

"""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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[int]:
 if isinstance(__UpperCamelCase      ,   torch.Tensor						):
  return image
 elif isinstance(__UpperCamelCase      ,   PIL.Image.Image						):
  __magic_name__     =      [image]

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





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase=0.9_995						) ->      str:
 if not isinstance(__UpperCamelCase      ,   np.ndarray						):
  __magic_name__     =      True
  __magic_name__     =      va.device
  __magic_name__     =      va.cpu().numpy()
  __magic_name__     =      va.cpu().numpy()

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

 if inputs_are_torch:
  __magic_name__     =      torch.from_numpy(__UpperCamelCase						).to(__UpperCamelCase						)

 return va





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__     =      F.normalize(__UpperCamelCase      ,   dim=-1						)
 __magic_name__     =      F.normalize(__UpperCamelCase      ,   dim=-1						)
 return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Union[str, Any]:
 for param in model.parameters():
  __magic_name__     =      value
class        _lowercase					(     __UpperCAmelCase     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						):
      super().__init__()
      self.register_modules(
          vae=UpperCamelCase_ ,						text_encoder=UpperCamelCase_ ,						clip_model=UpperCamelCase_ ,						tokenizer=UpperCamelCase_ ,						unet=UpperCamelCase_ ,						scheduler=UpperCamelCase_ ,						feature_extractor=UpperCamelCase_ ,						coca_model=UpperCamelCase_ ,						coca_tokenizer=UpperCamelCase_ ,						coca_transform=UpperCamelCase_ ,						)
      __magic_name__     =      (
          feature_extractor.size
          if isinstance(feature_extractor.size ,						UpperCamelCase_       )
          else feature_extractor.size['''shortest_edge''']
      )
      __magic_name__     =      transforms.Normalize(mean=feature_extractor.image_mean ,						std=feature_extractor.image_std       )
      set_requires_grad(self.text_encoder ,						UpperCamelCase_       )
      set_requires_grad(self.clip_model ,						UpperCamelCase_       )







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







     def 	lowerCAmelCase__    (      self       ):
      self.enable_attention_slicing(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      set_requires_grad(self.vae ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      set_requires_grad(self.vae ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      set_requires_grad(self.unet ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      set_requires_grad(self.unet ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      # get the original timestep using init_timestep
      __magic_name__     =      min(int(num_inference_steps * strength       ) ,						UpperCamelCase_       )

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

      return timesteps, num_inference_steps - t_start







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_=None       ):
      if not isinstance(UpperCamelCase_ ,						torch.Tensor       ):
       raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(UpperCamelCase_       )}'''       )

      __magic_name__     =      image.to(device=UpperCamelCase_ ,						dtype=UpperCamelCase_       )

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

      # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __magic_name__     =      0.1_8_2_1_5 * init_latents
      __magic_name__     =      init_latents.repeat_interleave(UpperCamelCase_ ,						dim=0       )

      __magic_name__     =      randn_tensor(init_latents.shape ,						generator=UpperCamelCase_ ,						device=UpperCamelCase_ ,						dtype=UpperCamelCase_       )

      # get latents
      __magic_name__     =      self.scheduler.add_noise(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      init_latents

      return latents







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







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







     @torch.enable_grad()
     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						):
      __magic_name__     =      latents.detach().requires_grad_()

      __magic_name__     =      self.scheduler.scale_model_input(UpperCamelCase_ ,						UpperCamelCase_       )

      # predict the noise residual
      __magic_name__     =      self.unet(UpperCamelCase_ ,						UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_       ).sample

      if isinstance(self.scheduler ,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)       ):
       __magic_name__     =      self.scheduler.alphas_cumprod[timestep]
       __magic_name__     =      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
       __magic_name__     =      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

       __magic_name__     =      torch.sqrt(UpperCamelCase_       )
       __magic_name__     =      pred_original_sample * (fac) + latents * (1 - fac)
      elif isinstance(self.scheduler ,						UpperCamelCase_       ):
       __magic_name__     =      self.scheduler.sigmas[index]
       __magic_name__     =      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
      __magic_name__     =      1 / 0.1_8_2_1_5 * sample
      __magic_name__     =      self.vae.decode(UpperCamelCase_       ).sample
      __magic_name__     =      (image / 2 + 0.5).clamp(0 ,						1       )

