





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 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=True							,       _lowerCAmelCase="pt"					)						->			List[Any]:
 UpperCamelCase     : Any	     =       {"add_prefix_space": True} if isinstance(_lowerCAmelCase							,       _lowerCAmelCase					) and not line.startswith(" "					) else {}
 UpperCamelCase     : List[str]	     =       padding_side
 return tokenizer(
     [line]							,       max_length=_lowerCAmelCase							,       padding="max_length" if pad_to_max_length else None							,       truncation=_lowerCAmelCase							,       return_tensors=_lowerCAmelCase							,       add_special_tokens=_lowerCAmelCase							,       **_lowerCAmelCase							,       )



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       )						->			List[str]:
 UpperCamelCase     : Any	     =       input_ids.ne(_lowerCAmelCase					).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  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_="train"	,							A_=None	,							A_=None	,							A_=None	,							A_=""	,							):
       '''simple docstring'''




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





      def __len__(			self       ):
       '''simple docstring'''




       return len(self.src_lens       )





      def __getitem__(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Any	     =       index + 1  # linecache starts at 1
       UpperCamelCase     : Union[str, Any]	     =       self.prefix + linecache.getline(str(self.src_file       )	,							A_       ).rstrip("\n"       )
       UpperCamelCase     : List[Any]	     =       linecache.getline(str(self.tgt_file       )	,							A_       ).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	,							A_       ):
        source_line += self.tokenizer.eos_token
        tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       UpperCamelCase     : str	     =       (
           self.tokenizer.question_encoder if isinstance(self.tokenizer	,							A_       ) else self.tokenizer
       )
       UpperCamelCase     : int	     =       self.tokenizer.generator if isinstance(self.tokenizer	,							A_       ) else self.tokenizer

       UpperCamelCase     : Union[str, Any]	     =       encode_line(A_	,							A_	,							self.max_source_length	,							"right"       )
       UpperCamelCase     : Optional[int]	     =       encode_line(A_	,							A_	,							self.max_target_length	,							"right"       )

       UpperCamelCase     : List[str]	     =       source_inputs["input_ids"].squeeze()
       UpperCamelCase     : List[str]	     =       target_inputs["input_ids"].squeeze()
       UpperCamelCase     : Union[str, Any]	     =       source_inputs["attention_mask"].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }





      @staticmethod
      def 	__UpperCamelCase(			A_       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       torch.stack([x["input_ids"] for x in batch]       )
       UpperCamelCase     : Any	     =       torch.stack([x["attention_mask"] for x in batch]       )
       UpperCamelCase     : List[Any]	     =       torch.stack([x["decoder_input_ids"] for x in batch]       )
       UpperCamelCase     : List[str]	     =       (
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer	,							A_       )
           else self.tokenizer.pad_token_id
       )
       UpperCamelCase     : List[str]	     =       (
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer	,							A_       )
           else self.tokenizer.pad_token_id
       )
       UpperCamelCase     : List[Any]	     =       trim_batch(A_	,							A_       )
       UpperCamelCase     ,   UpperCamelCase     : List[str]	     =       trim_batch(A_	,							A_	,							attention_mask=A_       )
       UpperCamelCase     : str	     =       {
           "input_ids": source_ids,
           "attention_mask": source_mask,
           "decoder_input_ids": y,
       }
       return batch


__lowerCamelCase	:					Any			  =					getLogger(__name__)



def 		A_    (					_lowerCAmelCase					)						->			Union[str, Any]:
 return list(itertools.chain.from_iterable(_lowerCAmelCase					)					)



def 		A_    (					_lowerCAmelCase					)						->			None:
 UpperCamelCase     : Any	     =       get_git_info()
 save_json(_lowerCAmelCase							,       os.path.join(_lowerCAmelCase							,       "git_log.json"					)					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=4							,       **_lowerCAmelCase					)						->			Optional[int]:
 with open(_lowerCAmelCase							,       "w"					) as f:
  json.dump(_lowerCAmelCase							,       _lowerCAmelCase							,       indent=_lowerCAmelCase							,       **_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase					)						->			Any:
 with open(_lowerCAmelCase					) as f:
  return json.load(_lowerCAmelCase					)



def 		A_    (					)						->			str:
 UpperCamelCase     : int	     =       git.Repo(search_parent_directories=_lowerCAmelCase					)
 UpperCamelCase     : Dict	     =       {
     "repo_id": str(_lowerCAmelCase					),
     "repo_sha": str(repo.head.object.hexsha					),
     "repo_branch": str(repo.active_branch					),
     "hostname": str(socket.gethostname()					),
 }
 return repo_infos



