



"""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       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[str]			,		UpperCamelCase						:		Tuple=True			,		UpperCamelCase						:		List[str]="pt"						):
 UpperCAmelCase	:					Union[str, Any]	=	{"""add_prefix_space""": True} if isinstance(UpperCamelCase			,		UpperCamelCase						) and not line.startswith(""" """						) else {}
 UpperCAmelCase	:					List[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       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Any=None			,		):
 UpperCAmelCase	:					Tuple	=	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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="train"      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=""      ,     )  ->      List[Any]:





    '''simple docstring'''




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

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





    '''simple docstring'''




    return len(self.src_lens						)

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





    '''simple docstring'''




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

    # Pad source and target to the right
    UpperCAmelCase	:					Optional[Any]	=	(
        self.tokenizer.question_encoder if isinstance(self.tokenizer      ,     _SCREAMING_SNAKE_CASE						) else self.tokenizer
    )
    UpperCAmelCase	:					Dict	=	self.tokenizer.generator if isinstance(self.tokenizer      ,     _SCREAMING_SNAKE_CASE						) else self.tokenizer

    UpperCAmelCase	:					Union[str, Any]	=	encode_line(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     self.max_source_length      ,     """right"""						)
    UpperCAmelCase	:					Any	=	encode_line(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     self.max_target_length      ,     """right"""						)

    UpperCAmelCase	:					Any	=	source_inputs["""input_ids"""].squeeze()
    UpperCAmelCase	:					List[str]	=	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 	SCREAMING_SNAKE_CASE     (   _SCREAMING_SNAKE_CASE						)  ->      Any:





    '''simple docstring'''




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




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Dict[str, torch.Tensor]:





    '''simple docstring'''




    UpperCAmelCase	:					List[Any]	=	torch.stack([x["""input_ids"""] for x in batch]						)
    UpperCAmelCase	:					str	=	torch.stack([x["""attention_mask"""] for x in batch]						)
    UpperCAmelCase	:					Optional[Any]	=	torch.stack([x["""decoder_input_ids"""] for x in batch]						)
    UpperCAmelCase	:					Optional[Any]	=	(
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer      ,     _SCREAMING_SNAKE_CASE						)
        else self.tokenizer.pad_token_id
    )
    UpperCAmelCase	:					List[str]	=	(
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer      ,     _SCREAMING_SNAKE_CASE						)
        else self.tokenizer.pad_token_id
    )
    UpperCAmelCase	:					Tuple	=	trim_batch(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	,		UpperCAmelCase	:					Union[str, Any]	=	trim_batch(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     attention_mask=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Any	=	{
        """input_ids""": source_ids,
        """attention_mask""": source_mask,
        """decoder_input_ids""": y,
    }
    return batch
A:   Optional[int]								=		getLogger(__name__)

def       _snake_case						(						UpperCamelCase						:		List[List]						):
 return list(itertools.chain.from_iterable(UpperCamelCase						)						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					Optional[int]	=	get_git_info()
 save_json(UpperCamelCase			,		os.path.join(UpperCamelCase			,		"""git_log.json"""						)						)

def       _snake_case						(						UpperCamelCase						:		Union[str, Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Union[str, Any]=4			,		**UpperCamelCase						:		Optional[int]						):
 with open(UpperCamelCase			,		"""w"""						) as f:
  json.dump(UpperCamelCase			,		UpperCamelCase			,		indent=UpperCamelCase			,		**UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		Optional[int]						):
 with open(UpperCamelCase						) as f:
  return json.load(UpperCamelCase						)

def       _snake_case						(						):
 UpperCAmelCase	:					Union[str, Any]	=	git.Repo(search_parent_directories=UpperCamelCase						)
 UpperCAmelCase	:					Dict	=	{
     """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       _snake_case						(						UpperCamelCase						:		Callable			,		UpperCamelCase						:		Iterable						):
 return list(map(UpperCamelCase			,		UpperCamelCase						)						)

def       _snake_case						(						UpperCamelCase						:		Dict			,		UpperCamelCase						:		List[str]						):
 with open(UpperCamelCase			,		"""wb"""						) as f:
  return pickle.dump(UpperCamelCase			,		UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		Dict						):

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

 def white_space_fix(UpperCamelCase						:		Union[str, Any]						):
  return " ".join(text.split()						)

 def remove_punc(UpperCamelCase						:		List[str]						):
  UpperCAmelCase	:					Tuple	=	set(string.punctuation						)
  return "".join(ch for ch in text if ch not in exclude						)

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

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

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

def       _snake_case						(						UpperCamelCase						:		Any			,		UpperCamelCase						:		List[Any]						):
 return normalize_answer(UpperCamelCase						) == normalize_answer(UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		List[str]			,		UpperCamelCase						:		List[str]						):
 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       _snake_case						(						UpperCamelCase						:		str						):
 return model_prefix.startswith("""rag"""						)

def       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		int			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					Tuple	=	{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(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	:					Optional[int]	=	p if hasattr(UpperCamelCase			,		UpperCamelCase						) else equivalent_param[p]
   setattr(UpperCamelCase			,		UpperCamelCase			,		getattr(UpperCamelCase			,		UpperCamelCase						)						)
   delattr(UpperCamelCase			,		UpperCamelCase						)
 return hparams, config





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A:   List[str]								=		{
    "configuration_clipseg": [
        "CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPSegConfig",
        "CLIPSegTextConfig",
        "CLIPSegVisionConfig",
    ],
    "processing_clipseg": ["CLIPSegProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST",
          "CLIPSegModel",
          "CLIPSegPreTrainedModel",
          "CLIPSegTextModel",
          "CLIPSegVisionModel",
          "CLIPSegForImageSegmentation",
      ]

if TYPE_CHECKING:
      from .configuration_clipseg import (
          CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CLIPSegConfig,
          CLIPSegTextConfig,
          CLIPSegVisionConfig,
      )
      from .processing_clipseg import CLIPSegProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_clipseg import (
                CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                CLIPSegForImageSegmentation,
                CLIPSegModel,
                CLIPSegPreTrainedModel,
                CLIPSegTextModel,
                CLIPSegVisionModel,
            )

else:
      import sys

      A:   Tuple								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
