the callback, if provided
           if callback is not None and i % callback_steps == 0:
             callback(__A    ,__A    ,__A	)

         _lowercase						    =			1 / 0.18215 * latents
         _lowercase						    =			self.vae.decode(__A	).sample

         _lowercase						    =			(image / 2 + 0.5).clamp(0    ,1	)

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         _lowercase						    =			image.cpu().permute(0    ,2    ,3    ,1	).float().numpy()

         if self.safety_checker is not None:
           _lowercase						    =			self.feature_extractor(self.numpy_to_pil(__A	)    ,return_tensors='pt'	).to(
               self.device	)
           _lowercase    ,		_lowercase						    =			self.safety_checker(
               images=__A    ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype	)	)
         else:
           _lowercase						    =			None

         if output_type == "pil":
           _lowercase						    =			self.numpy_to_pil(__A	)

         if not return_dict:
           return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=__A    ,nsfw_content_detected=__A	)






'''simple docstring'''




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


__UpperCAmelCase            =       logging.get_logger(__name__)

__UpperCAmelCase            =       {
    """naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''donut-swin'''

					SCREAMING_SNAKE_CASE__				=	{
					    '''num_attention_heads''': '''num_heads''',
					    '''num_hidden_layers''': '''num_layers''',
					}


					def __init__(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=2_24      ,						lowerCamelCase_				:			Union[str, Any]=4      ,						lowerCamelCase_				:			Optional[Any]=3      ,						lowerCamelCase_				:			Dict=96      ,						lowerCamelCase_				:			int=[2, 2, 6, 2]      ,						lowerCamelCase_				:			Optional[Any]=[3, 6, 12, 24]      ,						lowerCamelCase_				:			List[str]=7      ,						lowerCamelCase_				:			List[str]=4.0      ,						lowerCamelCase_				:			Optional[Any]=True      ,						lowerCamelCase_				:			List[str]=0.0      ,						lowerCamelCase_				:			Dict=0.0      ,						lowerCamelCase_				:			Dict=0.1      ,						lowerCamelCase_				:			Union[str, Any]="gelu"      ,						lowerCamelCase_				:			List[str]=False      ,						lowerCamelCase_				:			Optional[int]=0.02      ,						lowerCamelCase_				:			Any=1e-5      ,						**lowerCamelCase_				:			Union[str, Any]      ,						):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				int		=			image_size
							SCREAMING_SNAKE_CASE :				Dict		=			patch_size
							SCREAMING_SNAKE_CASE :				str		=			num_channels
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			embed_dim
							SCREAMING_SNAKE_CASE :				Optional[int]		=			depths
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			len(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			num_heads
							SCREAMING_SNAKE_CASE :				Optional[int]		=			window_size
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			mlp_ratio
							SCREAMING_SNAKE_CASE :				Dict		=			qkv_bias
							SCREAMING_SNAKE_CASE :				Optional[int]		=			hidden_dropout_prob
							SCREAMING_SNAKE_CASE :				Tuple		=			attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE :				Dict		=			drop_path_rate
							SCREAMING_SNAKE_CASE :				List[str]		=			hidden_act
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			use_absolute_embeddings
							SCREAMING_SNAKE_CASE :				Tuple		=			layer_norm_eps
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							SCREAMING_SNAKE_CASE :				str		=			int(embed_dim * 2 ** (len(lowerCamelCase_						) - 1)						)