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			List:
 return list(map(_lowerCAmelCase							,       _lowerCAmelCase					)					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			str:
 with open(_lowerCAmelCase							,       "wb"					) as f:
  return pickle.dump(_lowerCAmelCase							,       _lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase					)						->			Dict:

 def remove_articles(_lowerCAmelCase					):
  return re.sub(r"\b(a|an|the)\b"							,       " "							,       _lowerCAmelCase					)

 def white_space_fix(_lowerCAmelCase					):
  return " ".join(text.split()					)

 def remove_punc(_lowerCAmelCase					):
  UpperCamelCase     : Optional[Any]	     =       set(string.punctuation					)
  return "".join(ch for ch in text if ch not in exclude					)

 def lower(_lowerCAmelCase					):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase					)					)					)					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 UpperCamelCase     : Optional[int]	     =       normalize_answer(_lowerCAmelCase					).split()
 UpperCamelCase     : Optional[Any]	     =       normalize_answer(_lowerCAmelCase					).split()
 UpperCamelCase     : List[str]	     =       Counter(_lowerCAmelCase					) & Counter(_lowerCAmelCase					)
 UpperCamelCase     : int	     =       sum(common.values()					)
 if num_same == 0:
  return 0
 UpperCamelCase     : Optional[int]	     =       1.0 * num_same / len(_lowerCAmelCase					)
 UpperCamelCase     : Optional[int]	     =       1.0 * num_same / len(_lowerCAmelCase					)
 UpperCamelCase     : Optional[Any]	     =       (2 * precision * recall) / (precision + recall)
 return fa



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Any:
 return normalize_answer(_lowerCAmelCase					) == normalize_answer(_lowerCAmelCase					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase					)						->			Dict:
 assert len(_lowerCAmelCase					) == len(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       0
 for hypo, pred in zip(_lowerCAmelCase							,       _lowerCAmelCase					):
  em += exact_match_score(_lowerCAmelCase							,       _lowerCAmelCase					)
 if len(_lowerCAmelCase					) > 0:
  em /= len(_lowerCAmelCase					)
 return {"em": em}



def 		A_    (					_lowerCAmelCase					)						->			List[Any]:
 return model_prefix.startswith("rag"					)



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			List[str]:
 UpperCamelCase     : int	     =       {p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 UpperCamelCase     : str	     =       "dropout_rate"
 for p in extra_params:
  if getattr(_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					):
   if not hasattr(_lowerCAmelCase							,       _lowerCAmelCase					) and not hasattr(_lowerCAmelCase							,       equivalent_param[p]					):
    logger.info("config doesn't have a `{}` attribute".format(_lowerCAmelCase					)					)
    delattr(_lowerCAmelCase							,       _lowerCAmelCase					)
    continue
   UpperCamelCase     : str	     =       p if hasattr(_lowerCAmelCase							,       _lowerCAmelCase					) else equivalent_param[p]
   setattr(_lowerCAmelCase							,       _lowerCAmelCase							,       getattr(_lowerCAmelCase							,       _lowerCAmelCase					)					)
   delattr(_lowerCAmelCase							,       _lowerCAmelCase					)
 return hparams, config







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


__lowerCamelCase	:					List[str]			  =					logging.get_logger(__name__)  # pylint: disable=invalid-name



class  A__       (	__snake_case      ):





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self	,							A_ = "auto"       ):
       '''simple docstring'''




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





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       self.enable_attention_slicing(A_       )





      @torch.no_grad()
      def __call__(			self	,							A_	,							A_ = 512	,							A_ = 512	,							A_ = 50	,							A_ = 7.5	,							A_ = None	,							A_ = 1	,							A_ = 0.0	,							A_ = None	,							A_ = None	,							A_ = "pil"	,							A_ = True	,							A_ = None	,							A_ = 1	,							A_ = None	,							**A_	,							):
       '''simple docstring'''




       if isinstance(A_	,							A_       ):
        UpperCamelCase     : Any	     =       1
       elif isinstance(A_	,							A_       ):
        UpperCamelCase     : Optional[Any]	     =       len(A_       )
       else:
        raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(A_       )}"""       )

       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(A_	,							A_       ) or callback_steps <= 0)
       ):
        raise ValueError(
            F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
            F""" {type(A_       )}."""       )

