






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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=True ,							__lowerCamelCase="pt"       ) ->			Tuple:

     '''simple docstring'''
     UpperCAmelCase__      :  Union[str, Any]				      =			{"""add_prefix_space""": True} if isinstance(__lowerCamelCase ,							__lowerCamelCase       ) and not line.startswith(""" """       ) else {}
     UpperCAmelCase__      :  Dict				      =			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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=None ,							) ->			Optional[int]:

     '''simple docstring'''
     UpperCAmelCase__      :  str				      =			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 				UpperCAmelCase_  (		__lowerCamelCase		):




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




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




     def __getitem__(				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[int]				      =			index + 1  # linecache starts at 1
          UpperCAmelCase__      :  List[Any]				      =			self.prefix + linecache.getline(str(self.src_file		)	,						_lowerCAmelCase		).rstrip("""\n"""		)
          UpperCAmelCase__      :  Union[str, Any]				      =			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
          UpperCAmelCase__      :  Optional[int]				      =			(
              self.tokenizer.question_encoder if isinstance(self.tokenizer	,						_lowerCAmelCase		) else self.tokenizer
          )
          UpperCAmelCase__      :  Optional[Any]				      =			self.tokenizer.generator if isinstance(self.tokenizer	,						_lowerCAmelCase		) else self.tokenizer

          UpperCAmelCase__      :  Optional[Any]				      =			encode_line(_lowerCAmelCase	,						_lowerCAmelCase	,						self.max_source_length	,						"""right"""		)
          UpperCAmelCase__      :  Any				      =			encode_line(_lowerCAmelCase	,						_lowerCAmelCase	,						self.max_target_length	,						"""right"""		)

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




     @staticmethod
     def   __UpperCAmelCase  (				_lowerCAmelCase		):
          return [len(_lowerCAmelCase		) for x in Path(_lowerCAmelCase		).open().readlines()]




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



SCREAMING_SNAKE_CASE__		:							Optional[int] 				=				getLogger(__name__)





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     return list(itertools.chain.from_iterable(__lowerCamelCase       )       )





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			None:

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





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=4 ,							**__lowerCamelCase       ) ->			Tuple:

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





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			int:

     '''simple docstring'''
     with open(__lowerCamelCase       ) as f:
          return json.load(__lowerCamelCase       )





def  _lowerCamelCase						(						) ->			str:

     '''simple docstring'''
     UpperCAmelCase__      :  Dict				      =			git.Repo(search_parent_directories=__lowerCamelCase       )
     UpperCAmelCase__      :  Tuple				      =			{
         """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  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List:

     '''simple docstring'''
     return list(map(__lowerCamelCase ,							__lowerCamelCase       )       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List[str]:

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





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''

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

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

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

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

     return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase       )       )       )       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[int]				      =			normalize_answer(__lowerCamelCase       ).split()
     UpperCAmelCase__      :  int				      =			normalize_answer(__lowerCamelCase       ).split()
     UpperCAmelCase__      :  List[Any]				      =			Counter(__lowerCamelCase       ) & Counter(__lowerCamelCase       )
     UpperCAmelCase__      :  Dict				      =			sum(common.values()       )
     if num_same == 0:
          return 0
     UpperCAmelCase__      :  Optional[Any]				      =			1.0 * num_same / len(__lowerCamelCase       )
     UpperCAmelCase__      :  List[Any]				      =			1.0 * num_same / len(__lowerCamelCase       )
     UpperCAmelCase__      :  str				      =			(2 * precision * recall) / (precision + recall)
     return fa





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			List[Any]:

     '''simple docstring'''
     return normalize_answer(__lowerCamelCase       ) == normalize_answer(__lowerCamelCase       )





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     assert len(__lowerCamelCase       ) == len(__lowerCamelCase       )
     UpperCAmelCase__      :  Optional[Any]				      =			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  _lowerCamelCase						(						__lowerCamelCase       ) ->			Optional[Any]:

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







def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			{p: p for p in extra_params}
     # T5 models don't have `dropout` param, they have `dropout_rate` instead
     UpperCAmelCase__      :  Any				      =			"""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
               UpperCAmelCase__      :  str				      =			p if hasattr(__lowerCamelCase ,							__lowerCamelCase       ) else equivalent_param[p]
               setattr(__lowerCamelCase ,							__lowerCamelCase ,							getattr(__lowerCamelCase ,							__lowerCamelCase       )       )
               delattr(__lowerCamelCase ,							__lowerCamelCase       )
     return hparams, config













"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor



class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def __init__(		self :							Any       , __a :							Any       , __a :							Optional[Any]=7       , __a :							Tuple=3       , __a :							int=18       , __a :							Optional[Any]=30       , __a :							Optional[Any]=400       , __a :							List[str]=True       , __a :							Any=32       , __a :							Tuple=True       , )		->					List[str]:
        _UpperCamelCase   :						List[str]    =							parent
        _UpperCamelCase   :						int    =							batch_size
        _UpperCamelCase   :						List[str]    =							num_channels
        _UpperCamelCase   :						Union[str, Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							min_resolution
        _UpperCamelCase   :						Union[str, Any]    =							max_resolution
        _UpperCamelCase   :						Optional[Any]    =							do_resize
        _UpperCamelCase   :						str    =							size_divisor
        _UpperCamelCase   :						Optional[Any]    =							do_rescale


  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        return {
            "do_resize": self.do_resize,
            "size_divisor": self.size_divisor,
            "do_rescale": self.do_rescale,
        }





@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Any						  =       GLPNImageProcessor if is_vision_available() else None

  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[Any]    =							GLPNImageProcessingTester(self						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        return self.image_processor_tester.prepare_image_processor_dict()

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Union[str, Any]:
        _UpperCamelCase   :						int    =							self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(__a       , "do_resize"						)						)
        self.assertTrue(hasattr(__a       , "size_divisor"						)						)
        self.assertTrue(hasattr(__a       , "resample"						)						)
        self.assertTrue(hasattr(__a       , "do_rescale"						)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[Any]:
        pass

  def  __SCREAMING_SNAKE_CASE					(		self :							int						)		->					Optional[Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[Any]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PIL images
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , Image.Image						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Dict:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        _UpperCamelCase   :						List[str]    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , numpify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , np.ndarray						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Dict    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Union[str, Any]:
        # Initialize image_processing
        _UpperCamelCase   :						Optional[int]    =							self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        _UpperCamelCase   :						Dict    =							prepare_image_inputs(self.image_processor_tester       , equal_resolution=__a       , torchify=__a						)
        for image in image_inputs:
              self.assertIsInstance(__a       , torch.Tensor						)

        # Test not batched input (GLPNImageProcessor doesn't support batching)
        _UpperCamelCase   :						Optional[int]    =							image_processing(image_inputs[0]       , return_tensors="pt"						).pixel_values
        self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
        self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)
