
'''simple docstring'''


import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)



def  __snake_case     (    _UpperCAmelCase					:       Union[str, Any],      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       Any):
   if isinstance(_UpperCAmelCase,      torch.Tensor):
      return image
   elif isinstance(_UpperCAmelCase,      PIL.Image.Image):
      UpperCamelCase       =  [image]

   if isinstance(image[0],      PIL.Image.Image):
      UpperCamelCase       =  [np.array(i.resize((w, h),      resample=PIL_INTERPOLATION['''lanczos''']))[None, :] for i in image]
      UpperCamelCase       =  np.concatenate(_UpperCAmelCase,      axis=0)
      UpperCamelCase       =  np.array(_UpperCAmelCase).astype(np.floataa) / 255.0
      UpperCamelCase       =  image.transpose(0,      3,      1,      2)
      UpperCamelCase       =  2.0 * image - 1.0
      UpperCamelCase       =  torch.from_numpy(_UpperCAmelCase)
   elif isinstance(image[0],      torch.Tensor):
      UpperCamelCase       =  torch.cat(_UpperCAmelCase,      dim=0)
   return image



def  __snake_case     (    _UpperCAmelCase					:       int,      _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       str,      _UpperCAmelCase					:       Any=0.9_9_9_5):
   if not isinstance(_UpperCAmelCase,      np.ndarray):
      UpperCamelCase       =  True
      UpperCamelCase       =  va.device
      UpperCamelCase       =  va.cpu().numpy()
      UpperCamelCase       =  va.cpu().numpy()

   UpperCamelCase       =  np.sum(va * va / (np.linalg.norm(_UpperCAmelCase) * np.linalg.norm(_UpperCAmelCase)))
   if np.abs(_UpperCAmelCase) > DOT_THRESHOLD:
      UpperCamelCase       =  (1 - t) * va + t * va
   else:
      UpperCamelCase       =  np.arccos(_UpperCAmelCase)
      UpperCamelCase       =  np.sin(_UpperCAmelCase)
      UpperCamelCase       =  theta_a * t
      UpperCamelCase       =  np.sin(_UpperCAmelCase)
      UpperCamelCase       =  np.sin(theta_a - theta_t) / sin_theta_a
      UpperCamelCase       =  sin_theta_t / sin_theta_a
      UpperCamelCase       =  sa * va + sa * va

   if inputs_are_torch:
      UpperCamelCase       =  torch.from_numpy(_UpperCAmelCase).to(_UpperCAmelCase)

   return va



def  __snake_case     (    _UpperCAmelCase					:       List[str],      _UpperCAmelCase					:       int):
   UpperCamelCase       =  F.normalize(_UpperCAmelCase,      dim=-1)
   UpperCamelCase       =  F.normalize(_UpperCAmelCase,      dim=-1)
   return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)



def  __snake_case     (    _UpperCAmelCase					:       str,      _UpperCAmelCase					:       int):
   for param in model.parameters():
      UpperCamelCase       =  value


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   lowerCamelCase__=None ,   ):

      '''simple docstring'''







      super().__init__()
      self.register_modules(
          vae=lowerCamelCase__ ,   text_encoder=lowerCamelCase__ ,   clip_model=lowerCamelCase__ ,   tokenizer=lowerCamelCase__ ,   unet=lowerCamelCase__ ,   scheduler=lowerCamelCase__ ,   feature_extractor=lowerCamelCase__ ,   coca_model=lowerCamelCase__ ,   coca_tokenizer=lowerCamelCase__ ,   coca_transform=lowerCamelCase__ ,   )
      UpperCamelCase       =  (
          feature_extractor.size
          if isinstance(feature_extractor.size ,   lowerCamelCase__   )
          else feature_extractor.size['''shortest_edge''']
      )
      UpperCamelCase       =  transforms.Normalize(mean=feature_extractor.image_mean ,   std=feature_extractor.image_std   )
      set_requires_grad(self.text_encoder ,   lowerCamelCase__   )
      set_requires_grad(self.clip_model ,   lowerCamelCase__   )







   def       UpperCAmelCase      (			self ,   lowerCamelCase__ = "auto"   ):

      '''simple docstring'''







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







   def       UpperCAmelCase      (			self   ):

      '''simple docstring'''







      self.enable_attention_slicing(lowerCamelCase__   )







   def       UpperCAmelCase      (			self   ):

      '''simple docstring'''







      set_requires_grad(self.vae ,   lowerCamelCase__   )







   def       UpperCAmelCase      (			self   ):

      '''simple docstring'''







      set_requires_grad(self.vae ,   lowerCamelCase__   )







   def       UpperCAmelCase      (			self   ):

