






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


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






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       def __init__(				self	:Union[str, Any],      snake_case	:AutoencoderKL,      snake_case	:CLIPTextModel,      snake_case	:CLIPTokenizer,      snake_case	:UNetaDConditionModel,      snake_case	:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],      snake_case	:StableDiffusionSafetyChecker,      snake_case	:CLIPImageProcessor,      ):



          """simple docstring"""

          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 				UpperCamelCase__						(				self	:str,      snake_case	:Optional[Union[str, int]] = "auto"):



          """simple docstring"""

          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(snake_case)





       def 				UpperCamelCase__						(				self	:Tuple):



          """simple docstring"""

          self.enable_attention_slicing(snake_case)





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



          """simple docstring"""

          if isinstance(snake_case,      snake_case):
             _lowercase              =1
          elif isinstance(snake_case,      snake_case):
             _lowercase              =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
          _lowercase              =self.tokenizer(
              snake_case,      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,      snake_case,      1)
          _lowercase              =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.
          _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(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):
                _lowercase              =[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:
                _lowercase              =negative_prompt

             _lowercase              =text_input_ids.shape[-1]
             _lowercase              =self.tokenizer(
                 snake_case,      padding='max_length',      max_length=snake_case,      truncation=snake_case,      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(snake_case,      snake_case,      1)
             _lowercase              =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
             _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(
                    snake_case,      generator=snake_case,      device='cpu',      dtype=snake_case).to(self.device)
                _lowercase              =torch.randn(snake_case,      generator=snake_case,      device='cpu',      dtype=snake_case).to(
                    self.device)
             else:
                _lowercase              =torch.randn(
                    snake_case,      generator=snake_case,      device=self.device,      dtype=snake_case)
                _lowercase              =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}''')
             _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(snake_case)

          # 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(snake_case)):
             # 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(snake_case,      snake_case)

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

          _lowercase              =1 / 0.1_8_2_1_5 * latents
          _lowercase              =self.vae.decode(snake_case).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(snake_case),      return_tensors='pt').to(
                 self.device)
             _lowercase       ,       _lowercase              =self.safety_checker(
                 images=snake_case,      clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
          else:
             _lowercase              =None

          if output_type == "pil":
             _lowercase              =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)










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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   =  {
    "uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		str					='''mra'''





       def __init__(				self	:Any,      snake_case	:List[str]=5_0265,      snake_case	:List[Any]=768,      snake_case	:Optional[Any]=12,      snake_case	:Optional[Any]=12,      snake_case	:str=3072,      snake_case	:Tuple="gelu",      snake_case	:Optional[int]=0.1,      snake_case	:int=0.1,      snake_case	:Any=512,      snake_case	:Union[str, Any]=1,      snake_case	:Union[str, Any]=0.0_2,      snake_case	:List[Any]=1e-5,      snake_case	:Optional[int]="absolute",      snake_case	:Optional[int]=4,      snake_case	:str="full",      snake_case	:Optional[int]=0,      snake_case	:List[Any]=0,      snake_case	:int=1,      snake_case	:List[Any]=0,      snake_case	:Dict=2,      **snake_case	:Dict,      ):



          """simple docstring"""

          super().__init__(pad_token_id=snake_case,      bos_token_id=snake_case,      eos_token_id=snake_case,      **snake_case)

          _lowercase              =vocab_size
          _lowercase              =max_position_embeddings
          _lowercase              =hidden_size
          _lowercase              =num_hidden_layers
          _lowercase              =num_attention_heads
          _lowercase              =intermediate_size
          _lowercase              =hidden_act
          _lowercase              =hidden_dropout_prob
          _lowercase              =attention_probs_dropout_prob
          _lowercase              =initializer_range
          _lowercase              =type_vocab_size
          _lowercase              =layer_norm_eps
          _lowercase              =position_embedding_type
          _lowercase              =block_per_row
          _lowercase              =approx_mode
          _lowercase              =initial_prior_first_n_blocks
          _lowercase              =initial_prior_diagonal_n_blocks


