


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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=True      ,	SCREAMING_SNAKE_CASE__="pt"						):
 __lowerCamelCase		:							Dict		 =						{'add_prefix_space': True} if isinstance(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						) and not line.startswith(' '						) else {}
 __lowerCamelCase		:							Optional[int]		 =						padding_side
 return tokenizer(
     [line]      ,	max_length=SCREAMING_SNAKE_CASE_      ,	padding='max_length' if pad_to_max_length else None      ,	truncation=SCREAMING_SNAKE_CASE_      ,	return_tensors=SCREAMING_SNAKE_CASE_      ,	add_special_tokens=SCREAMING_SNAKE_CASE_      ,	**SCREAMING_SNAKE_CASE_      ,	)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	):
 __lowerCamelCase		:							Optional[Any]		 =						input_ids.ne(SCREAMING_SNAKE_CASE_						).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_ (	__UpperCamelCase   ):




      '''simple docstring'''


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




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




      def __getitem__(    self:	Union[str, Any]						,				a:	Any    ):
       __lowerCamelCase		:							Union[str, Any]		 =						index + 1  # linecache starts at 1
       __lowerCamelCase		:							Any		 =						self.prefix + linecache.getline(str(self.src_file    )						,				_lowercase    ).rstrip('\n'    )
       __lowerCamelCase		:							Optional[int]		 =						linecache.getline(str(self.tgt_file    )						,				_lowercase    ).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						,				_lowercase    ):
        source_line += self.tokenizer.eos_token
        tgt_line += self.tokenizer.eos_token

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

       __lowerCamelCase		:							List[str]		 =						encode_line(_lowercase						,				_lowercase						,				self.max_source_length						,				'right'    )
       __lowerCamelCase		:							Union[str, Any]		 =						encode_line(_lowercase						,				_lowercase						,				self.max_target_length						,				'right'    )

       __lowerCamelCase		:							Tuple		 =						source_inputs['input_ids'].squeeze()
       __lowerCamelCase		:							List[str]		 =						target_inputs['input_ids'].squeeze()
       __lowerCamelCase		:							str		 =						source_inputs['attention_mask'].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }




      @staticmethod
      def 					_snake_case     (    a:	Dict    ):
       return [len(_lowercase    ) for x in Path(_lowercase    ).open().readlines()]




      def 					_snake_case     (    self:	int						,				a:	int    ):
       __lowerCamelCase		:							Optional[Any]		 =						torch.stack([x['input_ids'] for x in batch]    )
       __lowerCamelCase		:							str		 =						torch.stack([x['attention_mask'] for x in batch]    )
       __lowerCamelCase		:							Any		 =						torch.stack([x['decoder_input_ids'] for x in batch]    )
       __lowerCamelCase		:							Tuple		 =						(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer						,				_lowercase    )
           else self.tokenizer.pad_token_id
       )
       __lowerCamelCase		:							Optional[Any]		 =						(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer						,				_lowercase    )
           else self.tokenizer.pad_token_id
       )
       __lowerCamelCase		:							Union[str, Any]		 =						trim_batch(_lowercase						,				_lowercase    )
       __lowerCamelCase ,					__lowerCamelCase		:							Any		 =						trim_batch(_lowercase						,				_lowercase						,				attention_mask=_lowercase    )
       __lowerCamelCase		:							Any		 =						{
           'input_ids': source_ids,
           'attention_mask': source_mask,
           'decoder_input_ids': y,
       }
       return batch



lowercase_	   =     getLogger(__name__)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return list(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE_						)						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						get_git_info()
 save_json(SCREAMING_SNAKE_CASE_      ,	os.path.join(SCREAMING_SNAKE_CASE_      ,	'git_log.json'						)						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=4      ,	**SCREAMING_SNAKE_CASE__						):






 with open(SCREAMING_SNAKE_CASE_      ,	'w'						) as f:
  json.dump(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_      ,	indent=SCREAMING_SNAKE_CASE_      ,	**SCREAMING_SNAKE_CASE_						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):






