



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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''image_processor''', '''tokenizer''']
   _UpperCAmelCase     	=			'''BridgeTowerImageProcessor'''
   _UpperCAmelCase     	=			('''RobertaTokenizer''', '''RobertaTokenizerFast''')

   def __init__(      self			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Any							)       ->				Optional[Any]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							,   __lowerCAmelCase							)

   def __call__(      self			:       Any							,   __lowerCAmelCase			:       Tuple							,   __lowerCAmelCase			:       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,   __lowerCAmelCase			:       bool = True							,   __lowerCAmelCase			:       Union[bool, str, PaddingStrategy] = False							,   __lowerCAmelCase			:       Union[bool, str, TruncationStrategy] = None							,   __lowerCAmelCase			:       Optional[int] = None							,   __lowerCAmelCase			:       int = 0							,   __lowerCAmelCase			:       Optional[int] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = True							,   __lowerCAmelCase			:       Optional[Union[str, TensorType]] = None							,   **__lowerCAmelCase			:       Any							,   )       ->				BatchEncoding:


    """simple docstring"""






    a						=		self.tokenizer(
        text=__lowerCAmelCase							,   add_special_tokens=__lowerCAmelCase							,   padding=__lowerCAmelCase							,   truncation=__lowerCAmelCase							,   max_length=__lowerCAmelCase							,   stride=__lowerCAmelCase							,   pad_to_multiple_of=__lowerCAmelCase							,   return_token_type_ids=__lowerCAmelCase							,   return_attention_mask=__lowerCAmelCase							,   return_overflowing_tokens=__lowerCAmelCase							,   return_special_tokens_mask=__lowerCAmelCase							,   return_offsets_mapping=__lowerCAmelCase							,   return_length=__lowerCAmelCase							,   verbose=__lowerCAmelCase							,   return_tensors=__lowerCAmelCase							,   **__lowerCAmelCase							,   )
    # add pixel_values + pixel_mask
    a						=		self.image_processor(
        __lowerCAmelCase							,   return_tensors=__lowerCAmelCase							,   do_normalize=__lowerCAmelCase							,   do_center_crop=__lowerCAmelCase							,   **__lowerCAmelCase							)
    encoding.update(__lowerCAmelCase							)

    return encoding

   def 		A							(      self			:       Optional[int]							,   *__lowerCAmelCase			:       Optional[int]							,   **__lowerCAmelCase			:       Optional[int]							)       ->				Dict:


    """simple docstring"""






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

   def 		A							(      self			:       str							,   *__lowerCAmelCase			:       int							,   **__lowerCAmelCase			:       Optional[int]							)       ->				Tuple:


    """simple docstring"""






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




   @property
   def 		A							(      self			:       int							)       ->				Dict:


    """simple docstring"""






    a						=		self.tokenizer.model_input_names
    a						=		self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

