

'''simple docstring'''



from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


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

UpperCAmelCase					:			Optional[Any]       =	'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'







def   a__			(    a__ ,       a__ ,       a__=8		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = h // scale_factor**2
     if h % scale_factor**2 != 0:
          new_h += 1
     __SCREAMING_SNAKE_CASE      = w // scale_factor**2
     if w % scale_factor**2 != 0:
          new_w += 1
     return new_h * scale_factor, new_w * scale_factor







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			MultilingualCLIP   ,				__SCREAMING_SNAKE_CASE   :			XLMRobertaTokenizer   ,				__SCREAMING_SNAKE_CASE   :			UNetaDConditionModel   ,				__SCREAMING_SNAKE_CASE   :			Union[DDIMScheduler, DDPMScheduler]   ,				__SCREAMING_SNAKE_CASE   :			VQModel   ,				)    ->					Union[str, Any]:


      """simple docstring"""




      super().__init__()

      self.register_modules(
          text_encoder=__SCREAMING_SNAKE_CASE   ,				tokenizer=__SCREAMING_SNAKE_CASE   ,				unet=__SCREAMING_SNAKE_CASE   ,				scheduler=__SCREAMING_SNAKE_CASE   ,				movq=__SCREAMING_SNAKE_CASE   ,				)
      __SCREAMING_SNAKE_CASE      = 2 ** (len(self.movq.config.block_out_channels     ) - 1)
 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]     )    ->					int:


      """simple docstring"""




      if latents is None:
           __SCREAMING_SNAKE_CASE      = randn_tensor(__SCREAMING_SNAKE_CASE   ,				generator=__SCREAMING_SNAKE_CASE   ,				device=__SCREAMING_SNAKE_CASE   ,				dtype=__SCREAMING_SNAKE_CASE     )
      else:
           if latents.shape != shape:
                raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'     )
           __SCREAMING_SNAKE_CASE      = latents.to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = latents * scheduler.init_noise_sigma
      return latents
 def  UpperCAmelCase__						(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			Any   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = len(__SCREAMING_SNAKE_CASE     ) if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ) else 1
      # get prompt text embeddings
      __SCREAMING_SNAKE_CASE      = self.tokenizer(
          __SCREAMING_SNAKE_CASE   ,				padding="""max_length"""   ,				truncation=__SCREAMING_SNAKE_CASE   ,				max_length=77   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""   ,				)

      __SCREAMING_SNAKE_CASE      = text_inputs.input_ids
      __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				padding="""longest"""   ,				return_tensors="""pt"""     ).input_ids

      if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
           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}'     )

      __SCREAMING_SNAKE_CASE      = text_input_ids.to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = text_inputs.attention_mask.to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.text_encoder(
          input_ids=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = prompt_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE   ,				dim=0     )
      __SCREAMING_SNAKE_CASE      = text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE   ,				dim=0     )
      __SCREAMING_SNAKE_CASE      = text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE   ,				dim=0     )

      if do_classifier_free_guidance:
           __SCREAMING_SNAKE_CASE      = 42
           if negative_prompt is None:
                __SCREAMING_SNAKE_CASE      = [""""""] * batch_size
           elif type(__SCREAMING_SNAKE_CASE     ) is not type(__SCREAMING_SNAKE_CASE     ):
                raise TypeError(
                    f'`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE     )} !='
                    f' {type(__SCREAMING_SNAKE_CASE     )}.'     )
           elif isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
                __SCREAMING_SNAKE_CASE      = [negative_prompt]
           elif batch_size != len(__SCREAMING_SNAKE_CASE     ):
                raise ValueError(
                    f'`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_SNAKE_CASE     )}, but `prompt`:'
                    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                    """ the batch size of `prompt`."""     )
           else:
                __SCREAMING_SNAKE_CASE      = negative_prompt