       # get prompt text embeddings
       UpperCamelCase     : int	     =       self.tokenizer(
           A_	,							padding="max_length"	,							max_length=self.tokenizer.model_max_length	,							return_tensors="pt"	,							)
       UpperCamelCase     : Optional[Any]	     =       text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
        UpperCamelCase     : List[str]	     =       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     : Any	     =       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     : int	     =       text_embeddings.repeat(1	,							A_	,							1       )
       UpperCamelCase     : str	     =       text_embeddings.view(bs_embed * num_images_per_prompt	,							A_	,							-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[int]	     =       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     : Tuple	     =       [""]
        elif type(A_       ) is not type(A_       ):
         raise TypeError(
             F"""`negative_prompt` should be the same type to `prompt`, but got {type(A_       )} !="""
             F""" {type(A_       )}."""       )
        elif isinstance(A_	,							A_       ):
         UpperCamelCase     : Optional[int]	     =       [negative_prompt]
        elif batch_size != len(A_       ):
         raise ValueError(
             F"""`negative_prompt`: {negative_prompt} has batch size {len(A_       )}, but `prompt`:"""
             F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
             " the batch size of `prompt`."       )
        else:
         UpperCamelCase     : Union[str, Any]	     =       negative_prompt

        UpperCamelCase     : Tuple	     =       text_input_ids.shape[-1]
        UpperCamelCase     : str	     =       self.tokenizer(
            A_	,							padding="max_length"	,							max_length=A_	,							truncation=A_	,							return_tensors="pt"	,							)
        UpperCamelCase     : str	     =       self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

        # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
        UpperCamelCase     : Optional[int]	     =       uncond_embeddings.shape[1]
        UpperCamelCase     : Optional[Any]	     =       uncond_embeddings.repeat(A_	,							A_	,							1       )
        UpperCamelCase     : Dict	     =       uncond_embeddings.view(batch_size * num_images_per_prompt	,							A_	,							-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     : Union[str, Any]	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCamelCase     : Tuple	     =       (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
       UpperCamelCase     : Any	     =       text_embeddings.dtype
       if latents is None:
        if self.device.type == "mps":
         # randn does not exist on mps
         UpperCamelCase     : Optional[Any]	     =       torch.randn(
             A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(self.device       )
         UpperCamelCase     : Dict	     =       torch.randn(A_	,							generator=A_	,							device="cpu"	,							dtype=A_       ).to(
             self.device       )
        else:
         UpperCamelCase     : Tuple	     =       torch.randn(
             A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
         UpperCamelCase     : str	     =       torch.randn(A_	,							generator=A_	,							device=self.device	,							dtype=A_       )
       else:
        if latents_reference.shape != latents_shape:
         raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""       )
        UpperCamelCase     : List[Any]	     =       latents_reference.to(self.device       )
        UpperCamelCase     : Optional[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     : List[str]	     =       (latents_shape[3] - latents_shape_reference[3]) // 2
       UpperCamelCase     : str	     =       (latents_shape[2] - latents_shape_reference[2]) // 2
       UpperCamelCase     : List[Any]	     =       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     : str	     =       0 if dx < 0 else dx
       UpperCamelCase     : Union[str, Any]	     =       0 if dy < 0 else dy
       UpperCamelCase     : Union[str, Any]	     =       max(-dx	,							0       )
       UpperCamelCase     : Tuple	     =       max(-dy	,							0       )
       # import pdb
       # pdb.set_trace()
       UpperCamelCase     : Optional[Any]	     =       latents_reference[:, :, dy : dy + h, dx : dx + w]

       # set timesteps
       self.scheduler.set_timesteps(A_       )

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCamelCase     : Union[str, Any]	     =       self.scheduler.timesteps.to(self.device       )

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

       for i, t in enumerate(self.progress_bar(A_       )       ):
        # 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(A_	,							A_       )

        # predict the noise residual
        UpperCamelCase     : List[str]	     =       self.unet(A_	,							A_	,							encoder_hidden_states=A_       ).sample

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

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

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

       UpperCamelCase     : int	     =       1 / 0.1_82_15 * latents
       UpperCamelCase     : Tuple	     =       self.vae.decode(A_       ).sample

       UpperCamelCase     : 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     : Tuple	     =       image.cpu().permute(0	,							2	,							3	,							1       ).float().numpy()

       if self.safety_checker is not None:
        UpperCamelCase     : int	     =       self.feature_extractor(self.numpy_to_pil(A_       )	,							return_tensors="pt"       ).to(
            self.device       )
        UpperCamelCase     ,   UpperCamelCase     : int	     =       self.safety_checker(
            images=A_	,							clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype       )       )
       else:
        UpperCamelCase     : int	     =       None

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

       if not return_dict:
        return (image, has_nsfw_concept)

       return StableDiffusionPipelineOutput(images=A_	,							nsfw_content_detected=A_       )
