
'''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 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      if isinstance(_lowerCAmelCase				,	torch.Tensor							):
            return image
      elif isinstance(_lowerCAmelCase				,	PIL.Image.Image							):
            _lowerCamelCase				:   List[Any]								=   [image]

      if isinstance(image[0]				,	PIL.Image.Image							):
            _lowerCamelCase				:   Any								=   [np.array(i.resize((w, h)				,	resample=PIL_INTERPOLATION["lanczos"]							)							)[None, :] for i in image]
            _lowerCamelCase				:   List[str]								=   np.concatenate(_lowerCAmelCase				,	axis=0							)
            _lowerCamelCase				:   Tuple								=   np.array(_lowerCAmelCase							).astype(np.floataa							) / 2_5_5.0
            _lowerCamelCase				:   Optional[Any]								=   image.transpose(0				,	3				,	1				,	2							)
            _lowerCamelCase				:   List[str]								=   2.0 * image - 1.0
            _lowerCamelCase				:   List[Any]								=   torch.from_numpy(_lowerCAmelCase							)
      elif isinstance(image[0]				,	torch.Tensor							):
            _lowerCamelCase				:   List[Any]								=   torch.cat(_lowerCAmelCase				,	dim=0							)
      return image







def 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Union[str, Any]=0.9_9_9_5							):



      """simple docstring"""




      if not isinstance(_lowerCAmelCase				,	np.ndarray							):
            _lowerCamelCase				:   Tuple								=   True
            _lowerCamelCase				:   Any								=   va.device
            _lowerCamelCase				:   Tuple								=   va.cpu().numpy()
            _lowerCamelCase				:   Tuple								=   va.cpu().numpy()

      _lowerCamelCase				:   List[Any]								=   np.sum(va * va / (np.linalg.norm(_lowerCAmelCase							) * np.linalg.norm(_lowerCAmelCase							))							)
      if np.abs(_lowerCAmelCase							) > DOT_THRESHOLD:
            _lowerCamelCase				:   List[str]								=   (1 - t) * va + t * va
      else:
            _lowerCamelCase				:   List[Any]								=   np.arccos(_lowerCAmelCase							)
            _lowerCamelCase				:   Tuple								=   np.sin(_lowerCAmelCase							)
            _lowerCamelCase				:   List[str]								=   theta_a * t
            _lowerCamelCase				:   List[Any]								=   np.sin(_lowerCAmelCase							)
            _lowerCamelCase				:   List[Any]								=   np.sin(theta_a - theta_t							) / sin_theta_a
            _lowerCamelCase				:   int								=   sin_theta_t / sin_theta_a
            _lowerCamelCase				:   List[Any]								=   sa * va + sa * va

      if inputs_are_torch:
            _lowerCamelCase				:   int								=   torch.from_numpy(_lowerCAmelCase							).to(_lowerCAmelCase							)

      return va







def 		A_       (	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   Union[str, Any]								=   F.normalize(_lowerCAmelCase				,	dim=-1							)
      _lowerCamelCase				:   Union[str, Any]								=   F.normalize(_lowerCAmelCase				,	dim=-1							)
      return (x - y).norm(dim=-1							).div(2							).arcsin().pow(2							).mul(2							)







def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      for param in model.parameters():
            _lowerCamelCase				:   Tuple								=   value



class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       List[Any],__A      :       AutoencoderKL,__A      :       CLIPTextModel,__A      :       CLIPModel,__A      :       CLIPTokenizer,__A      :       UNetaDConditionModel,__A      :       Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler],__A      :       CLIPFeatureExtractor,__A      :       List[str]=None,__A      :       Dict=None,__A      :       int=None,):
          super().__init__()
          self.register_modules(
              vae=__A,text_encoder=__A,clip_model=__A,tokenizer=__A,unet=__A,scheduler=__A,feature_extractor=__A,coca_model=__A,coca_tokenizer=__A,coca_transform=__A,)
          _lowerCamelCase				:   Any								=   (
              feature_extractor.size
              if isinstance(feature_extractor.size,__A     )
              else feature_extractor.size["shortest_edge"]
          )
          _lowerCamelCase				:   str								=   transforms.Normalize(mean=feature_extractor.image_mean,std=feature_extractor.image_std     )
          set_requires_grad(self.text_encoder,__A     )
          set_requires_grad(self.clip_model,__A     )



