





import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
_A :   Optional[Any]  			=  [
    'kernels/rwkv/wkv_cuda.cu',
    'kernels/rwkv/wkv_op.cpp',
    'kernels/deformable_detr/ms_deform_attn.h',
    'kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh',
    'models/graphormer/algos_graphormer.pyx',
]



def 	_a						(   UpperCAmelCase					)    ->    str:







   """simple docstring"""
   # Test all the extensions added in the setup
   for file in FILES_TO_FIND:
      if not (transformers_path / file).exists():
         return False
   return True


if __name__ == "__main__":
       _A :   Dict  			=  argparse.ArgumentParser()
       parser.add_argument('--check_lib', action='store_true', help='Whether to check the build or the actual package.')
       _A :   int  			=  parser.parse_args()
       if args.check_lib:
              _A :   Dict  			=  importlib.import_module('transformers')
              _A :   Tuple  			=  Path(transformers_module.__file__).parent
       else:
              _A :   Optional[int]  			=  Path.cwd() / 'build/lib/transformers'
       if not test_custom_files_are_present(transformers_path):
              raise ValueError('The built release does not contain the custom files. Fix this before going further!')










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 :   Optional[Any]  			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):







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







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Optional[Union[str, int]] = "auto"      )       ->Tuple:
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             lowerCamelCase__       :						List[Any]        =							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          self.enable_attention_slicing(A      )







       @torch.no_grad()
       def __call__(				self					:     Union[str, Any]		, A					:     Union[str, List[str]]		, A					:     int = 5_1_2		, A					:     int = 5_1_2		, A					:     int = 5_0		, 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					:     int		, )       ->Tuple:

          if isinstance(A		, A      ):
             lowerCamelCase__       :						str        =							1
          elif isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							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
          lowerCamelCase__       :						Optional[Any]        =							self.tokenizer(
              A		, 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[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}"      )
             lowerCamelCase__       :						Any        =							text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
             lowerCamelCase__       :						str        =							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__       :						int        =							text_embeddings.shape
          lowerCamelCase__       :						int        =							text_embeddings.repeat(1		, A		, 1      )
          lowerCamelCase__       :						str        =							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.
          lowerCamelCase__       :						Optional[Any]        =							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__       :						Optional[int]        =							['''''']
             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      ):
                lowerCamelCase__       :						Optional[int]        =							[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:
                lowerCamelCase__       :						List[Any]        =							negative_prompt

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

          # Some schedulers like PNDM have timesteps as arrays
          # It's more optimized to move all timesteps to correct device beforehand
          lowerCamelCase__       :						Union[str, Any]        =							self.scheduler.timesteps.to(self.device      )

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

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

             # predict the noise residual
             lowerCamelCase__       :						Union[str, Any]        =							self.unet(A		, A		, encoder_hidden_states=A      ).sample

             # perform guidance
             if do_classifier_free_guidance:
                lowerCamelCase__       ,							lowerCamelCase__       :						List[str]        =							noise_pred.chunk(2      )
                lowerCamelCase__       :						int        =							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(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      )

          lowerCamelCase__       :						Optional[Any]        =							1 / 0.1_82_15 * latents
          lowerCamelCase__       :						int        =							self.vae.decode(A      ).sample

          lowerCamelCase__       :						Dict        =							(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__       :						List[str]        =							image.cpu().permute(0		, 2		, 3		, 1      ).float().numpy()

          if self.safety_checker is not None:
             lowerCamelCase__       :						Tuple        =							self.feature_extractor(self.numpy_to_pil(A      )		, return_tensors='''pt'''      ).to(
                 self.device      )
             lowerCamelCase__       ,							lowerCamelCase__       :						int        =							self.safety_checker(
                 images=A		, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
          else:
             lowerCamelCase__       :						List[Any]        =							None

          if output_type == "pil":
             lowerCamelCase__       :						Optional[Any]        =							self.numpy_to_pil(A      )

          if not return_dict:
             return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=A		, nsfw_content_detected=A      )



