'''simple docstring'''

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


UpperCamelCase__     :	Dict				=			logging.get_logger(__name__)  # pylint: disable=invalid-name


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,)			->			Optional[int]:



         '''simple docstring'''



         super().__init__()
         self.register_modules(
             vae=lowerCamelCase_					,text_encoder=lowerCamelCase_					,tokenizer=lowerCamelCase_					,unet=lowerCamelCase_					,scheduler=lowerCamelCase_					,safety_checker=lowerCamelCase_					,feature_extractor=lowerCamelCase_					,)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_ = "auto"							)			->			List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			Tuple:



         '''simple docstring'''



         self.enable_attention_slicing(lowerCamelCase_							)




      @torch.no_grad()
      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_ = 512					,lowerCamelCase_ = 512					,lowerCamelCase_ = 50					,lowerCamelCase_ = 7.5					,lowerCamelCase_ = None					,lowerCamelCase_ = 1					,lowerCamelCase_ = 0.0					,lowerCamelCase_ = None					,lowerCamelCase_ = None					,lowerCamelCase_ = "pil"					,lowerCamelCase_ = True					,lowerCamelCase_ = None					,lowerCamelCase_ = 1					,lowerCamelCase_ = None					,**lowerCamelCase_					,)			->			Tuple:



         '''simple docstring'''



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

         if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

         if (callback_steps is None) or (
             callback_steps is not None and (not isinstance(lowerCamelCase_					,lowerCamelCase_							) or callback_steps <= 0)
         ):
            raise ValueError(
                f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                f''' {type(lowerCamelCase_							)}.'''							)

         # get prompt text embeddings
         UpperCAmelCase__							:				Dict   				=  self.tokenizer(
             lowerCamelCase_					,padding='''max_length'''					,max_length=self.tokenizer.model_max_length					,return_tensors='''pt'''					,)
         UpperCAmelCase__							:				List[Any]   				=  text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            UpperCAmelCase__							:				Union[str, Any]   				=  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
            logger.warning(
                '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)
            UpperCAmelCase__							:				Optional[int]   				=  text_input_ids[:, : self.tokenizer.model_max_length]

         if text_embeddings is None:
            UpperCAmelCase__							:				str   				=  self.text_encoder(text_input_ids.to(self.device							)							)[0]

         # duplicate text embeddings for each generation per prompt, using mps friendly method
         UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  text_embeddings.shape
         UpperCAmelCase__							:				List[str]   				=  text_embeddings.repeat(1					,lowerCamelCase_					,1							)
         UpperCAmelCase__							:				List[Any]   				=  text_embeddings.view(bs_embed * num_images_per_prompt					,lowerCamelCase_					,-1							)

         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         UpperCAmelCase__							:				Optional[Any]   				=  guidance_scale > 1.0
         # get unconditional embeddings for classifier free guidance
         if do_classifier_free_guidance:
            UpperCAmelCase__							:				List[str]
            if negative_prompt is None:
               UpperCAmelCase__							:				Any   				=  ['''''']
            elif type(lowerCamelCase_							) is not type(lowerCamelCase_							):
               raise TypeError(
                   f'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase_							)} !='''
                   f''' {type(lowerCamelCase_							)}.'''							)
            elif isinstance(lowerCamelCase_					,lowerCamelCase_							):
               UpperCAmelCase__							:				Dict   				=  [negative_prompt]
            elif batch_size != len(lowerCamelCase_							):
               raise ValueError(
                   f'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase_							)}, but `prompt`:'''
                   f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
                   ''' the batch size of `prompt`.'''							)
            else:
               UpperCAmelCase__							:				str   				=  negative_prompt