      __magic_name__     =      transforms.Resize(self.feature_extractor_size       )(UpperCamelCase_       )
      __magic_name__     =      self.normalize(UpperCamelCase_       ).to(latents.dtype       )

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

      __magic_name__     =      spherical_dist_loss(UpperCamelCase_ ,						UpperCamelCase_       ).mean() * clip_guidance_scale

      __magic_name__     =      -torch.autograd.grad(UpperCamelCase_ ,						UpperCamelCase_       )[0]

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







     @torch.no_grad()
     def __call__(      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = 512 ,						UpperCamelCase_ = 512 ,						UpperCamelCase_ = 0.6 ,						UpperCamelCase_ = 50 ,						UpperCamelCase_ = 7.5 ,						UpperCamelCase_ = 1 ,						UpperCamelCase_ = 0.0 ,						UpperCamelCase_ = 100 ,						UpperCamelCase_ = None ,						UpperCamelCase_ = "pil" ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 0.8 ,						UpperCamelCase_ = 0.1 ,						UpperCamelCase_ = 0.1 ,						):
      if isinstance(UpperCamelCase_ ,						UpperCamelCase_       ) and len(UpperCamelCase_       ) != batch_size:
       raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(UpperCamelCase_       )} 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(UpperCamelCase_ ,						torch.Generator       ) and batch_size > 1:
       __magic_name__     =      [generator] + [None] * (batch_size - 1)

      __magic_name__     =      [
          ('''model''', self.coca_model is None),
          ('''tokenizer''', self.coca_tokenizer is None),
          ('''transform''', self.coca_transform is None),
      ]
      __magic_name__     =      [x[0] for x in coca_is_none if x[1]]
      __magic_name__     =      ''', '''.join(UpperCamelCase_       )
      # generate prompts with coca model if prompt is None
      if content_prompt is None:
       if len(UpperCamelCase_       ):
        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.'''       )
       __magic_name__     =      self.get_image_description(UpperCamelCase_       )
      if style_prompt is None:
       if len(UpperCamelCase_       ):
        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.'''       )
       __magic_name__     =      self.get_image_description(UpperCamelCase_       )

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

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

      __magic_name__     =      slerp(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )

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

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

      self.scheduler.set_timesteps(UpperCamelCase_ ,						**UpperCamelCase_       )
      # 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       )

      __magic_name__   ,						__magic_name__     =      self.get_timesteps(UpperCamelCase_ ,						UpperCamelCase_ ,						self.device       )
      __magic_name__     =      timesteps[:1].repeat(UpperCamelCase_       )

      # Preprocess image
      __magic_name__     =      preprocess(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.prepare_latents(
          UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						text_embeddings.dtype ,						self.device ,						UpperCamelCase_       )

      __magic_name__     =      preprocess(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.prepare_latents(
          UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						text_embeddings.dtype ,						self.device ,						UpperCamelCase_       )

      __magic_name__     =      slerp(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )

      if clip_guidance_scale > 0:
       __magic_name__     =      self.get_clip_image_embeddings(UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_clip_image_embeddings(UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      slerp(
           UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )

      # 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.
      __magic_name__     =      guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
       __magic_name__     =      content_text_input.input_ids.shape[-1]
       __magic_name__     =      self.tokenizer([''''''] ,						padding='''max_length''' ,						max_length=UpperCamelCase_ ,						return_tensors='''pt'''       )
       __magic_name__     =      self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
       # duplicate unconditional embeddings for each generation per prompt
       __magic_name__     =      uncond_embeddings.repeat_interleave(UpperCamelCase_ ,						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
       __magic_name__     =      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`.
      __magic_name__     =      (batch_size, self.unet.config.in_channels, height // 8, width // 8)
      __magic_name__     =      text_embeddings.dtype
      if latents is None:
       if self.device.type == "mps":
        # randn does not work reproducibly on mps
        __magic_name__     =      torch.randn(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						device='''cpu''' ,						dtype=UpperCamelCase_       ).to(
            self.device       )
       else:
        __magic_name__     =      torch.randn(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						device=self.device ,						dtype=UpperCamelCase_       )
      else:
       if latents.shape != latents_shape:
        raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''       )
       __magic_name__     =      latents.to(self.device       )