      '''simple docstring'''







      set_requires_grad(self.unet ,   lowerCamelCase__   )







   def       UpperCAmelCase      (			self   ):

      '''simple docstring'''







      set_requires_grad(self.unet ,   lowerCamelCase__   )







   def       UpperCAmelCase      (			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  min(int(num_inference_steps * strength   ) ,   lowerCamelCase__   )

      UpperCamelCase       =  max(num_inference_steps - init_timestep ,   0   )
      UpperCamelCase       =  self.scheduler.timesteps[t_start:]

      return timesteps, num_inference_steps - t_start







   def       UpperCAmelCase      (			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      if not isinstance(lowerCamelCase__ ,   torch.Tensor   ):
         raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase__   )}'   )

      UpperCamelCase       =  image.to(device=lowerCamelCase__ ,   dtype=lowerCamelCase__   )

      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
         UpperCamelCase       =  [
             self.vae.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(lowerCamelCase__   )
         ]
         UpperCamelCase       =  torch.cat(lowerCamelCase__ ,   dim=0   )
      else:
         UpperCamelCase       =  self.vae.encode(lowerCamelCase__   ).latent_dist.sample(lowerCamelCase__   )

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      UpperCamelCase       =  0.18215 * init_latents
      UpperCamelCase       =  init_latents.repeat_interleave(lowerCamelCase__ ,   dim=0   )

      UpperCamelCase       =  randn_tensor(init_latents.shape ,   generator=lowerCamelCase__ ,   device=lowerCamelCase__ ,   dtype=lowerCamelCase__   )

      # get latents
      UpperCamelCase       =  self.scheduler.add_noise(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
      UpperCamelCase       =  init_latents

      return latents







   def       UpperCAmelCase      (			self ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.coca_transform(lowerCamelCase__   ).unsqueeze(0   )
      with torch.no_grad(), torch.cuda.amp.autocast():
         UpperCamelCase       =  self.coca_model.generate(transformed_image.to(device=self.device ,   dtype=self.coca_model.dtype   )   )
      UpperCamelCase       =  self.coca_tokenizer.decode(generated[0].cpu().numpy()   )
      return generated.split('''<end_of_text>'''   )[0].replace('''<start_of_text>''' ,   ''''''   ).rstrip(''' .,'''   )







   def       UpperCAmelCase      (			self ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.feature_extractor.preprocess(lowerCamelCase__   )
      UpperCamelCase       =  torch.from_numpy(clip_image_input['''pixel_values'''][0]   ).unsqueeze(0   ).to(self.device   ).half()
      UpperCamelCase       =  self.clip_model.get_image_features(lowerCamelCase__   )
      UpperCamelCase       =  image_embeddings_clip / image_embeddings_clip.norm(p=2 ,   dim=-1 ,   keepdim=lowerCamelCase__   )
      UpperCamelCase       =  image_embeddings_clip.repeat_interleave(lowerCamelCase__ ,   dim=0   )
      return image_embeddings_clip







   @torch.enable_grad()
   def       UpperCAmelCase      (			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  latents.detach().requires_grad_()

      UpperCamelCase       =  self.scheduler.scale_model_input(lowerCamelCase__ ,   lowerCamelCase__   )

      # predict the noise residual
      UpperCamelCase       =  self.unet(lowerCamelCase__ ,   lowerCamelCase__ ,   encoder_hidden_states=lowerCamelCase__   ).sample

      if isinstance(self.scheduler ,   (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)   ):
         UpperCamelCase       =  self.scheduler.alphas_cumprod[timestep]
         UpperCamelCase       =  1 - alpha_prod_t
         # compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         UpperCamelCase       =  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

         UpperCamelCase       =  torch.sqrt(lowerCamelCase__   )
         UpperCamelCase       =  pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler ,   lowerCamelCase__   ):
         UpperCamelCase       =  self.scheduler.sigmas[index]
         UpperCamelCase       =  latents - sigma * noise_pred
      else:
         raise ValueError(f'scheduler type {type(self.scheduler   )} not supported'   )

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      UpperCamelCase       =  1 / 0.18215 * sample
      UpperCamelCase       =  self.vae.decode(lowerCamelCase__   ).sample
      UpperCamelCase       =  (image / 2 + 0.5).clamp(0 ,   1   )

      UpperCamelCase       =  transforms.Resize(self.feature_extractor_size   )(lowerCamelCase__   )
      UpperCamelCase       =  self.normalize(lowerCamelCase__   ).to(latents.dtype   )