            UpperCAmelCase__							:				List[str]   				=  text_input_ids.shape[-1]
            UpperCAmelCase__							:				Optional[int]   				=  self.tokenizer(
                lowerCamelCase_					,padding='''max_length'''					,max_length=lowerCamelCase_					,truncation=lowerCamelCase_					,return_tensors='''pt'''					,)
            UpperCAmelCase__							:				Any   				=  self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            UpperCAmelCase__							:				Tuple   				=  uncond_embeddings.shape[1]
            UpperCAmelCase__							:				str   				=  uncond_embeddings.repeat(lowerCamelCase_					,lowerCamelCase_					,1							)
            UpperCAmelCase__							:				int   				=  uncond_embeddings.view(batch_size * num_images_per_prompt					,lowerCamelCase_					,-1							)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            UpperCAmelCase__							:				Optional[int]   				=  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__							:				List[str]   				=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         UpperCAmelCase__							:				str   				=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
         UpperCAmelCase__							:				List[str]   				=  text_embeddings.dtype
         if latents is None:
            if self.device.type == "mps":
               # randn does not exist on mps
               UpperCAmelCase__							:				Union[str, Any]   				=  torch.randn(
                   lowerCamelCase_					,generator=lowerCamelCase_					,device='''cpu'''					,dtype=lowerCamelCase_							).to(self.device							)
               UpperCAmelCase__							:				Optional[int]   				=  torch.randn(lowerCamelCase_					,generator=lowerCamelCase_					,device='''cpu'''					,dtype=lowerCamelCase_							).to(
                   self.device							)
            else:
               UpperCAmelCase__							:				int   				=  torch.randn(
                   lowerCamelCase_					,generator=lowerCamelCase_					,device=self.device					,dtype=lowerCamelCase_							)
               UpperCAmelCase__							:				Union[str, Any]   				=  torch.randn(lowerCamelCase_					,generator=lowerCamelCase_					,device=self.device					,dtype=lowerCamelCase_							)
         else:
            if latents_reference.shape != latents_shape:
               raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
            UpperCAmelCase__							:				str   				=  latents_reference.to(self.device							)
            UpperCAmelCase__							:				Any   				=  latents.to(self.device							)

         # This is the key part of the pipeline where we
         # try to ensure that the generated images w/ the same seed
         # but different sizes actually result in similar images
         UpperCAmelCase__							:				str   				=  (latents_shape[3] - latents_shape_reference[3]) // 2
         UpperCAmelCase__							:				Tuple   				=  (latents_shape[2] - latents_shape_reference[2]) // 2
         UpperCAmelCase__							:				int   				=  latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
         UpperCAmelCase__							:				Optional[Any]   				=  latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
         UpperCAmelCase__							:				List[Any]   				=  0 if dx < 0 else dx
         UpperCAmelCase__							:				Dict   				=  0 if dy < 0 else dy
         UpperCAmelCase__							:				int   				=  max(-dx					,0							)
         UpperCAmelCase__							:				List[str]   				=  max(-dy					,0							)
         # import pdb
         # pdb.set_trace()
         UpperCAmelCase__							:				Tuple   				=  latents_reference[:, :, dy : dy + h, dx : dx + w]

         # set timesteps
         self.scheduler.set_timesteps(lowerCamelCase_							)

         # Some schedulers like PNDM have timesteps as arrays
         # It's more optimized to move all timesteps to correct device beforehand
         UpperCAmelCase__							:				int   				=  self.scheduler.timesteps.to(self.device							)

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

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

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

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

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase__							:				Optional[int]   				=  self.scheduler.step(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
               callback(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Union[str, Any]   				=  1 / 0.1_8215 * latents
         UpperCAmelCase__							:				str   				=  self.vae.decode(lowerCamelCase_							).sample

         UpperCAmelCase__							:				Optional[Any]   				=  (image / 2 + 0.5).clamp(0					,1							)

         # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
         UpperCAmelCase__							:				List[Any]   				=  image.cpu().permute(0					,2					,3					,1							).float().numpy()

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

         if output_type == "pil":
            UpperCAmelCase__							:				Union[str, Any]   				=  self.numpy_to_pil(lowerCamelCase_							)

         if not return_dict:
            return (image, has_nsfw_concept)

         return StableDiffusionPipelineOutput(images=lowerCamelCase_					,nsfw_content_detected=lowerCamelCase_							)





'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Union[str, Any]  ,			_A					:				Optional[int]  ,			_A					:				Any  ,			_A					:				Optional[int]=True  ,			_A					:				List[Any]="pt"    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  {'''add_prefix_space''': True} if isinstance(_A  ,			_A    ) and not line.startswith(''' '''    ) else {}
   UpperCAmelCase__							:				str   				=  padding_side
   return tokenizer(
       [line]  ,			max_length=_A  ,			padding='''max_length''' if pad_to_max_length else None  ,			truncation=_A  ,			return_tensors=_A  ,			add_special_tokens=_A  ,			**_A  ,			)







def __UpperCamelCase(   _A					:				Any  ,			_A					:				Union[str, Any]  ,			_A					:				str=None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  input_ids.ne(_A    ).any(dim=0    )
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_="train"					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=""					,)			->			Any:



