




"""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     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=True					,							UpperCamelCase="pt"				)      ->      List[Any]:


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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=None					,							)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			Any												=       input_ids.ne(UpperCamelCase				).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__					(						__magic_name__ ):


			def __init__(   self   :     Tuple   ,	UpperCamelCase_   :     str   ,	UpperCamelCase_   :     List[str]   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     Any   ,	UpperCamelCase_   :     Union[str, Any]="train"   ,	UpperCamelCase_   :     Any=None   ,	UpperCamelCase_   :     List[Any]=None   ,	UpperCamelCase_   :     Optional[int]=None   ,	UpperCamelCase_   :     Optional[int]=""   ,	):


						"""simple docstring"""




						super().__init__()
						__UpperCAmelCase						:			List[Any]												=       Path(UpperCamelCase_).joinpath(type_path + ".source")
						__UpperCAmelCase						:			List[str]												=       Path(UpperCamelCase_).joinpath(type_path + ".target")
						__UpperCAmelCase						:			int												=       self.get_char_lens(self.src_file)
						__UpperCAmelCase						:			Optional[int]												=       max_source_length
						__UpperCAmelCase						:			Dict												=       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						:			List[str]												=       self.src_lens[:n_obs]
						__UpperCAmelCase						:			Any												=       src_lang
						__UpperCAmelCase						:			List[Any]												=       tgt_lang


			def __len__(   self   :     str):


						"""simple docstring"""




						return len(self.src_lens)


			def __getitem__(   self   :     Optional[Any]   ,	UpperCamelCase_   :     Any):


						"""simple docstring"""




						__UpperCAmelCase						:			int												=       index + 1  # linecache starts at 1
						__UpperCAmelCase						:			Any												=       self.prefix + linecache.getline(str(self.src_file)   ,	UpperCamelCase_).rstrip("\n")
						__UpperCAmelCase						:			Dict												=       linecache.getline(str(self.tgt_file)   ,	UpperCamelCase_).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   ,	UpperCamelCase_):
									source_line += self.tokenizer.eos_token
									tgt_line += self.tokenizer.eos_token

						# Pad source and target to the right
						__UpperCAmelCase						:			Dict												=       (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer   ,	UpperCamelCase_) else self.tokenizer
						)
						__UpperCAmelCase						:			Dict												=       self.tokenizer.generator if isinstance(self.tokenizer   ,	UpperCamelCase_) else self.tokenizer

						__UpperCAmelCase						:			List[str]												=       encode_line(UpperCamelCase_   ,	UpperCamelCase_   ,	self.max_source_length   ,	"right")
						__UpperCAmelCase						:			List[str]												=       encode_line(UpperCamelCase_   ,	UpperCamelCase_   ,	self.max_target_length   ,	"right")

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


			@staticmethod
			def    a_  (   UpperCamelCase_   :     Tuple):


						"""simple docstring"""




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


			def    a_  (   self   :     Dict   ,	UpperCamelCase_   :     Union[str, Any]):


						"""simple docstring"""




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






A				=							getLogger(__name__)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""
			return list(itertools.chain.from_iterable(UpperCamelCase				)				)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      None:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       get_git_info()
			save_json(UpperCamelCase					,							os.path.join(UpperCamelCase					,							"git_log.json"				)				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=4					,							**UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			with open(UpperCamelCase					,							"w"				) as f:
						json.dump(UpperCamelCase					,							UpperCamelCase					,							indent=UpperCamelCase					,							**UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      int:


			"""simple docstring"""
			with open(UpperCamelCase				) as f:
						return json.load(UpperCamelCase				)

def        _UpperCamelCase     (      )      ->      Union[str, Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			List[str]												=       git.Repo(search_parent_directories=UpperCamelCase				)
			__UpperCAmelCase						:			Any												=       {
			    "repo_id": str(UpperCamelCase				),
			    "repo_sha": str(repo.head.object.hexsha				),
			    "repo_branch": str(repo.active_branch				),
			    "hostname": str(socket.gethostname()				),
			}
			return repo_infos

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      List:


			"""simple docstring"""
			return list(map(UpperCamelCase					,							UpperCamelCase				)				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			with open(UpperCamelCase					,							"wb"				) as f:
						return pickle.dump(UpperCamelCase					,							UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Optional[int]:


			"""simple docstring"""

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

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase				)				)				)				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[int]												=       normalize_answer(UpperCamelCase				).split()
			__UpperCAmelCase						:			Tuple												=       normalize_answer(UpperCamelCase				).split()
			__UpperCAmelCase						:			Union[str, Any]												=       Counter(UpperCamelCase				) & Counter(UpperCamelCase				)
			__UpperCAmelCase						:			str												=       sum(common.values()				)
			if num_same == 0:
						return 0
			__UpperCAmelCase						:			Any												=       1.0 * num_same / len(UpperCamelCase				)
			__UpperCAmelCase						:			List[str]												=       1.0 * num_same / len(UpperCamelCase				)
			__UpperCAmelCase						:			Tuple												=       (2 * precision * recall) / (precision + recall)
			return fa

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			return normalize_answer(UpperCamelCase				) == normalize_answer(UpperCamelCase				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      Dict:


			"""simple docstring"""
			assert len(UpperCamelCase				) == len(UpperCamelCase				)
			__UpperCAmelCase						:			Union[str, Any]												=       0
			for hypo, pred in zip(UpperCamelCase					,							UpperCamelCase				):
						em += exact_match_score(UpperCamelCase					,							UpperCamelCase				)
			if len(UpperCamelCase				) > 0:
						em /= len(UpperCamelCase				)
			return {"em": em}

def        _UpperCamelCase     (      UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			return model_prefix.startswith("rag"				)





def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Optional[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, 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(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				):
									if not hasattr(UpperCamelCase					,							UpperCamelCase				) and not hasattr(UpperCamelCase					,							equivalent_param[p]				):
												logger.info("config doesn't have a `{}` attribute".format(UpperCamelCase				)				)
												delattr(UpperCamelCase					,							UpperCamelCase				)
												continue
									__UpperCAmelCase						:			List[Any]												=       p if hasattr(UpperCamelCase					,							UpperCamelCase				) else equivalent_param[p]
									setattr(UpperCamelCase					,							UpperCamelCase					,							getattr(UpperCamelCase					,							UpperCamelCase				)				)
									delattr(UpperCamelCase					,							UpperCamelCase				)
			return hparams, config










'''simple docstring'''

from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .multicontrolnet import MultiControlNetModel
      from .pipeline_controlnet import StableDiffusionControlNetPipeline
      from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
      from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
      from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline






