



"""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_    ( A_   :						Tuple,							A_   :						List[str],							A_   :						Optional[Any],							A_   :						Dict,							A_   :						Dict=True,							A_   :						int="pt"		):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					{'''add_prefix_space''': True} if isinstance(A_,							A_		) and not line.startswith(''' '''		) else {}
       _lowerCamelCase		:							Union[str, Any]            =					padding_side
       return tokenizer(
           [line],							max_length=A_,							padding='''max_length''' if pad_to_max_length else None,							truncation=A_,							return_tensors=A_,							add_special_tokens=A_,							**A_,							)






def 			snake_case_    ( A_   :						Any,							A_   :						Optional[int],							A_   :						List[Any]=None,							):


       '''simple docstring'''





       _lowerCamelCase		:							Optional[int]            =					input_ids.ne(A_		).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			(      _lowercase):




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






         """simple docstring"""






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




  def __len__(					self	:		int						):






         """simple docstring"""






         return len(self.src_lens						)




  def __getitem__(					self	:		Dict		,   __lowerCAmelCase	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							str            =					index + 1  # linecache starts at 1
         _lowerCamelCase		:							Union[str, Any]            =					self.prefix + linecache.getline(str(self.src_file						)		,   __lowerCAmelCase						).rstrip('''\n'''						)
         _lowerCamelCase		:							Optional[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
         _lowerCamelCase		:							Optional[int]            =					(
             self.tokenizer.question_encoder if isinstance(self.tokenizer		,   __lowerCAmelCase						) else self.tokenizer
         )
         _lowerCamelCase		:							Union[str, Any]            =					self.tokenizer.generator if isinstance(self.tokenizer		,   __lowerCAmelCase						) else self.tokenizer

         _lowerCamelCase		:							List[str]            =					encode_line(__lowerCAmelCase		,   __lowerCAmelCase		,   self.max_source_length		,   '''right'''						)
         _lowerCamelCase		:							List[str]            =					encode_line(__lowerCAmelCase		,   __lowerCAmelCase		,   self.max_target_length		,   '''right'''						)

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




  @staticmethod
  def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase	:		str						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   __lowerCAmelCase	:		Any						):






         """simple docstring"""






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


lowerCAmelCase__              =							getLogger(__name__)






def 			snake_case_    ( A_   :						List[List]		):


       '''simple docstring'''





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






def 			snake_case_    ( A_   :						str		):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					get_git_info()
       save_json(A_,							os.path.join(A_,							'''git_log.json'''		)		)






def 			snake_case_    ( A_   :						str,							A_   :						Union[str, Any],							A_   :						int=4,							**A_   :						Optional[int]		):


       '''simple docstring'''





       with open(A_,							'''w'''		) as f:
              json.dump(A_,							A_,							indent=A_,							**A_		)






def 			snake_case_    ( A_   :						Any		):


       '''simple docstring'''





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






def 			snake_case_    ( ):


       '''simple docstring'''





       _lowerCamelCase		:							List[str]            =					git.Repo(search_parent_directories=A_		)
       _lowerCamelCase		:							str            =					{
           '''repo_id''': str(A_		),
           '''repo_sha''': str(repo.head.object.hexsha		),
           '''repo_branch''': str(repo.active_branch		),
           '''hostname''': str(socket.gethostname()		),
       }
       return repo_infos






def 			snake_case_    ( A_   :						Callable,							A_   :						Iterable		):


       '''simple docstring'''





       return list(map(A_,							A_		)		)






def 			snake_case_    ( A_   :						str,							A_   :						Tuple		):


       '''simple docstring'''





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






def 			snake_case_    ( A_   :						List[str]		):


       '''simple docstring'''





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

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

       def remove_punc(A_   :						List[Any]		):
              _lowerCamelCase		:							Any            =					set(string.punctuation		)
              return "".join(ch for ch in text if ch not in exclude		)

