


"""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 __lowercase     (      lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      List[str]=True						,					lowerCamelCase_  :      Any="pt"  ):
				SCREAMING_SNAKE_CASE__     							=				{"add_prefix_space": True} if isinstance(lowerCamelCase_						,					lowerCamelCase_  ) and not line.startswith(" "  ) else {}
				SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      Optional[Any]=None						,					):
				SCREAMING_SNAKE_CASE__     							=				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 			lowerCamelCase_		(			a__   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__="train"       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=""       ,		):
								super().__init__()
								SCREAMING_SNAKE_CASE__     							=				Path(lowerCamelCase_     ).joinpath(type_path + ".source"     )
								SCREAMING_SNAKE_CASE__     							=				Path(lowerCamelCase_     ).joinpath(type_path + ".target"     )
								SCREAMING_SNAKE_CASE__     							=				self.get_char_lens(self.src_file     )
								SCREAMING_SNAKE_CASE__     							=				max_source_length
								SCREAMING_SNAKE_CASE__     							=				max_target_length
								assert min(self.src_lens     ) > 0, f'''found empty line in {self.src_file}'''
								SCREAMING_SNAKE_CASE__     							=				tokenizer
								SCREAMING_SNAKE_CASE__     							=				prefix
								if n_obs is not None:
												SCREAMING_SNAKE_CASE__     							=				self.src_lens[:n_obs]
								SCREAMING_SNAKE_CASE__     							=				src_lang
								SCREAMING_SNAKE_CASE__     							=				tgt_lang



				def __len__(						self     ):
								return len(self.src_lens     )



				def __getitem__(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				index + 1  # linecache starts at 1
								SCREAMING_SNAKE_CASE__     							=				self.prefix + linecache.getline(str(self.src_file     )       ,		lowerCamelCase_     ).rstrip("\n"     )
								SCREAMING_SNAKE_CASE__     							=				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
								SCREAMING_SNAKE_CASE__     							=				(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer       ,		lowerCamelCase_     ) else self.tokenizer
								)
								SCREAMING_SNAKE_CASE__     							=				self.tokenizer.generator if isinstance(self.tokenizer       ,		lowerCamelCase_     ) else self.tokenizer

								SCREAMING_SNAKE_CASE__     							=				encode_line(lowerCamelCase_       ,		lowerCamelCase_       ,		self.max_source_length       ,		"right"     )
								SCREAMING_SNAKE_CASE__     							=				encode_line(lowerCamelCase_       ,		lowerCamelCase_       ,		self.max_target_length       ,		"right"     )

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



				@staticmethod
				def   lowerCAmelCase__		(						UpperCAmelCase__     ):
								return [len(lowerCamelCase_     ) for x in Path(lowerCamelCase_     ).open().readlines()]






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								SCREAMING_SNAKE_CASE__     							=				torch.stack([x["input_ids"] for x in batch]     )
								SCREAMING_SNAKE_CASE__     							=				torch.stack([x["attention_mask"] for x in batch]     )
								SCREAMING_SNAKE_CASE__     							=				torch.stack([x["decoder_input_ids"] for x in batch]     )
								SCREAMING_SNAKE_CASE__     							=				(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer       ,		lowerCamelCase_     )
								    else self.tokenizer.pad_token_id
								)
								SCREAMING_SNAKE_CASE__     							=				(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer       ,		lowerCamelCase_     )
								    else self.tokenizer.pad_token_id
								)
								SCREAMING_SNAKE_CASE__     							=				trim_batch(lowerCamelCase_       ,		lowerCamelCase_     )
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				trim_batch(lowerCamelCase_       ,		lowerCamelCase_       ,		attention_mask=lowerCamelCase_     )
								SCREAMING_SNAKE_CASE__     							=				{
								    "input_ids": source_ids,
								    "attention_mask": source_mask,
								    "decoder_input_ids": y,
								}
								return batch


_lowerCamelCase     =  getLogger(__name__)


def __lowercase     (      lowerCamelCase_  :      Any  ):
				return list(itertools.chain.from_iterable(lowerCamelCase_  )  )


def __lowercase     (      lowerCamelCase_  :      Dict  ):
				SCREAMING_SNAKE_CASE__     							=				get_git_info()
				save_json(lowerCamelCase_						,					os.path.join(lowerCamelCase_						,					"git_log.json"  )  )


def __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      str						,					lowerCamelCase_  :      Optional[int]=4						,					**lowerCamelCase_  :      Optional[Any]  ):
				with open(lowerCamelCase_						,					"w"  ) as f:
								json.dump(lowerCamelCase_						,					lowerCamelCase_						,					indent=lowerCamelCase_						,					**lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      Union[str, Any]  ):
				with open(lowerCamelCase_  ) as f:
								return json.load(lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				git.Repo(search_parent_directories=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				{
				    "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 __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      List[Any]  ):
				return list(map(lowerCamelCase_						,					lowerCamelCase_  )  )


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      Optional[Any]  ):
				with open(lowerCamelCase_						,					"wb"  ) as f:
								return pickle.dump(lowerCamelCase_						,					lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      Any  ):

				def remove_articles(lowerCamelCase_  :      Union[str, Any]  ):
								return re.sub(R"\b(a|an|the)\b"						,					" "						,					lowerCamelCase_  )

				def white_space_fix(lowerCamelCase_  :      Any  ):
								return " ".join(text.split()  )

				def remove_punc(lowerCamelCase_  :      Dict  ):
								SCREAMING_SNAKE_CASE__     							=				set(string.punctuation  )
								return "".join(ch for ch in text if ch not in exclude  )

				def lower(lowerCamelCase_  :      List[str]  ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase_  )  )  )  )


def __lowercase     (      lowerCamelCase_  :      Dict						,					lowerCamelCase_  :      Dict  ):
				SCREAMING_SNAKE_CASE__     							=				normalize_answer(lowerCamelCase_  ).split()
				SCREAMING_SNAKE_CASE__     							=				normalize_answer(lowerCamelCase_  ).split()
				SCREAMING_SNAKE_CASE__     							=				Counter(lowerCamelCase_  ) & Counter(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				sum(common.values()  )
				if num_same == 0:
								return 0
				SCREAMING_SNAKE_CASE__     							=				1.0 * num_same / len(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				1.0 * num_same / len(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				(2 * precision * recall) / (precision + recall)
				return fa


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Optional[Any]  ):
				return normalize_answer(lowerCamelCase_  ) == normalize_answer(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      Optional[int]  ):
				assert len(lowerCamelCase_  ) == len(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      Optional[int]  ):
				return model_prefix.startswith("rag"  )


def __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				{p: p for p in extra_params}
				# T5 models don't have `dropout` param, they have `dropout_rate` instead
				SCREAMING_SNAKE_CASE__     							=				"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
												SCREAMING_SNAKE_CASE__     							=				p if hasattr(lowerCamelCase_						,					lowerCamelCase_  ) else equivalent_param[p]
												setattr(lowerCamelCase_						,					lowerCamelCase_						,					getattr(lowerCamelCase_						,					lowerCamelCase_  )  )
												delattr(lowerCamelCase_						,					lowerCamelCase_  )
				return hparams, config








"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase     =  0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

								self.encoder.set_watermark("bits"       ,		self.watermark     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

								SCREAMING_SNAKE_CASE__     							=				(255 * (images / 2 + 0.5)).cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				torch.clamp(2 * (images / 255 - 0.5)       ,		min=-1.0       ,		max=1.0     )
								return images