           __SCREAMING_SNAKE_CASE      = self.tokenizer(
               __SCREAMING_SNAKE_CASE   ,				padding="""max_length"""   ,				max_length=77   ,				truncation=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""   ,				)
           __SCREAMING_SNAKE_CASE      = uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = uncond_input.attention_mask.to(__SCREAMING_SNAKE_CASE     )

           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self.text_encoder(
               input_ids=__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE     )

           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

           __SCREAMING_SNAKE_CASE      = negative_prompt_embeds.shape[1]
           __SCREAMING_SNAKE_CASE      = negative_prompt_embeds.repeat(1   ,				__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = negative_prompt_embeds.view(batch_size * num_images_per_prompt   ,				__SCREAMING_SNAKE_CASE     )

           __SCREAMING_SNAKE_CASE      = uncond_text_encoder_hidden_states.shape[1]
           __SCREAMING_SNAKE_CASE      = uncond_text_encoder_hidden_states.repeat(1   ,				__SCREAMING_SNAKE_CASE   ,				1     )
           __SCREAMING_SNAKE_CASE      = uncond_text_encoder_hidden_states.view(
               batch_size * num_images_per_prompt   ,				__SCREAMING_SNAKE_CASE   ,				-1     )
           __SCREAMING_SNAKE_CASE      = uncond_text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE   ,				dim=0     )

           # done duplicates

           # 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
           __SCREAMING_SNAKE_CASE      = torch.cat([negative_prompt_embeds, prompt_embeds]     )
           __SCREAMING_SNAKE_CASE      = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

           __SCREAMING_SNAKE_CASE      = torch.cat([uncond_text_mask, text_mask]     )

      return prompt_embeds, text_encoder_hidden_states, text_mask
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					List[str]:


      """simple docstring"""




      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError("""Please install accelerate via `pip install accelerate`"""     )

      __SCREAMING_SNAKE_CASE      = torch.device(f'cuda:{gpu_id}'     )

      __SCREAMING_SNAKE_CASE      = [
          self.unet,
          self.text_encoder,
          self.movq,
      ]
      for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
                cpu_offload(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0     )    ->					int:


      """simple docstring"""




      if is_accelerate_available() and is_accelerate_version(""">="""   ,				"""0.17.0.dev0"""     ):
           from accelerate import cpu_offload_with_hook
      else:
           raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""     )

      __SCREAMING_SNAKE_CASE      = torch.device(f'cuda:{gpu_id}'     )

      if self.device.type != "cpu":
           self.to("""cpu"""   ,				silence_dtype_warnings=__SCREAMING_SNAKE_CASE     )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      __SCREAMING_SNAKE_CASE      = None
      for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = cpu_offload_with_hook(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				prev_module_hook=__SCREAMING_SNAKE_CASE     )

      if self.safety_checker is not None:
           __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = cpu_offload_with_hook(self.safety_checker   ,				__SCREAMING_SNAKE_CASE   ,				prev_module_hook=__SCREAMING_SNAKE_CASE     )

      # We'll offload the last model manually.
      __SCREAMING_SNAKE_CASE      = hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[Any]:


      """simple docstring"""




      if not hasattr(self.unet   ,				"""_hf_hook"""     ):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(__SCREAMING_SNAKE_CASE   ,				"""_hf_hook"""     )
               and hasattr(module._hf_hook   ,				"""execution_device"""     )
               and module._hf_hook.execution_device is not None
           ):
                return torch.device(module._hf_hook.execution_device     )
      return self.device


 @torch.no_grad()
 @replace_example_docstring(__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, List[str]]   ,				__SCREAMING_SNAKE_CASE   :			Union[torch.FloatTensor, List[torch.FloatTensor]]   ,				__SCREAMING_SNAKE_CASE   :			Union[torch.FloatTensor, List[torch.FloatTensor]]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, List[str]]] = None   ,				__SCREAMING_SNAKE_CASE   :			int = 512   ,				__SCREAMING_SNAKE_CASE   :			int = 512   ,				__SCREAMING_SNAKE_CASE   :			int = 100   ,				__SCREAMING_SNAKE_CASE   :			float = 4.0   ,				__SCREAMING_SNAKE_CASE   :			int = 1   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[torch.FloatTensor] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[str] = "pil"   ,				__SCREAMING_SNAKE_CASE   :			bool = True   ,				)    ->					Optional[int]:


