


'''simple docstring'''






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


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






class    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				snake_case    ,				)		->    Any:




          '''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      lowerCAmelCase						(     self    ,				snake_case = "auto")		->    List[Any]:




          '''simple docstring'''
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             _UpperCAmelCase  :  Any				       =self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(snake_case)






       def      lowerCAmelCase						(     self)		->    Optional[int]:




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






       @torch.no_grad()
       def __call__(     self    ,				snake_case    ,				snake_case = 5_1_2    ,				snake_case = 5_1_2    ,				snake_case = 5_0    ,				snake_case = 7.5    ,				snake_case = None    ,				snake_case = 1    ,				snake_case = 0.0    ,				snake_case = None    ,				snake_case = None    ,				snake_case = "pil"    ,				snake_case = True    ,				snake_case = None    ,				snake_case = 1    ,				snake_case = None    ,				**snake_case    ,				)		->    Tuple:




          '''simple docstring'''

          if isinstance(snake_case    ,				snake_case):
             _UpperCAmelCase  :  List[Any]				       =1
          elif isinstance(snake_case    ,				snake_case):
             _UpperCAmelCase  :  List[Any]				       =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
          _UpperCAmelCase  :  str				       =self.tokenizer(
              snake_case    ,				padding='max_length'    ,				max_length=self.tokenizer.model_max_length    ,				return_tensors='pt'    ,				)
          _UpperCAmelCase  :  str				       =text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             _UpperCAmelCase  :  Union[str, Any]				       =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}")
             _UpperCAmelCase  :  Dict				       =text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
             _UpperCAmelCase  :  Union[str, Any]				       =self.text_encoder(text_input_ids.to(self.device))[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =text_embeddings.shape
          _UpperCAmelCase  :  List[Any]				       =text_embeddings.repeat(1    ,				snake_case    ,				1)
          _UpperCAmelCase  :  List[Any]				       =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.
          _UpperCAmelCase  :  List[Any]				       =guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             _UpperCAmelCase  :  List[str]
             if negative_prompt is None:
                _UpperCAmelCase  :  Dict				       =['']
             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):
                _UpperCAmelCase  :  Any				       =[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:
                _UpperCAmelCase  :  Dict				       =negative_prompt

             _UpperCAmelCase  :  Tuple				       =text_input_ids.shape[-1]
             _UpperCAmelCase  :  List[str]				       =self.tokenizer(
                 snake_case    ,				padding='max_length'    ,				max_length=snake_case    ,				truncation=snake_case    ,				return_tensors='pt'    ,				)
             _UpperCAmelCase  :  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
             _UpperCAmelCase  :  List[Any]				       =uncond_embeddings.shape[1]
             _UpperCAmelCase  :  Any				       =uncond_embeddings.repeat(snake_case    ,				snake_case    ,				1)
             _UpperCAmelCase  :  Tuple				       =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
             _UpperCAmelCase  :  Tuple				       =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`.
          _UpperCAmelCase  :  Optional[int]				       =(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          _UpperCAmelCase  :  Optional[Any]				       =(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
          _UpperCAmelCase  :  Optional[Any]				       =text_embeddings.dtype
          if latents is None:
             if self.device.type == "mps":
                # randn does not exist on mps
                _UpperCAmelCase  :  Optional[Any]				       =torch.randn(
                    snake_case    ,				generator=snake_case    ,				device='cpu'    ,				dtype=snake_case).to(self.device)
                _UpperCAmelCase  :  str				       =torch.randn(snake_case    ,				generator=snake_case    ,				device='cpu'    ,				dtype=snake_case).to(
                    self.device)
             else:
                _UpperCAmelCase  :  Optional[int]				       =torch.randn(
                    snake_case    ,				generator=snake_case    ,				device=self.device    ,				dtype=snake_case)
                _UpperCAmelCase  :  Dict				       =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}")
             _UpperCAmelCase  :  List[str]				       =latents_reference.to(self.device)
             _UpperCAmelCase  :  List[Any]				       =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
          _UpperCAmelCase  :  List[str]				       =(latents_shape[3] - latents_shape_reference[3]) // 2
          _UpperCAmelCase  :  Dict				       =(latents_shape[2] - latents_shape_reference[2]) // 2
          _UpperCAmelCase  :  Dict				       =latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          _UpperCAmelCase  :  Union[str, Any]				       =latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          _UpperCAmelCase  :  List[Any]				       =0 if dx < 0 else dx
          _UpperCAmelCase  :  Any				       =0 if dy < 0 else dy
          _UpperCAmelCase  :  Optional[int]				       =max(-dx    ,				0)
          _UpperCAmelCase  :  Dict				       =max(-dy    ,				0)
          # import pdb
          # pdb.set_trace()
          _UpperCAmelCase  :  Dict				       =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
          _UpperCAmelCase  :  Optional[int]				       =self.scheduler.timesteps.to(self.device)

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

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

             # predict the noise residual
             _UpperCAmelCase  :  Optional[Any]				       =self.unet(snake_case    ,				snake_case    ,				encoder_hidden_states=snake_case).sample

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

             # compute the previous noisy sample x_t -> x_t-1
             _UpperCAmelCase  :  Any				       =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)

          _UpperCAmelCase  :  Optional[int]				       =1 / 0.1_82_15 * latents
          _UpperCAmelCase  :  List[str]				       =self.vae.decode(snake_case).sample

          _UpperCAmelCase  :  List[str]				       =(image / 2 + 0.5).clamp(0    ,				1)

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

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

          if output_type == "pil":
             _UpperCAmelCase  :  Union[str, Any]				       =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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