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



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          self.enable_attention_slicing(__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          set_requires_grad(self.vae,__A     )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          set_requires_grad(self.vae,__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          set_requires_grad(self.unet,__A     )



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          set_requires_grad(self.unet,__A     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       Tuple,__A      :       Tuple,__A      :       List[str]     ):
          # get the original timestep using init_timestep
          _lowerCamelCase				:   Union[str, Any]								=   min(int(num_inference_steps * strength     ),__A     )

          _lowerCamelCase				:   int								=   max(num_inference_steps - init_timestep,0     )
          _lowerCamelCase				:   Union[str, Any]								=   self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       Tuple,__A      :       Optional[int],__A      :       Union[str, Any],__A      :       Dict,__A      :       List[Any],__A      :       Optional[Any]=None     ):
          if not isinstance(__A,torch.Tensor     ):
                raise ValueError(f'`image` has to be of type `torch.Tensor` but is {type(__A     )}'     )

          _lowerCamelCase				:   List[str]								=   image.to(device=__A,dtype=__A     )

          if isinstance(__A,__A     ):
                _lowerCamelCase				:   str								=   [
                    self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(__A     )
                ]
                _lowerCamelCase				:   List[str]								=   torch.cat(__A,dim=0     )
          else:
                _lowerCamelCase				:   Union[str, Any]								=   self.vae.encode(__A     ).latent_dist.sample(__A     )

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

          _lowerCamelCase				:   List[Any]								=   randn_tensor(init_latents.shape,generator=__A,device=__A,dtype=__A     )

          # get latents
          _lowerCamelCase				:   Union[str, Any]								=   self.scheduler.add_noise(__A,__A,__A     )
          _lowerCamelCase				:   Union[str, Any]								=   init_latents

          return latents



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       Optional[int]     ):
          _lowerCamelCase				:   str								=   self.coca_transform(__A     ).unsqueeze(0     )
          with torch.no_grad(), torch.cuda.amp.autocast():
                _lowerCamelCase				:   Optional[Any]								=   self.coca_model.generate(transformed_image.to(device=self.device,dtype=self.coca_model.dtype     )     )
          _lowerCamelCase				:   int								=   self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
          return generated.split("<end_of_text>"     )[0].replace("<start_of_text>",""     ).rstrip(" .,"     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       Tuple,__A      :       Tuple     ):
          _lowerCamelCase				:   Optional[int]								=   self.feature_extractor.preprocess(__A     )
          _lowerCamelCase				:   Optional[Any]								=   torch.from_numpy(clip_image_input["pixel_values"][0]     ).unsqueeze(0     ).to(self.device     ).half()
          _lowerCamelCase				:   Dict								=   self.clip_model.get_image_features(__A     )
          _lowerCamelCase				:   Any								=   image_embeddings_clip / image_embeddings_clip.norm(p=2,dim=-1,keepdim=__A     )
          _lowerCamelCase				:   List[str]								=   image_embeddings_clip.repeat_interleave(__A,dim=0     )
          return image_embeddings_clip



    @torch.enable_grad()
    def   lowerCamelCase_  (	self      :       str,__A      :       List[str],__A      :       Union[str, Any],__A      :       List[Any],__A      :       Dict,__A      :       List[Any],__A      :       Union[str, Any],__A      :       Union[str, Any],):
          _lowerCamelCase				:   Dict								=   latents.detach().requires_grad_()