         '''simple docstring'''



         super().__init__()
         UpperCAmelCase__							:				List[str]   				=  Path(lowerCamelCase_							).joinpath(type_path + '''.source'''							)
         UpperCAmelCase__							:				Optional[int]   				=  Path(lowerCamelCase_							).joinpath(type_path + '''.target'''							)
         UpperCAmelCase__							:				Tuple   				=  self.get_char_lens(self.src_file							)
         UpperCAmelCase__							:				Union[str, Any]   				=  max_source_length
         UpperCAmelCase__							:				Optional[int]   				=  max_target_length
         assert min(self.src_lens							) > 0, f'''found empty line in {self.src_file}'''
         UpperCAmelCase__							:				str   				=  tokenizer
         UpperCAmelCase__							:				Optional[int]   				=  prefix
         if n_obs is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.src_lens[:n_obs]
         UpperCAmelCase__							:				int   				=  src_lang
         UpperCAmelCase__							:				Dict   				=  tgt_lang




      def __len__(				self							)			->			Optional[int]:



         '''simple docstring'''



         return len(self.src_lens							)




      def __getitem__(				self					,lowerCamelCase_							)			->			Dict[str, torch.Tensor]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  index + 1  # linecache starts at 1
         UpperCAmelCase__							:				Tuple   				=  self.prefix + linecache.getline(str(self.src_file							)					,lowerCamelCase_							).rstrip('''\n'''							)
         UpperCAmelCase__							:				List[str]   				=  linecache.getline(str(self.tgt_file							)					,lowerCamelCase_							).rstrip('''\n'''							)
         assert source_line, f'''empty source line for index {index}'''
         assert tgt_line, f'''empty tgt line for index {index}'''

         # Need to add eos token manually for T5
         if isinstance(self.tokenizer					,lowerCamelCase_							):
            source_line += self.tokenizer.eos_token
            tgt_line += self.tokenizer.eos_token

         # Pad source and target to the right
         UpperCAmelCase__							:				int   				=  (
             self.tokenizer.question_encoder if isinstance(self.tokenizer					,lowerCamelCase_							) else self.tokenizer
         )
         UpperCAmelCase__							:				Any   				=  self.tokenizer.generator if isinstance(self.tokenizer					,lowerCamelCase_							) else self.tokenizer

         UpperCAmelCase__							:				List[Any]   				=  encode_line(lowerCamelCase_					,lowerCamelCase_					,self.max_source_length					,'''right'''							)
         UpperCAmelCase__							:				Dict   				=  encode_line(lowerCamelCase_					,lowerCamelCase_					,self.max_target_length					,'''right'''							)

         UpperCAmelCase__							:				Any   				=  source_inputs['''input_ids'''].squeeze()
         UpperCAmelCase__							:				Any   				=  target_inputs['''input_ids'''].squeeze()
         UpperCAmelCase__							:				int   				=  source_inputs['''attention_mask'''].squeeze()
         return {
             "input_ids": source_ids,
             "attention_mask": src_mask,
             "decoder_input_ids": target_ids,
         }




      @staticmethod
      def 		lowerCAmelCase__					(				lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         return [len(lowerCamelCase_							) for x in Path(lowerCamelCase_							).open().readlines()]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Dict[str, torch.Tensor]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  torch.stack([x['''input_ids'''] for x in batch]							)
         UpperCAmelCase__							:				int   				=  torch.stack([x['''attention_mask'''] for x in batch]							)
         UpperCAmelCase__							:				List[Any]   				=  torch.stack([x['''decoder_input_ids'''] for x in batch]							)
         UpperCAmelCase__							:				Optional[Any]   				=  (
             self.tokenizer.generator.pad_token_id
             if isinstance(self.tokenizer					,lowerCamelCase_							)
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase__							:				Optional[Any]   				=  (
             self.tokenizer.question_encoder.pad_token_id
             if isinstance(self.tokenizer					,lowerCamelCase_							)
             else self.tokenizer.pad_token_id
         )
         UpperCAmelCase__							:				Dict   				=  trim_batch(lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__				,      UpperCAmelCase__							:				List[str]   				=  trim_batch(lowerCamelCase_					,lowerCamelCase_					,attention_mask=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  {
             '''input_ids''': source_ids,
             '''attention_mask''': source_mask,
             '''decoder_input_ids''': y,
         }
         return batch




