






"""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 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  Tuple		,					__snake_case			:  Optional[Any]=True		,					__snake_case			:  Optional[int]="pt"    ):





  '''simple docstring'''





  lowercase							    =       {'add_prefix_space': True} if isinstance(__snake_case		,					__snake_case    ) and not line.startswith(' '    ) else {}
  lowercase							    =       padding_side
  return tokenizer(
      [line]		,					max_length=__snake_case		,					padding='max_length' if pad_to_max_length else None		,					truncation=__snake_case		,					return_tensors=__snake_case		,					add_special_tokens=__snake_case		,					**__snake_case		,					)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple		,					__snake_case			:  List[str]		,					__snake_case			:  Optional[int]=None		,					):





  '''simple docstring'''





  lowercase							    =       input_ids.ne(__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     (   a_	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase="train"							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		_lowerCamelCase=""							,		):
      super().__init__()
      lowercase							    =       Path(_lowerCamelCase       ).joinpath(type_path + '.source'       )
      lowercase							    =       Path(_lowerCamelCase       ).joinpath(type_path + '.target'       )
      lowercase							    =       self.get_char_lens(self.src_file       )
      lowercase							    =       max_source_length
      lowercase							    =       max_target_length
      assert min(self.src_lens       ) > 0, F'found empty line in {self.src_file}'
      lowercase							    =       tokenizer
      lowercase							    =       prefix
      if n_obs is not None:
        lowercase							    =       self.src_lens[:n_obs]
      lowercase							    =       src_lang
      lowercase							    =       tgt_lang







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







    def __getitem__(			self							,		_lowerCamelCase       ):
      lowercase							    =       index + 1  # linecache starts at 1
      lowercase							    =       self.prefix + linecache.getline(str(self.src_file       )							,		_lowerCamelCase       ).rstrip('\n'       )
      lowercase							    =       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
      lowercase							    =       (
          self.tokenizer.question_encoder if isinstance(self.tokenizer							,		_lowerCamelCase       ) else self.tokenizer
      )
      lowercase							    =       self.tokenizer.generator if isinstance(self.tokenizer							,		_lowerCamelCase       ) else self.tokenizer

      lowercase							    =       encode_line(_lowerCamelCase							,		_lowerCamelCase							,		self.max_source_length							,		'right'       )
      lowercase							    =       encode_line(_lowerCamelCase							,		_lowerCamelCase							,		self.max_target_length							,		'right'       )

      lowercase							    =       source_inputs['input_ids'].squeeze()
      lowercase							    =       target_inputs['input_ids'].squeeze()
      lowercase							    =       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       ):
      lowercase							    =       torch.stack([x['input_ids'] for x in batch]       )
      lowercase							    =       torch.stack([x['attention_mask'] for x in batch]       )
      lowercase							    =       torch.stack([x['decoder_input_ids'] for x in batch]       )
      lowercase							    =       (
          self.tokenizer.generator.pad_token_id
          if isinstance(self.tokenizer							,		_lowerCamelCase       )
          else self.tokenizer.pad_token_id
      )
      lowercase							    =       (
          self.tokenizer.question_encoder.pad_token_id
          if isinstance(self.tokenizer							,		_lowerCamelCase       )
          else self.tokenizer.pad_token_id
      )
      lowercase							    =       trim_batch(_lowerCamelCase							,		_lowerCamelCase       )
      lowercase    ,     lowercase							    =       trim_batch(_lowerCamelCase							,		_lowerCamelCase							,		attention_mask=_lowerCamelCase       )
      lowercase							    =       {
          'input_ids': source_ids,
          'attention_mask': source_mask,
          'decoder_input_ids': y,
      }
      return batch


_UpperCamelCase		:  str						=		getLogger(__name__)
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[List]    ):





  '''simple docstring'''





  return list(itertools.chain.from_iterable(__snake_case    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       get_git_info()
  save_json(__snake_case		,					os.path.join(__snake_case		,					'git_log.json'    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Optional[Any]		,					__snake_case			:  int=4		,					**__snake_case			:  List[str]    ):





  '''simple docstring'''





  with open(__snake_case		,					'w'    ) as f:
    json.dump(__snake_case		,					__snake_case		,					indent=__snake_case		,					**__snake_case    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





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





  '''simple docstring'''





  lowercase							    =       git.Repo(search_parent_directories=__snake_case    )
  lowercase							    =       {
      'repo_id': str(__snake_case    ),
      'repo_sha': str(repo.head.object.hexsha    ),
      'repo_branch': str(repo.active_branch    ),
      'hostname': str(socket.gethostname()    ),
  }
  return repo_infos
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Callable		,					__snake_case			:  Iterable    ):





  '''simple docstring'''





  return list(map(__snake_case		,					__snake_case    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  with open(__snake_case		,					'wb'    ) as f:
    return pickle.dump(__snake_case		,					__snake_case    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





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

  def white_space_fix(__snake_case			:  Tuple    ):
    return " ".join(text.split()    )

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

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

  return white_space_fix(remove_articles(remove_punc(lower(__snake_case    )    )    )    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[Any]		,					__snake_case			:  str    ):





  '''simple docstring'''





  lowercase							    =       normalize_answer(__snake_case    ).split()
  lowercase							    =       normalize_answer(__snake_case    ).split()
  lowercase							    =       Counter(__snake_case    ) & Counter(__snake_case    )
  lowercase							    =       sum(common.values()    )
  if num_same == 0:
    return 0
  lowercase							    =       1.0 * num_same / len(__snake_case    )
  lowercase							    =       1.0 * num_same / len(__snake_case    )
  lowercase							    =       (2 * precision * recall) / (precision + recall)
  return fa
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  List[Any]    ):





  '''simple docstring'''





  return normalize_answer(__snake_case    ) == normalize_answer(__snake_case    )
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  assert len(__snake_case    ) == len(__snake_case    )
  lowercase							    =       0
  for hypo, pred in zip(__snake_case		,					__snake_case    ):
    em += exact_match_score(__snake_case		,					__snake_case    )
  if len(__snake_case    ) > 0:
    em /= len(__snake_case    )
  return {"em": em}
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  return model_prefix.startswith('rag'    )



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  Union[str, Any]		,					__snake_case			:  int    ):





  '''simple docstring'''





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










"""simple docstring"""



import unittest

import numpy as np

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

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 DPTImageProcessor

class        a     (   unittest.TestCase	):







    def __init__(			self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							    =       size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							    =       parent
      lowercase							    =       batch_size
      lowercase							    =       num_channels
      lowercase							    =       image_size
      lowercase							    =       min_resolution
      lowercase							    =       max_resolution
      lowercase							    =       do_resize
      lowercase							    =       size
      lowercase							    =       do_normalize
      lowercase							    =       image_mean
      lowercase							    =       image_std







    def 			UpperCamelCase_      (			self       ):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class        a     (   a_,				unittest.TestCase	):
    UpperCAmelCase_ :     Optional[int]	     =DPTImageProcessor if is_vision_available() else None







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       DPTImageProcessingTester(self       )







    @property
    def 			UpperCamelCase_      (			self       ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'       )       )
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict       )
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}       )

      lowercase							    =       self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2       )
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}       )







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PIL images
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image       )

      # Test not batched input
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random numpy tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray       )

      # Test not batched input
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def 			UpperCamelCase_      (			self       ):
      # Initialize image_processing
      lowercase							    =       self.image_processing_class(**self.image_processor_dict       )
      # create random PyTorch tensors
      lowercase							    =       prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase       )
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor       )

      # Test not batched input
      lowercase							    =       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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							    =       image_processing(_lowerCamelCase							,		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.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