 with open(SCREAMING_SNAKE_CASE_						) as f:
  return json.load(SCREAMING_SNAKE_CASE_						)
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							str		 =						git.Repo(search_parent_directories=SCREAMING_SNAKE_CASE_						)
 __lowerCamelCase		:							Tuple		 =						{
     'repo_id': str(SCREAMING_SNAKE_CASE_						),
     'repo_sha': str(repo.head.object.hexsha						),
     'repo_branch': str(repo.active_branch						),
     'hostname': str(socket.gethostname()						),
 }
 return repo_infos
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return list(map(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						)						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):






 with open(SCREAMING_SNAKE_CASE_      ,	'wb'						) as f:
  return pickle.dump(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):






 def remove_articles(SCREAMING_SNAKE_CASE__						):
  return re.sub(r'\b(a|an|the)\b'      ,	' '      ,	SCREAMING_SNAKE_CASE_						)

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

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

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

 return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_						)						)						)						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							int		 =						normalize_answer(SCREAMING_SNAKE_CASE_						).split()
 __lowerCamelCase		:							List[str]		 =						normalize_answer(SCREAMING_SNAKE_CASE_						).split()
 __lowerCamelCase		:							str		 =						Counter(SCREAMING_SNAKE_CASE_						) & Counter(SCREAMING_SNAKE_CASE_						)
 __lowerCamelCase		:							List[str]		 =						sum(common.values()						)
 if num_same == 0:
  return 0
 __lowerCamelCase		:							Optional[Any]		 =						1.0 * num_same / len(SCREAMING_SNAKE_CASE_						)
 __lowerCamelCase		:							List[str]		 =						1.0 * num_same / len(SCREAMING_SNAKE_CASE_						)
 __lowerCamelCase		:							Dict		 =						(2 * precision * recall) / (precision + recall)
 return fa
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return normalize_answer(SCREAMING_SNAKE_CASE_						) == normalize_answer(SCREAMING_SNAKE_CASE_						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 assert len(SCREAMING_SNAKE_CASE_						) == len(SCREAMING_SNAKE_CASE_						)
 __lowerCamelCase		:							List[Any]		 =						0
 for hypo, pred in zip(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						):
  em += exact_match_score(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						)
 if len(SCREAMING_SNAKE_CASE_						) > 0:
  em /= len(SCREAMING_SNAKE_CASE_						)
 return {"em": em}
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 return model_prefix.startswith('rag'						)



def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						{p: p for p in extra_params}
 # T5 models don't have `dropout` param, they have `dropout_rate` instead
 __lowerCamelCase		:							List[Any]		 =						'dropout_rate'
 for p in extra_params:
  if getattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						):
   if not hasattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						) and not hasattr(SCREAMING_SNAKE_CASE_      ,	equivalent_param[p]						):
    logger.info('config doesn\'t have a `{}` attribute'.format(SCREAMING_SNAKE_CASE_						)						)
    delattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						)
    continue
   __lowerCamelCase		:							Optional[Any]		 =						p if hasattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						) else equivalent_param[p]
   setattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_      ,	getattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						)						)
   delattr(SCREAMING_SNAKE_CASE_      ,	SCREAMING_SNAKE_CASE_						)
 return hparams, config





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_	   =     {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['ConvNextFeatureExtractor']
       lowercase_	   =     ['ConvNextImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'ConvNextForImageClassification',
           'ConvNextModel',
           'ConvNextPreTrainedModel',
           'ConvNextBackbone',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFConvNextForImageClassification',
           'TFConvNextModel',
           'TFConvNextPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_convnext import ConvNextFeatureExtractor
              from .image_processing_convnext import ConvNextImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnext import (
                  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextBackbone,
                  ConvNextForImageClassification,
                  ConvNextModel,
                  ConvNextPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
       import sys

       lowercase_	   =     _LazyModule(__name__, globals()['__file__'], _import_structure)