          _lowerCamelCase				:   Dict								=   self.scheduler.scale_model_input(__A,__A     )

          # predict the noise residual
          _lowerCamelCase				:   int								=   self.unet(__A,__A,encoder_hidden_states=__A     ).sample

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

                _lowerCamelCase				:   str								=   torch.sqrt(__A     )
                _lowerCamelCase				:   List[Any]								=   pred_original_sample * (fac) + latents * (1 - fac)
          elif isinstance(self.scheduler,__A     ):
                _lowerCamelCase				:   List[str]								=   self.scheduler.sigmas[index]
                _lowerCamelCase				:   Optional[Any]								=   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
          _lowerCamelCase				:   List[Any]								=   1 / 0.18215 * sample
          _lowerCamelCase				:   Any								=   self.vae.decode(__A     ).sample
          _lowerCamelCase				:   Optional[int]								=   (image / 2 + 0.5).clamp(0,1     )

          _lowerCamelCase				:   str								=   transforms.Resize(self.feature_extractor_size     )(__A     )
          _lowerCamelCase				:   Optional[int]								=   self.normalize(__A     ).to(latents.dtype     )

          _lowerCamelCase				:   Any								=   self.clip_model.get_image_features(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   image_embeddings_clip / image_embeddings_clip.norm(p=2,dim=-1,keepdim=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   spherical_dist_loss(__A,__A     ).mean() * clip_guidance_scale

          _lowerCamelCase				:   str								=   -torch.autograd.grad(__A,__A     )[0]

          if isinstance(self.scheduler,__A     ):
                _lowerCamelCase				:   List[str]								=   latents.detach() + grads * (sigma**2)
                _lowerCamelCase				:   Optional[Any]								=   noise_pred_original
          else:
                _lowerCamelCase				:   List[Any]								=   noise_pred_original - torch.sqrt(__A     ) * grads
          return noise_pred, latents



    @torch.no_grad()
    def __call__(	self      :       Optional[int],__A      :       Union[torch.FloatTensor, PIL.Image.Image],__A      :       Union[torch.FloatTensor, PIL.Image.Image],__A      :       Optional[str] = None,__A      :       Optional[str] = None,__A      :       Optional[int] = 5_1_2,__A      :       Optional[int] = 5_1_2,__A      :       float = 0.6,__A      :       Optional[int] = 5_0,__A      :       Optional[float] = 7.5,__A      :       Optional[int] = 1,__A      :       float = 0.0,__A      :       Optional[float] = 1_0_0,__A      :       Optional[torch.Generator] = None,__A      :       Optional[str] = "pil",__A      :       bool = True,__A      :       float = 0.8,__A      :       float = 0.1,__A      :       float = 0.1,):
          if isinstance(__A,__A     ) and len(__A     ) != batch_size:
                raise ValueError(f'You have passed {batch_size} batch_size, but only {len(__A     )} 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(__A,torch.Generator     ) and batch_size > 1:
                _lowerCamelCase				:   List[str]								=   [generator] + [None] * (batch_size - 1)

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

          # get prompt text embeddings for content and style
          _lowerCamelCase				:   str								=   self.tokenizer(
              __A,padding="max_length",max_length=self.tokenizer.model_max_length,truncation=__A,return_tensors="pt",)
          _lowerCamelCase				:   Optional[int]								=   self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

          _lowerCamelCase				:   Optional[int]								=   self.tokenizer(
              __A,padding="max_length",max_length=self.tokenizer.model_max_length,truncation=__A,return_tensors="pt",)
          _lowerCamelCase				:   List[str]								=   self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

          _lowerCamelCase				:   Optional[int]								=   slerp(__A,__A,__A     )

          # duplicate text embeddings for each generation per prompt
          _lowerCamelCase				:   Optional[int]								=   text_embeddings.repeat_interleave(__A,dim=0     )

