



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


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






class       UpperCamelCase							(		_a      ):



	def __init__(  self					,			UpperCAmelCase__					,			UpperCAmelCase__					,			UpperCAmelCase__					,			UpperCAmelCase__					,			UpperCAmelCase__					,			UpperCAmelCase__					,			UpperCAmelCase__					,			):
							super().__init__()
							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_					,			)



	def 					__A				(  self					,			UpperCAmelCase__ = "auto"						):
							if slice_size == "auto":
													# half the attention head size is usually a good trade-off between
													# speed and memory
													A__					=  self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(snake_case_						)



	def 					__A				(  self						):
							self.enable_attention_slicing(snake_case_						)



	@torch.no_grad()
	def __call__(  self					,			UpperCAmelCase__					,			UpperCAmelCase__ = 512					,			UpperCAmelCase__ = 512					,			UpperCAmelCase__ = 50					,			UpperCAmelCase__ = 7.5					,			UpperCAmelCase__ = None					,			UpperCAmelCase__ = 1					,			UpperCAmelCase__ = 0.0					,			UpperCAmelCase__ = None					,			UpperCAmelCase__ = None					,			UpperCAmelCase__ = "pil"					,			UpperCAmelCase__ = True					,			UpperCAmelCase__ = None					,			UpperCAmelCase__ = 1					,			UpperCAmelCase__ = None					,			**UpperCAmelCase__					,			):

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

							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(snake_case_					,			snake_case_						) or callback_steps <= 0)
							):
													raise ValueError(
													    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
													    F""" {type(snake_case_						)}."""						)

							# get prompt text embeddings
							A__					=  self.tokenizer(
							    snake_case_					,			padding="max_length"					,			max_length=self.tokenizer.model_max_length					,			return_tensors="pt"					,			)
							A__					=  text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													A__					=  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}"""						)
													A__					=  text_input_ids[:, : self.tokenizer.model_max_length]

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

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							A__					=  text_embeddings.shape
							A__					=  text_embeddings.repeat(1					,			snake_case_					,			1						)
							A__					=  text_embeddings.view(bs_embed * num_images_per_prompt					,			snake_case_					,			-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.
							A__					=  guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
													A__					=  42
													if negative_prompt is None:
																			A__					=  [""""""]
													elif type(snake_case_						) is not type(snake_case_						):
																			raise TypeError(
																			    F"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_						)} !="""
																			    F""" {type(snake_case_						)}."""						)
													elif isinstance(snake_case_					,			snake_case_						):
																			A__					=  [negative_prompt]
													elif batch_size != len(snake_case_						):
																			raise ValueError(
																			    F"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case_						)}, but `prompt`:"""
																			    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																			    " the batch size of `prompt`."						)
													else:
																			A__					=  negative_prompt

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

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													A__					=  uncond_embeddings.shape[1]
													A__					=  uncond_embeddings.repeat(snake_case_					,			snake_case_					,			1						)
													A__					=  uncond_embeddings.view(batch_size * num_images_per_prompt					,			snake_case_					,			-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
													A__					=  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`.
							A__					=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							A__					=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							A__					=  text_embeddings.dtype
							if latents is None:
													if self.device.type == "mps":
																			# randn does not exist on mps
																			A__					=  torch.randn(
																			    snake_case_					,			generator=snake_case_					,			device="cpu"					,			dtype=snake_case_						).to(self.device						)
																			A__					=  torch.randn(snake_case_					,			generator=snake_case_					,			device="cpu"					,			dtype=snake_case_						).to(
																			    self.device						)
													else:
																			A__					=  torch.randn(
																			    snake_case_					,			generator=snake_case_					,			device=self.device					,			dtype=snake_case_						)
																			A__					=  torch.randn(snake_case_					,			generator=snake_case_					,			device=self.device					,			dtype=snake_case_						)
							else:
													if latents_reference.shape != latents_shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
													A__					=  latents_reference.to(self.device						)
													A__					=  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
							A__					=  (latents_shape[3] - latents_shape_reference[3]) // 2
							A__					=  (latents_shape[2] - latents_shape_reference[2]) // 2
							A__					=  latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							A__					=  latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							A__					=  0 if dx < 0 else dx
							A__					=  0 if dy < 0 else dy
							A__					=  max(-dx					,			0						)
							A__					=  max(-dy					,			0						)
							# import pdb
							# pdb.set_trace()
							A__					=  latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(snake_case_						)

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

							# scale the initial noise by the standard deviation required by the scheduler
							A__					=  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]
							A__					=  """eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
							A__					=  {}
							if accepts_eta:
													A__					=  eta

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

													# predict the noise residual
													A__					=  self.unet(snake_case_					,			snake_case_					,			encoder_hidden_states=snake_case_						).sample

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

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

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

							A__					=  1 / 0.18_215 * latents
							A__					=  self.vae.decode(snake_case_						).sample

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

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

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

							if output_type == "pil":
													A__					=  self.numpy_to_pil(snake_case_						)

							if not return_dict:
													return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=snake_case_					,			nsfw_content_detected=snake_case_						)










'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "shi-labs/dinat-mini-in1k-224": "https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class        UpperCAmelCase_			(			_a						,						_a      ):
    """simple docstring"""







    lowercase   =						"dinat"

    lowercase   =						{
        "num_attention_heads": "num_heads",
        "num_hidden_layers": "num_layers",
    }




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Optional[Any]=4					,	snake_case_   :	List[str]=3					,	snake_case_   :	Tuple=64					,	snake_case_   :	List[Any]=[3, 4, 6, 5]					,	snake_case_   :	List[Any]=[2, 4, 8, 16]					,	snake_case_   :	List[str]=7					,	snake_case_   :	List[str]=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]					,	snake_case_   :	List[Any]=3.0					,	snake_case_   :	Dict=True					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	str=0.1					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Optional[int]=1E-5					,	snake_case_   :	str=0.0					,	snake_case_   :	Dict=None					,	snake_case_   :	Dict=None					,	**snake_case_   :	Dict					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Optional[Any]       		= patch_size
          snake_case__    :       Union[str, Any]       		= num_channels
          snake_case__    :       int       		= embed_dim
          snake_case__    :       Dict       		= depths
          snake_case__    :       Dict       		= len(snake_case_		)
          snake_case__    :       Optional[int]       		= num_heads
          snake_case__    :       Any       		= kernel_size
          snake_case__    :       str       		= dilations
          snake_case__    :       Any       		= mlp_ratio
          snake_case__    :       int       		= qkv_bias
          snake_case__    :       int       		= hidden_dropout_prob
          snake_case__    :       int       		= attention_probs_dropout_prob
          snake_case__    :       Optional[Any]       		= drop_path_rate
          snake_case__    :       int       		= hidden_act
          snake_case__    :       Tuple       		= layer_norm_eps
          snake_case__    :       Any       		= initializer_range
          # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case__    :       Optional[Any]       		= int(embed_dim * 2 ** (len(snake_case_		) - 1)		)
          snake_case__    :       int       		= layer_scale_init_value
          snake_case__    :       Dict       		= ["""stem"""] + [f"stage{idx}" for idx in range(1					,	len(snake_case_		) + 1		)]
          snake_case__		,							snake_case__    :       Tuple       		= get_aligned_output_features_output_indices(
              out_features=snake_case_					,	out_indices=snake_case_					,	stage_names=self.stage_names		)
