

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					:		Any ,   _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any] ,   _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[str]=True ,   _SCREAMING_SNAKE_CASE					:		int="pt"       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{'add_prefix_space': True} if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ) and not line.startswith(' '       ) else {}
    SCREAMING_SNAKE_CASE_									=						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					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		List[Any]=None ,   ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						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       __snake_case  (    lowerCAmelCase__       ):

  def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A="train"						,		_A=None						,		_A=None						,		_A=None						,		_A=""						,		):
      super().__init__()
      SCREAMING_SNAKE_CASE_									=						Path(_A).joinpath(type_path + '.source')
      SCREAMING_SNAKE_CASE_									=						Path(_A).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						,		_A):
      SCREAMING_SNAKE_CASE_									=						index + 1  # linecache starts at 1
      SCREAMING_SNAKE_CASE_									=						self.prefix + linecache.getline(str(self.src_file)						,		_A).rstrip('\n')
      SCREAMING_SNAKE_CASE_									=						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
      SCREAMING_SNAKE_CASE_									=						(
          self.tokenizer.question_encoder if isinstance(self.tokenizer						,		_A) else self.tokenizer
      )
      SCREAMING_SNAKE_CASE_									=						self.tokenizer.generator if isinstance(self.tokenizer						,		_A) else self.tokenizer

      SCREAMING_SNAKE_CASE_									=						encode_line(_A						,		_A						,		self.max_source_length						,		'right')
      SCREAMING_SNAKE_CASE_									=						encode_line(_A						,		_A						,		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__     (					_A):
      return [len(_A) for x in Path(_A).open().readlines()]


  def 						lowerCAmelCase__     (					self						,		_A):
      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						,		_A)
          else self.tokenizer.pad_token_id
      )
      SCREAMING_SNAKE_CASE_									=						(
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer						,		_A)
          else self.tokenizer.pad_token_id
      )
      SCREAMING_SNAKE_CASE_									=						trim_batch(_A						,		_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						trim_batch(_A						,		_A						,		attention_mask=_A)
      SCREAMING_SNAKE_CASE_									=						{
          'input_ids': source_ids,
          'attention_mask': source_mask,
          'decoder_input_ids': y,
      }
      return batch






UpperCamelCase__  :      Tuple			=    getLogger(__name__)


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[List]       ):





    """simple docstring"""


    return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_git_info()
    save_json(_SCREAMING_SNAKE_CASE ,   os.path.join(_SCREAMING_SNAKE_CASE ,   'git_log.json'       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		int=4 ,   **_SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    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					:		List[str]       ):





    """simple docstring"""


    with open(_SCREAMING_SNAKE_CASE       ) as f:
        return json.load(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						{
        '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					:		Callable ,   _SCREAMING_SNAKE_CASE					:		Iterable       ):





    """simple docstring"""


    return list(map(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[Any]       ):





    """simple docstring"""


    with open(_SCREAMING_SNAKE_CASE ,   'wb'       ) as f:
        return pickle.dump(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


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

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

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

    def lower(_SCREAMING_SNAKE_CASE					:		Optional[int]       ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE       )       )       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						normalize_answer(_SCREAMING_SNAKE_CASE       ).split()
    SCREAMING_SNAKE_CASE_									=						normalize_answer(_SCREAMING_SNAKE_CASE       ).split()
    SCREAMING_SNAKE_CASE_									=						Counter(_SCREAMING_SNAKE_CASE       ) & Counter(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						sum(common.values()       )
    if num_same == 0:
        return 0
    SCREAMING_SNAKE_CASE_									=						1.0 * num_same / len(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						1.0 * num_same / len(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						(2 * precision * recall) / (precision + recall)
    return fa


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int] ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    return normalize_answer(_SCREAMING_SNAKE_CASE       ) == normalize_answer(_SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[str] ,   _SCREAMING_SNAKE_CASE					:		List[str]       ):





    """simple docstring"""


    assert len(_SCREAMING_SNAKE_CASE       ) == len(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						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					:		Tuple       ):





    """simple docstring"""


    return model_prefix.startswith('rag'       )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    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(_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
            SCREAMING_SNAKE_CASE_									=						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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCamelCase__  :      Tuple			=    {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Tuple			=    [
           "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TrOCRForCausalLM",
           "TrOCRPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
       from .processing_trocr import TrOCRProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
       import sys

       UpperCamelCase__  :      str			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