          # set timesteps
          _lowerCamelCase				:   List[str]								=   "offset" in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
          _lowerCamelCase				:   Union[str, Any]								=   {}
          if accepts_offset:
                _lowerCamelCase				:   Dict								=   1

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

          _lowerCamelCase						,		_lowerCamelCase				:   Any								=   self.get_timesteps(__A,__A,self.device     )
          _lowerCamelCase				:   Optional[int]								=   timesteps[:1].repeat(__A     )

          # Preprocess image
          _lowerCamelCase				:   int								=   preprocess(__A,__A,__A     )
          _lowerCamelCase				:   Optional[int]								=   self.prepare_latents(
              __A,__A,__A,text_embeddings.dtype,self.device,__A     )

          _lowerCamelCase				:   Tuple								=   preprocess(__A,__A,__A     )
          _lowerCamelCase				:   Optional[int]								=   self.prepare_latents(
              __A,__A,__A,text_embeddings.dtype,self.device,__A     )

          _lowerCamelCase				:   Tuple								=   slerp(__A,__A,__A     )

          if clip_guidance_scale > 0:
                _lowerCamelCase				:   Dict								=   self.get_clip_image_embeddings(__A,__A     )
                _lowerCamelCase				:   Dict								=   self.get_clip_image_embeddings(__A,__A     )
                _lowerCamelCase				:   Any								=   slerp(
                    __A,__A,__A     )

          # 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				:   List[str]								=   guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
                _lowerCamelCase				:   Union[str, Any]								=   content_text_input.input_ids.shape[-1]
                _lowerCamelCase				:   Tuple								=   self.tokenizer([""],padding="max_length",max_length=__A,return_tensors="pt"     )
                _lowerCamelCase				:   List[Any]								=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
                # duplicate unconditional embeddings for each generation per prompt
                _lowerCamelCase				:   str								=   uncond_embeddings.repeat_interleave(__A,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
                _lowerCamelCase				:   Optional[Any]								=   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				:   List[str]								=   (batch_size, self.unet.config.in_channels, height // 8, width // 8)
          _lowerCamelCase				:   int								=   text_embeddings.dtype
          if latents is None:
                if self.device.type == "mps":
                      # randn does not work reproducibly on mps
                      _lowerCamelCase				:   List[Any]								=   torch.randn(__A,generator=__A,device="cpu",dtype=__A     ).to(
                          self.device     )
                else:
                      _lowerCamelCase				:   Any								=   torch.randn(__A,generator=__A,device=self.device,dtype=__A     )
          else:
                if latents.shape != latents_shape:
                      raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'     )
                _lowerCamelCase				:   str								=   latents.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				:   str								=   "eta" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
          _lowerCamelCase				:   Any								=   {}
          if accepts_eta:
                _lowerCamelCase				:   List[str]								=   eta

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

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

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

                      # perform classifier free guidance
                      if do_classifier_free_guidance:
                            _lowerCamelCase						,		_lowerCamelCase				:   Any								=   noise_pred.chunk(2     )
                            _lowerCamelCase				:   List[str]								=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                      # perform clip guidance
                      if clip_guidance_scale > 0:
                            _lowerCamelCase				:   Optional[Any]								=   (
                                text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
                            )
                            _lowerCamelCase						,		_lowerCamelCase				:   Dict								=   self.cond_fn(
                                __A,__A,__A,__A,__A,__A,__A,)

                      # compute the previous noisy sample x_t -> x_t-1
                      _lowerCamelCase				:   Any								=   self.scheduler.step(__A,__A,__A,**__A     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
          _lowerCamelCase				:   List[Any]								=   1 / 0.18215 * latents
          _lowerCamelCase				:   Dict								=   self.vae.decode(__A     ).sample