      UpperCamelCase       =  self.clip_model.get_image_features(lowerCamelCase__   )
      UpperCamelCase       =  image_embeddings_clip / image_embeddings_clip.norm(p=2 ,   dim=-1 ,   keepdim=lowerCamelCase__   )

      UpperCamelCase       =  spherical_dist_loss(lowerCamelCase__ ,   lowerCamelCase__   ).mean() * clip_guidance_scale

      UpperCamelCase       =  -torch.autograd.grad(lowerCamelCase__ ,   lowerCamelCase__   )[0]

      if isinstance(self.scheduler ,   lowerCamelCase__   ):
         UpperCamelCase       =  latents.detach() + grads * (sigma**2)
         UpperCamelCase       =  noise_pred_original
      else:
         UpperCamelCase       =  noise_pred_original - torch.sqrt(lowerCamelCase__   ) * grads
      return noise_pred, latents







   @torch.no_grad()
   def __call__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 5_1_2 ,   lowerCamelCase__ = 0.6 ,   lowerCamelCase__ = 5_0 ,   lowerCamelCase__ = 7.5 ,   lowerCamelCase__ = 1 ,   lowerCamelCase__ = 0.0 ,   lowerCamelCase__ = 1_0_0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = "pil" ,   lowerCamelCase__ = True ,   lowerCamelCase__ = 0.8 ,   lowerCamelCase__ = 0.1 ,   lowerCamelCase__ = 0.1 ,   ):

      '''simple docstring'''







      if isinstance(lowerCamelCase__ ,   lowerCamelCase__   ) and len(lowerCamelCase__   ) != batch_size:
         raise ValueError(f'You have passed {batch_size} batch_size, but only {len(lowerCamelCase__   )} generators.'   )

      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 isinstance(lowerCamelCase__ ,   torch.Generator   ) and batch_size > 1:
         UpperCamelCase       =  [generator] + [None] * (batch_size - 1)

      UpperCamelCase       =  [
          ('''model''', self.coca_model is None),
          ('''tokenizer''', self.coca_tokenizer is None),
          ('''transform''', self.coca_transform is None),
      ]
      UpperCamelCase       =  [x[0] for x in coca_is_none if x[1]]
      UpperCamelCase       =  ''', '''.join(lowerCamelCase__   )
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
         if len(lowerCamelCase__   ):
            raise ValueError(
                f'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
                f'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'   )
         UpperCamelCase       =  self.get_image_description(lowerCamelCase__   )
      if style_prompt is None:
         if len(lowerCamelCase__   ):
            raise ValueError(
                f'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
                f' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'   )
         UpperCamelCase       =  self.get_image_description(lowerCamelCase__   )

      # get prompt text embeddings for content and style
      UpperCamelCase       =  self.tokenizer(
          lowerCamelCase__ ,   padding='''max_length''' ,   max_length=self.tokenizer.model_max_length ,   truncation=lowerCamelCase__ ,   return_tensors='''pt''' ,   )
      UpperCamelCase       =  self.text_encoder(content_text_input.input_ids.to(self.device   )   )[0]

      UpperCamelCase       =  self.tokenizer(
          lowerCamelCase__ ,   padding='''max_length''' ,   max_length=self.tokenizer.model_max_length ,   truncation=lowerCamelCase__ ,   return_tensors='''pt''' ,   )
      UpperCamelCase       =  self.text_encoder(style_text_input.input_ids.to(self.device   )   )[0]

      UpperCamelCase       =  slerp(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )

      # duplicate text embeddings for each generation per prompt
      UpperCamelCase       =  text_embeddings.repeat_interleave(lowerCamelCase__ ,   dim=0   )

      # set timesteps
      UpperCamelCase       =  '''offset''' in set(inspect.signature(self.scheduler.set_timesteps   ).parameters.keys()   )
      UpperCamelCase       =  {}
      if accepts_offset:
         UpperCamelCase       =  1

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

      UpperCamelCase     , UpperCamelCase       =  self.get_timesteps(lowerCamelCase__ ,   lowerCamelCase__ ,   self.device   )
      UpperCamelCase       =  timesteps[:1].repeat(lowerCamelCase__   )

      # Preprocess image
      UpperCamelCase       =  preprocess(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
      UpperCamelCase       =  self.prepare_latents(
          lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   text_embeddings.dtype ,   self.device ,   lowerCamelCase__   )

      UpperCamelCase       =  preprocess(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )
      UpperCamelCase       =  self.prepare_latents(
          lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   text_embeddings.dtype ,   self.device ,   lowerCamelCase__   )