UpperCamelCase__     :	int				=			getLogger(__name__)







def __UpperCamelCase(   _A					:				List[List]    ):
   '''simple docstring'''
   return list(itertools.chain.from_iterable(_A    )    )







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  get_git_info()
   save_json(_A  ,			os.path.join(_A  ,			'''git_log.json'''    )    )







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Optional[Any]  ,			_A					:				List[Any]=4  ,			**_A					:				Optional[Any]    ):
   '''simple docstring'''
   with open(_A  ,			'''w'''    ) as f:
      json.dump(_A  ,			_A  ,			indent=_A  ,			**_A    )







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   with open(_A    ) as f:
      return json.load(_A    )







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  git.Repo(search_parent_directories=_A    )
   UpperCAmelCase__							:				int   				=  {
       '''repo_id''': str(_A    ),
       '''repo_sha''': str(repo.head.object.hexsha    ),
       '''repo_branch''': str(repo.active_branch    ),
       '''hostname''': str(socket.gethostname()    ),
   }
   return repo_infos







def __UpperCamelCase(   _A					:				Callable  ,			_A					:				Iterable    ):
   '''simple docstring'''
   return list(map(_A  ,			_A    )    )







def __UpperCamelCase(   _A					:				str  ,			_A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''wb'''    ) as f:
      return pickle.dump(_A  ,			_A    )







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''

   def remove_articles(_A					:				int    ):
      return re.sub(R'''\b(a|an|the)\b'''  ,			''' '''  ,			_A    )

   def white_space_fix(_A					:				List[Any]    ):
      return " ".join(text.split()    )

   def remove_punc(_A					:				Tuple    ):
      UpperCAmelCase__							:				Optional[int]   				=  set(string.punctuation    )
      return "".join(ch for ch in text if ch not in exclude    )

   def lower(_A					:				Any    ):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(_A    )    )    )    )







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  normalize_answer(_A    ).split()
   UpperCAmelCase__							:				str   				=  normalize_answer(_A    ).split()
   UpperCAmelCase__							:				str   				=  Counter(_A    ) & Counter(_A    )
   UpperCAmelCase__							:				Optional[Any]   				=  sum(common.values()    )
   if num_same == 0:
      return 0
   UpperCAmelCase__							:				Union[str, Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				List[Any]   				=  1.0 * num_same / len(_A    )
   UpperCAmelCase__							:				Dict   				=  (2 * precision * recall) / (precision + recall)
   return fa







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Tuple    ):
   '''simple docstring'''
   return normalize_answer(_A    ) == normalize_answer(_A    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   assert len(_A    ) == len(_A    )
   UpperCAmelCase__							:				Optional[int]   				=  0
   for hypo, pred in zip(_A  ,			_A    ):
      em += exact_match_score(_A  ,			_A    )
   if len(_A    ) > 0:
      em /= len(_A    )
   return {"em": em}







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''
   return model_prefix.startswith('''rag'''    )







def __UpperCamelCase(   _A					:				str  ,			_A					:				Any  ,			_A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   UpperCAmelCase__							:				Optional[int]   				=  '''dropout_rate'''
   for p in extra_params:
      if getattr(_A  ,			_A  ,			_A    ):
         if not hasattr(_A  ,			_A    ) and not hasattr(_A  ,			equivalent_param[p]    ):
            logger.info('''config doesn\'t have a `{}` attribute'''.format(_A    )    )
            delattr(_A  ,			_A    )
            continue
         UpperCAmelCase__							:				int   				=  p if hasattr(_A  ,			_A    ) else equivalent_param[p]
         setattr(_A  ,			_A  ,			getattr(_A  ,			_A    )    )
         delattr(_A  ,			_A    )
   return hparams, config