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

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

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

        # predict the noise residual
        __magic_name__     =      self.unet(UpperCamelCase_ ,						UpperCamelCase_ ,						encoder_hidden_states=UpperCamelCase_       ).sample

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

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

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
      __magic_name__     =      1 / 0.1_8_2_1_5 * latents
      __magic_name__     =      self.vae.decode(UpperCamelCase_       ).sample

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

      if output_type == "pil":
       __magic_name__     =      self.numpy_to_pil(UpperCamelCase_       )

      if not return_dict:
       return (image, None)

      return StableDiffusionPipelineOutput(images=UpperCamelCase_ ,						nsfw_content_detected=UpperCamelCase_       )




"""simple docstring"""






import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      tempfile.mkdtemp()

      # fmt: off
      __magic_name__     =      ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
      # fmt: on
      __magic_name__     =      os.path.join(self.tmpdirname ,						VOCAB_FILES_NAMES['''vocab_file''']       )
      with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
       vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]       )       )

      __magic_name__     =      {
          '''do_resize''': True,
          '''size''': {'''height''': 18, '''width''': 18},
          '''do_normalize''': True,
          '''image_mean''': [0.5, 0.5, 0.5],
          '''image_std''': [0.5, 0.5, 0.5],
      }
      __magic_name__     =      os.path.join(self.tmpdirname ,						UpperCamelCase_       )
      with open(self.image_processor_file ,						'''w''' ,						encoding='''utf-8'''       ) as fp:
       json.dump(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return BertTokenizer.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      return ViTImageProcessor.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      shutil.rmtree(self.tmpdirname       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [np.random.randint(255 ,						size=(3, 30, 400) ,						dtype=np.uinta       )]

      __magic_name__     =      [Image.fromarray(np.moveaxis(UpperCamelCase_ ,						0 ,						-1       )       ) for x in image_inputs]

      return image_inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      self.get_image_processor()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      processor.save_pretrained(self.tmpdirname       )
      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()       )
      processor.save_pretrained(self.tmpdirname       )

      __magic_name__     =      self.get_tokenizer(bos_token='''(BOS)''' ,						eos_token='''(EOS)'''       )
      __magic_name__     =      self.get_image_processor(do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      __magic_name__     =      VisionTextDualEncoderProcessor.from_pretrained(
          self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=UpperCamelCase_ ,						padding_value=1.0       )

      self.assertEqual(processor.tokenizer.get_vocab() ,						tokenizer_add_kwargs.get_vocab()       )
      self.assertIsInstance(processor.tokenizer ,						(BertTokenizer, BertTokenizerFast)       )

      self.assertEqual(processor.image_processor.to_json_string() ,						image_processor_add_kwargs.to_json_string()       )
      self.assertIsInstance(processor.image_processor ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      image_processor(UpperCamelCase_ ,						return_tensors='''np'''       )
      __magic_name__     =      processor(images=UpperCamelCase_ ,						return_tensors='''np'''       )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum() ,						input_processor[key].sum() ,						delta=1E-2       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''

      __magic_name__     =      processor(text=UpperCamelCase_       )

      __magic_name__     =      tokenizer(UpperCamelCase_       )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key] ,						encoded_processor[key]       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']       )

      # test if it raises when no input is passed
      with self.assertRaises(UpperCamelCase_       ):
       processor()







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __magic_name__     =      processor.batch_decode(UpperCamelCase_       )
      __magic_name__     =      tokenizer.batch_decode(UpperCamelCase_       )

      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_image_processor()
      __magic_name__     =      self.get_tokenizer()

      __magic_name__     =      VisionTextDualEncoderProcessor(tokenizer=UpperCamelCase_ ,						image_processor=UpperCamelCase_       )

      __magic_name__     =      '''lower newer'''
      __magic_name__     =      self.prepare_image_inputs()

      __magic_name__     =      processor(text=UpperCamelCase_ ,						images=UpperCamelCase_       )

      self.assertListEqual(list(inputs.keys()       ) ,						processor.model_input_names       )

