






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


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







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



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



          '''simple docstring'''
          super().__init__()
          self.register_modules(
              vae=_lowerCAmelCase    ,	text_encoder=_lowerCAmelCase    ,	tokenizer=_lowerCAmelCase    ,	unet=_lowerCAmelCase    ,	scheduler=_lowerCAmelCase    ,	safety_checker=_lowerCAmelCase    ,	feature_extractor=_lowerCAmelCase    ,	)
      def 			snake_case_      (   self    ,	_lowerCAmelCase = "auto"		):



          '''simple docstring'''
          if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              lowerCAmelCase__   :Union[str, Any]      							=				self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(_lowerCAmelCase		)
      def 			snake_case_      (   self		):



          '''simple docstring'''
          self.enable_attention_slicing(_lowerCAmelCase		)



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



          '''simple docstring'''

          if isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		):
              lowerCAmelCase__   :Optional[int]      							=				1
          elif isinstance(_lowerCAmelCase    ,	_lowerCAmelCase		):
              lowerCAmelCase__   :Dict      							=				len(_lowerCAmelCase		)
          else:
              raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_lowerCAmelCase		)}'''		)

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

          # get prompt text embeddings
          lowerCAmelCase__   :str      							=				self.tokenizer(
              _lowerCAmelCase    ,	padding="max_length"    ,	max_length=self.tokenizer.model_max_length    ,	return_tensors="pt"    ,	)
          lowerCAmelCase__   :Dict      							=				text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
              lowerCAmelCase__   :Optional[int]      							=				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}'''		)
              lowerCAmelCase__   :Optional[Any]      							=				text_input_ids[:, : self.tokenizer.model_max_length]

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

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

              lowerCAmelCase__   :Optional[int]      							=				text_input_ids.shape[-1]
              lowerCAmelCase__   :Union[str, Any]      							=				self.tokenizer(
                  _lowerCAmelCase    ,	padding="max_length"    ,	max_length=_lowerCAmelCase    ,	truncation=_lowerCAmelCase    ,	return_tensors="pt"    ,	)
              lowerCAmelCase__   :Union[str, Any]      							=				self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

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

          # set timesteps
          self.scheduler.set_timesteps(_lowerCAmelCase		)

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          lowerCAmelCase__   :Optional[int]      							=				self.scheduler.timesteps.to(self.device		)

          # scale the initial noise by the standard deviation required by the scheduler
          lowerCAmelCase__   :Any      							=				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]
          lowerCAmelCase__   :str      							=				"eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
          lowerCAmelCase__   :List[Any]      							=				{}
          if accepts_eta:
              lowerCAmelCase__   :Union[str, Any]      							=				eta

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

              # predict the noise residual
              lowerCAmelCase__   :List[Any]      							=				self.unet(_lowerCAmelCase    ,	_lowerCAmelCase    ,	encoder_hidden_states=_lowerCAmelCase		).sample

              # perform guidance
              if do_classifier_free_guidance:
                  lowerCAmelCase__							,lowerCAmelCase__   :List[str]      							=				noise_pred.chunk(2		)
                  lowerCAmelCase__   :Any      							=				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

              # compute the previous noisy sample x_t -> x_t-1
              lowerCAmelCase__   :Dict      							=				self.scheduler.step(_lowerCAmelCase    ,	_lowerCAmelCase    ,	_lowerCAmelCase    ,	**_lowerCAmelCase		).prev_sample

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

          lowerCAmelCase__   :Union[str, Any]      							=				1 / 0.1_8215 * latents
          lowerCAmelCase__   :int      							=				self.vae.decode(_lowerCAmelCase		).sample

          lowerCAmelCase__   :Union[str, Any]      							=				(image / 2 + 0.5).clamp(0    ,	1		)

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

          if self.safety_checker is not None:
              lowerCAmelCase__   :List[Any]      							=				self.feature_extractor(self.numpy_to_pil(_lowerCAmelCase		)    ,	return_tensors="pt"		).to(
                  self.device		)
              lowerCAmelCase__							,lowerCAmelCase__   :str      							=				self.safety_checker(
                  images=_lowerCAmelCase    ,	clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
          else:
              lowerCAmelCase__   :Union[str, Any]      							=				None

          if output_type == "pil":
              lowerCAmelCase__   :Dict      							=				self.numpy_to_pil(_lowerCAmelCase		)

          if not return_dict:
              return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=_lowerCAmelCase    ,	nsfw_content_detected=_lowerCAmelCase		)











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


_a    :   Dict        =			logging.get_logger(__name__)

_a    :   Optional[Any]        =			{
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}







class 	_UpperCAmelCase  (  _A		):




      """simple docstring"""



      A          =    '''swin2sr'''

      A          =    {
          '''hidden_size''': '''embed_dim''',
          '''num_attention_heads''': '''num_heads''',
          '''num_hidden_layers''': '''num_layers''',
      }
      def __init__(   self    ,	_lowerCAmelCase=64    ,	_lowerCAmelCase=1    ,	_lowerCAmelCase=3    ,	_lowerCAmelCase=180    ,	_lowerCAmelCase=[6, 6, 6, 6, 6, 6]    ,	_lowerCAmelCase=[6, 6, 6, 6, 6, 6]    ,	_lowerCAmelCase=8    ,	_lowerCAmelCase=2.0    ,	_lowerCAmelCase=True    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.0    ,	_lowerCAmelCase=0.1    ,	_lowerCAmelCase="gelu"    ,	_lowerCAmelCase=False    ,	_lowerCAmelCase=0.02    ,	_lowerCAmelCase=1e-5    ,	_lowerCAmelCase=2    ,	_lowerCAmelCase=1.0    ,	_lowerCAmelCase="1conv"    ,	_lowerCAmelCase="pixelshuffle"    ,	**_lowerCAmelCase    ,	):



          '''simple docstring'''
          super().__init__(**_lowerCAmelCase		)

          lowerCAmelCase__   :Union[str, Any]      							=				image_size
          lowerCAmelCase__   :List[Any]      							=				patch_size
          lowerCAmelCase__   :Any      							=				num_channels
          lowerCAmelCase__   :Union[str, Any]      							=				embed_dim
          lowerCAmelCase__   :Optional[int]      							=				depths
          lowerCAmelCase__   :int      							=				len(_lowerCAmelCase		)
          lowerCAmelCase__   :List[str]      							=				num_heads
          lowerCAmelCase__   :str      							=				window_size
          lowerCAmelCase__   :List[str]      							=				mlp_ratio
          lowerCAmelCase__   :List[Any]      							=				qkv_bias
          lowerCAmelCase__   :Union[str, Any]      							=				hidden_dropout_prob
          lowerCAmelCase__   :Optional[int]      							=				attention_probs_dropout_prob
          lowerCAmelCase__   :Dict      							=				drop_path_rate
          lowerCAmelCase__   :Tuple      							=				hidden_act
          lowerCAmelCase__   :Dict      							=				use_absolute_embeddings
          lowerCAmelCase__   :Tuple      							=				layer_norm_eps
          lowerCAmelCase__   :Dict      							=				initializer_range
          lowerCAmelCase__   :Optional[int]      							=				upscale
          lowerCAmelCase__   :Optional[Any]      							=				img_range
          lowerCAmelCase__   :List[str]      							=				resi_connection
          lowerCAmelCase__   :Union[str, Any]      							=				upsampler