      """simple docstring"""




      if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = 1
      elif isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = len(__SCREAMING_SNAKE_CASE     )
      else:
           raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE     )}'     )

      __SCREAMING_SNAKE_CASE      = self._execution_device

      __SCREAMING_SNAKE_CASE      = batch_size * num_images_per_prompt
      __SCREAMING_SNAKE_CASE      = guidance_scale > 1.0

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = self._encode_prompt(
          __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = torch.cat(__SCREAMING_SNAKE_CASE   ,				dim=0     )
      if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           __SCREAMING_SNAKE_CASE      = torch.cat(__SCREAMING_SNAKE_CASE   ,				dim=0     )

      if do_classifier_free_guidance:
           __SCREAMING_SNAKE_CASE      = image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE   ,				dim=0     )
           __SCREAMING_SNAKE_CASE      = negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE   ,				dim=0     )

      __SCREAMING_SNAKE_CASE      = torch.cat([negative_image_embeds, image_embeds]   ,				dim=0     ).to(
          dtype=prompt_embeds.dtype   ,				device=__SCREAMING_SNAKE_CASE     )

      self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   ,				device=__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = self.scheduler.timesteps

      __SCREAMING_SNAKE_CASE      = self.unet.config.in_channels

      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = get_new_h_w(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				self.movq_scale_factor     )

      # create initial latent
      __SCREAMING_SNAKE_CASE      = self.prepare_latents(
          (batch_size, num_channels_latents, height, width)   ,				text_encoder_hidden_states.dtype   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				self.scheduler   ,				)

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

           __SCREAMING_SNAKE_CASE      = {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
           __SCREAMING_SNAKE_CASE      = self.unet(
               sample=__SCREAMING_SNAKE_CASE   ,				timestep=__SCREAMING_SNAKE_CASE   ,				encoder_hidden_states=__SCREAMING_SNAKE_CASE   ,				added_cond_kwargs=__SCREAMING_SNAKE_CASE   ,				return_dict=__SCREAMING_SNAKE_CASE   ,				)[0]

           if do_classifier_free_guidance:
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = noise_pred.split(latents.shape[1]   ,				dim=1     )
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = noise_pred.chunk(2     )
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = variance_pred.chunk(2     )
                __SCREAMING_SNAKE_CASE      = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                __SCREAMING_SNAKE_CASE      = torch.cat([noise_pred, variance_pred_text]   ,				dim=1     )

           if not (
               hasattr(self.scheduler.config   ,				"""variance_type"""     )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
                __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = noise_pred.split(latents.shape[1]   ,				dim=1     )

           # compute the previous noisy sample x_t -> x_t-1
           __SCREAMING_SNAKE_CASE      = self.scheduler.step(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				generator=__SCREAMING_SNAKE_CASE   ,				).prev_sample
      # post-processing
      __SCREAMING_SNAKE_CASE      = self.movq.decode(__SCREAMING_SNAKE_CASE   ,				force_not_quantize=__SCREAMING_SNAKE_CASE     )["""sample"""]

      if output_type not in ["pt", "np", "pil"]:
           raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'     )

      if output_type in ["np", "pil"]:
           __SCREAMING_SNAKE_CASE      = image * 0.5 + 0.5
           __SCREAMING_SNAKE_CASE      = image.clamp(0   ,				1     )
           __SCREAMING_SNAKE_CASE      = image.cpu().permute(0   ,				2   ,				3   ,				1     ).float().numpy()

      if output_type == "pil":
           __SCREAMING_SNAKE_CASE      = self.numpy_to_pil(__SCREAMING_SNAKE_CASE     )

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE     )









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






