
from typing import List, Optional, Union

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




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      ['''image_processor''', '''tokenizer''']
  SCREAMING_SNAKE_CASE__         =      '''BlipImageProcessor'''
  SCREAMING_SNAKE_CASE__         =      '''AutoTokenizer'''







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    False
       super().__init__(_snake_case	,      _snake_case						)
       UpperCamelCase__				=    self.image_processor







  def __call__(self	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = 0	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = None	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = False	,      SCREAMING_SNAKE_CASE_ = True	,      SCREAMING_SNAKE_CASE_ = None	,      **SCREAMING_SNAKE_CASE_	,      ):


       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:
            UpperCamelCase__				=    self.tokenizer
            UpperCamelCase__				=    self.tokenizer(
                text=_snake_case	,      add_special_tokens=_snake_case	,      padding=_snake_case	,      truncation=_snake_case	,      max_length=_snake_case	,      stride=_snake_case	,      pad_to_multiple_of=_snake_case	,      return_attention_mask=_snake_case	,      return_overflowing_tokens=_snake_case	,      return_special_tokens_mask=_snake_case	,      return_offsets_mapping=_snake_case	,      return_token_type_ids=_snake_case	,      return_length=_snake_case	,      verbose=_snake_case	,      return_tensors=_snake_case	,      **_snake_case	,      )
            return text_encoding

       # add pixel_values
       UpperCamelCase__				=    self.image_processor(_snake_case	,      return_tensors=_snake_case						)

       if text is not None:
            UpperCamelCase__				=    self.tokenizer(
                text=_snake_case	,      add_special_tokens=_snake_case	,      padding=_snake_case	,      truncation=_snake_case	,      max_length=_snake_case	,      stride=_snake_case	,      pad_to_multiple_of=_snake_case	,      return_attention_mask=_snake_case	,      return_overflowing_tokens=_snake_case	,      return_special_tokens_mask=_snake_case	,      return_offsets_mapping=_snake_case	,      return_token_type_ids=_snake_case	,      return_length=_snake_case	,      verbose=_snake_case	,      return_tensors=_snake_case	,      **_snake_case	,      )
       else:
            UpperCamelCase__				=    None

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

       return encoding_image_processor







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.batch_decode(*_snake_case	,      **_snake_case						)







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       return self.tokenizer.decode(*_snake_case	,      **_snake_case						)







  @property
  # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.tokenizer.model_input_names
       UpperCamelCase__				=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows






def 						__magic_name__			(   __a      :	Any						):  # picklable for multiprocessing



     '''simple docstring'''




     return i + 1






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def 						__magic_name__			(   ):



     '''simple docstring'''




     with parallel_backend("""spark"""						):
          assert ParallelBackendConfig.backend_name == "spark"

     UpperCamelCase__				=    [1, 2, 3]
     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=2						)

     with pytest.raises(__a						):
          with parallel_backend("""unsupported backend"""						):
               map_nested(__a	,		__a	,		num_proc=-1						)






@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""	,		[2, -1]						)
def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    [1, 2]
     UpperCamelCase__				=    {"""a""": 1, """b""": 2}
     UpperCamelCase__				=    {"""a""": [1, 2], """b""": [3, 4]}
     UpperCamelCase__				=    {"""a""": {"""1""": 1}, """b""": 2}
     UpperCamelCase__				=    {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
     UpperCamelCase__				=    [2, 3]
     UpperCamelCase__				=    {"""a""": 2, """b""": 3}
     UpperCamelCase__				=    {"""a""": [2, 3], """b""": [4, 5]}
     UpperCamelCase__				=    {"""a""": {"""1""": 2}, """b""": 3}
     UpperCamelCase__				=    {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}

     with parallel_backend("""spark"""						):
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa
          assert map_nested(__a	,		__a	,		num_proc=__a						) == expected_map_nested_sa