      UpperCamelCase       =  slerp(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )

      if clip_guidance_scale > 0:
         UpperCamelCase       =  self.get_clip_image_embeddings(lowerCamelCase__ ,   lowerCamelCase__   )
         UpperCamelCase       =  self.get_clip_image_embeddings(lowerCamelCase__ ,   lowerCamelCase__   )
         UpperCamelCase       =  slerp(
             lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   )

      # 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       =  guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
         UpperCamelCase       =  content_text_input.input_ids.shape[-1]
         UpperCamelCase       =  self.tokenizer([''''''] ,   padding='''max_length''' ,   max_length=lowerCamelCase__ ,   return_tensors='''pt'''   )
         UpperCamelCase       =  self.text_encoder(uncond_input.input_ids.to(self.device   )   )[0]
         # duplicate unconditional embeddings for each generation per prompt
         UpperCamelCase       =  uncond_embeddings.repeat_interleave(lowerCamelCase__ ,   dim=0   )

         # 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       =  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       =  (batch_size, self.unet.config.in_channels, height // 8, width // 8)
      UpperCamelCase       =  text_embeddings.dtype
      if latents is None:
         if self.device.type == "mps":
            # randn does not work reproducibly on mps
            UpperCamelCase       =  torch.randn(lowerCamelCase__ ,   generator=lowerCamelCase__ ,   device='''cpu''' ,   dtype=lowerCamelCase__   ).to(
                self.device   )
         else:
            UpperCamelCase       =  torch.randn(lowerCamelCase__ ,   generator=lowerCamelCase__ ,   device=self.device ,   dtype=lowerCamelCase__   )
      else:
         if latents.shape != latents_shape:
            raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'   )
         UpperCamelCase       =  latents.to(self.device   )

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

      # check if the scheduler accepts generator
      UpperCamelCase       =  '''generator''' in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
      if accepts_generator:
         UpperCamelCase       =  generator

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

            # predict the noise residual
            UpperCamelCase       =  self.unet(lowerCamelCase__ ,   lowerCamelCase__ ,   encoder_hidden_states=lowerCamelCase__   ).sample

            # perform classifier free guidance
            if do_classifier_free_guidance:
               UpperCamelCase     , UpperCamelCase       =  noise_pred.chunk(2   )
               UpperCamelCase       =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # perform clip guidance
            if clip_guidance_scale > 0:
               UpperCamelCase       =  (
                   text_embeddings.chunk(2   )[1] if do_classifier_free_guidance else text_embeddings
               )
               UpperCamelCase     , UpperCamelCase       =  self.cond_fn(
                   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   )

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      UpperCamelCase       =  1 / 0.18215 * latents
      UpperCamelCase       =  self.vae.decode(lowerCamelCase__   ).sample

      UpperCamelCase       =  (image / 2 + 0.5).clamp(0 ,   1   )
      UpperCamelCase       =  image.cpu().permute(0 ,   2 ,   3 ,   1   ).numpy()

      if output_type == "pil":
         UpperCamelCase       =  self.numpy_to_pil(lowerCamelCase__   )

      if not return_dict:
         return (image, None)

      return StableDiffusionPipelineOutput(images=lowerCamelCase__ ,   nsfw_content_detected=lowerCamelCase__   )







'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''tokenizer''']
   _snake_case		   =							'''BlipImageProcessor'''
   _snake_case		   =							'''AutoTokenizer'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__   )

      # add QFormer tokenizer
      UpperCamelCase       =  qformer_tokenizer







   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You have to specify at least images or text.'''   )

      UpperCamelCase       =  BatchFeature()

      if text is not None:
         UpperCamelCase       =  self.tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         encoding.update(lowerCamelCase__   )
         UpperCamelCase       =  self.qformer_tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         UpperCamelCase       =  qformer_text_encoding.pop('''input_ids'''   )
         UpperCamelCase       =  qformer_text_encoding.pop('''attention_mask'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(lowerCamelCase__ ,   return_tensors=lowerCamelCase__   )
         encoding.update(lowerCamelCase__   )

      return encoding







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.batch_decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer.model_input_names
      UpperCamelCase       =  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      if os.path.isfile(lowerCamelCase__   ):
         raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
      os.makedirs(lowerCamelCase__ ,   exist_ok=lowerCamelCase__   )
      UpperCamelCase       =  os.path.join(lowerCamelCase__ ,   '''qformer_tokenizer'''   )
      self.qformer_tokenizer.save_pretrained(lowerCamelCase__   )
      return super().save_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,   subfolder='''qformer_tokenizer'''   )
      UpperCamelCase       =  cls._get_arguments_from_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )
      args.append(lowerCamelCase__   )
      return cls(*lowerCamelCase__   )