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

       return white_space_fix(remove_articles(remove_punc(lower(A_		)		)		)		)






def 			snake_case_    ( A_   :						int,							A_   :						List[Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							str            =					normalize_answer(A_		).split()
       _lowerCamelCase		:							int            =					normalize_answer(A_		).split()
       _lowerCamelCase		:							str            =					Counter(A_		) & Counter(A_		)
       _lowerCamelCase		:							Any            =					sum(common.values()		)
       if num_same == 0:
              return 0
       _lowerCamelCase		:							int            =					1.0 * num_same / len(A_		)
       _lowerCamelCase		:							str            =					1.0 * num_same / len(A_		)
       _lowerCamelCase		:							List[Any]            =					(2 * precision * recall) / (precision + recall)
       return fa






def 			snake_case_    ( A_   :						Dict,							A_   :						str		):


       '''simple docstring'''





       return normalize_answer(A_		) == normalize_answer(A_		)






def 			snake_case_    ( A_   :						List[str],							A_   :						List[str]		):


       '''simple docstring'''





       assert len(A_		) == len(A_		)
       _lowerCamelCase		:							Optional[Any]            =					0
       for hypo, pred in zip(A_,							A_		):
              em += exact_match_score(A_,							A_		)
       if len(A_		) > 0:
              em /= len(A_		)
       return {"em": em}






def 			snake_case_    ( A_   :						Optional[int]		):


       '''simple docstring'''





       return model_prefix.startswith('''rag'''		)







def 			snake_case_    ( A_   :						Dict,							A_   :						int,							A_   :						List[Any]		):


       '''simple docstring'''





       _lowerCamelCase		:							Dict            =					{p: p for p in extra_params}
       # T5 models don't have `dropout` param, they have `dropout_rate` instead
       _lowerCamelCase		:							Tuple            =					'''dropout_rate'''
       for p in extra_params:
              if getattr(A_,							A_,							A_		):
                     if not hasattr(A_,							A_		) and not hasattr(A_,							equivalent_param[p]		):
                            logger.info('''config doesn\'t have a `{}` attribute'''.format(A_		)		)
                            delattr(A_,							A_		)
                            continue
                     _lowerCamelCase		:							Union[str, Any]            =					p if hasattr(A_,							A_		) else equivalent_param[p]
                     setattr(A_,							A_,							getattr(A_,							A_		)		)
                     delattr(A_,							A_		)
       return hparams, config








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 PoolFormerImageProcessor

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0.9  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						Optional[int]:
         UpperCAmelCase_     :				int  =     size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_     :				List[str]  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_     :				Dict  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     min_resolution
         UpperCAmelCase_     :				Tuple  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize_and_center_crop
         UpperCAmelCase_     :				Tuple  =     size
         UpperCAmelCase_     :				List[str]  =     crop_pct
         UpperCAmelCase_     :				List[str]  =     crop_size
         UpperCAmelCase_     :				Any  =     do_normalize
         UpperCAmelCase_     :				str  =     image_mean
         UpperCAmelCase_     :				List[Any]  =     image_std





    def 				a__ ( self    )					->						str:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = PoolFormerImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				str  =     PoolFormerImageProcessingTester(self    )





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





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_resize_and_center_crop'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''size'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''crop_pct'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_normalize'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_mean'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_std'''    )    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 30}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 30, '''width''': 30}    )

         UpperCAmelCase_     :				Optional[Any]  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42  ,crop_size=84    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 84, '''width''': 84}    )





    def 				a__ ( self    )					->						Optional[int]:
         pass





    def 				a__ ( self    )					->						Dict:
         # Initialize image_processing
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				int  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,Image.Image    )

         # Test not batched input
         UpperCAmelCase_     :				Optional[int]  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values

         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				Union[str, Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





    def 				a__ ( self    )					->						List[Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Any  =     self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_     :				List[str]  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE  ,numpify=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,np.ndarray    )

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)





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

         # Test not batched input
         UpperCAmelCase_     :				Tuple  =     image_processing(image_inputs[0]  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)

         # Test batched
         UpperCAmelCase_     :				List[Any]  =     image_processing(_SCREAMING_SNAKE_CASE  ,return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape  ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )  ,)