          _lowerCamelCase				:   Optional[Any]								=   (image / 2 + 0.5).clamp(0,1     )
          _lowerCamelCase				:   List[str]								=   image.cpu().permute(0,2,3,1     ).numpy()

          if output_type == "pil":
                _lowerCamelCase				:   List[str]								=   self.numpy_to_pil(__A     )

          if not return_dict:
                return (image, None)

          return StableDiffusionPipelineOutput(images=__A,nsfw_content_detected=__A     )

'''simple docstring'''






import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.roberta.modeling_flax_roberta import (
     FlaxRobertaForCausalLM,
     FlaxRobertaForMaskedLM,
     FlaxRobertaForMultipleChoice,
     FlaxRobertaForQuestionAnswering,
     FlaxRobertaForSequenceClassification,
     FlaxRobertaForTokenClassification,
     FlaxRobertaModel,
 )

class  UpperCAmelCase__						(       unittest.TestCase ):



    def __init__(	self      :       Union[str, Any],__A      :       Dict,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       str=True,__A      :       Optional[int]=True,__A      :       Optional[Any]=True,__A      :       Any=True,__A      :       List[str]=9_9,__A      :       str=3_2,__A      :       List[str]=5,__A      :       Optional[Any]=4,__A      :       Any=3_7,__A      :       Optional[Any]="gelu",__A      :       List[Any]=0.1,__A      :       Any=0.1,__A      :       Dict=5_1_2,__A      :       Tuple=1_6,__A      :       Tuple=2,__A      :       List[Any]=0.02,__A      :       Any=4,):
          _lowerCamelCase				:   List[Any]								=   parent
          _lowerCamelCase				:   Optional[int]								=   batch_size
          _lowerCamelCase				:   Tuple								=   seq_length
          _lowerCamelCase				:   Tuple								=   is_training
          _lowerCamelCase				:   Union[str, Any]								=   use_attention_mask
          _lowerCamelCase				:   Optional[Any]								=   use_token_type_ids
          _lowerCamelCase				:   List[Any]								=   use_labels
          _lowerCamelCase				:   str								=   vocab_size
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Union[str, Any]								=   num_hidden_layers
          _lowerCamelCase				:   Tuple								=   num_attention_heads
          _lowerCamelCase				:   Union[str, Any]								=   intermediate_size
          _lowerCamelCase				:   Optional[Any]								=   hidden_act
          _lowerCamelCase				:   Tuple								=   hidden_dropout_prob
          _lowerCamelCase				:   Optional[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   List[Any]								=   max_position_embeddings
          _lowerCamelCase				:   Union[str, Any]								=   type_vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   type_sequence_label_size
          _lowerCamelCase				:   str								=   initializer_range
          _lowerCamelCase				:   List[Any]								=   num_choices



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Dict								=   None
          if self.use_attention_mask:
                _lowerCamelCase				:   Optional[int]								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   List[str]								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   Tuple								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   Optional[int]								=   RobertaConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,is_decoder=__A,initializer_range=self.initializer_range,)

          return config, input_ids, token_type_ids, attention_mask



    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   List[str]								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   str								=   config_and_inputs
          _lowerCamelCase				:   List[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
          return config, inputs_dict



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   Any								=   self.prepare_config_and_inputs()
          _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Dict								=   config_and_inputs

          _lowerCamelCase				:   int								=   True
          _lowerCamelCase				:   List[Any]								=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
          _lowerCamelCase				:   Optional[int]								=   ids_tensor([self.batch_size, self.seq_length],vocab_size=2     )

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )


@require_flax
class  UpperCAmelCase__						(       A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		True

    lowerCAmelCase_				    =		(
        (
            FlaxRobertaModel,
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   FlaxRobertaModelTester(self     )



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          for model_class_name in self.all_model_classes:
                _lowerCamelCase				:   Union[str, Any]								=   model_class_name.from_pretrained("roberta-base",from_pt=__A     )
                _lowerCamelCase				:   Union[str, Any]								=   model(np.ones((1, 1)     )     )
                self.assertIsNotNone(__A     )