






'''simple docstring'''
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   str		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   Dict		       =    'Pix2StructImageProcessor'
    __lowerCamelCase:   Union[str, Any]		       =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(			self  :       str  ,     a  :       Dict  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     False
       super().__init__(a  ,     a			)




    def __call__(			self  :       Tuple  ,     a  :       int=None  ,     a  :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     a  :       bool = True  ,     a  :       Union[bool, str, PaddingStrategy] = False  ,     a  :       Union[bool, str, TruncationStrategy] = None  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = 2_0_4_8  ,     a  :       int = 0  ,     a  :       Optional[int] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = True  ,     a  :       Optional[Union[str, TensorType]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       if images is None and text is None:
          raise ValueError("You have to specify either images or text."			)

       # Get only text
       if images is None and not self.image_processor.is_vqa:
          lowercase_	:    Dict           =     self.tokenizer
          lowercase_	:    Tuple           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )
          return text_encoding

       if not self.image_processor.is_vqa:
          # add pixel_values
          lowercase_	:    Optional[int]           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     **a			)
       else:
          # add pixel_values and bbox
          lowercase_	:    Any           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     header_text=a  ,     **a			)

       if text is not None and not self.image_processor.is_vqa:
          lowercase_	:    int           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )

          if "attention_mask" in text_encoding:
             lowercase_	:    str           =     text_encoding.pop("attention_mask"			)
          if "input_ids" in text_encoding:
             lowercase_	:    Dict           =     text_encoding.pop("input_ids"			)
       else:
          lowercase_	:    str           =     None

       if text_encoding is not None:
          encoding_image_processor.update(a			)

       return encoding_image_processor




    def 				lowerCAmelCase__				(			self  :       Any  ,     *a  :       str  ,     **a  :       Tuple			):






       '''simple docstring'''






       return self.tokenizer.batch_decode(*a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     *a  :       Optional[int]  ,     **a  :       Any			):






       '''simple docstring'''






       return self.tokenizer.decode(*a  ,     **a			)





    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.tokenizer.model_input_names
       lowercase_	:    Dict           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




