


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  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Optional[Any]      =['image_processor', 'tokenizer']
      UpperCamelCase__							:  List[Any]      ='BlipImageProcessor'
      UpperCamelCase__							:  Tuple      =('BertTokenizer', 'BertTokenizerFast')






      def __init__(					self						:     Tuple			,   lowercase_						:     int			,   lowercase_						:     Tuple )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=False
             super().__init__(lowercase_			,   lowercase_ )
             _lowerCamelCase       :		Any		=self.image_processor






      def __call__(					self						:     str			,   lowercase_						:     ImageInput = None			,   lowercase_						:     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,   lowercase_						:     bool = True			,   lowercase_						:     Union[bool, str, PaddingStrategy] = False			,   lowercase_						:     Union[bool, str, TruncationStrategy] = None			,   lowercase_						:     Optional[int] = None			,   lowercase_						:     int = 0			,   lowercase_						:     Optional[int] = None			,   lowercase_						:     Optional[bool] = None			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = False			,   lowercase_						:     bool = True			,   lowercase_						:     Optional[Union[str, TensorType]] = None			,   **lowercase_						:     List[Any]			,   )					->						BatchEncoding:
             """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:
                    _lowerCamelCase       :		Optional[Any]		=self.tokenizer
                    _lowerCamelCase       :		Optional[int]		=self.tokenizer(
                        text=lowercase_			,   add_special_tokens=lowercase_			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=lowercase_			,   stride=lowercase_			,   pad_to_multiple_of=lowercase_			,   return_attention_mask=lowercase_			,   return_overflowing_tokens=lowercase_			,   return_special_tokens_mask=lowercase_			,   return_offsets_mapping=lowercase_			,   return_token_type_ids=lowercase_			,   return_length=lowercase_			,   verbose=lowercase_			,   return_tensors=lowercase_			,   **lowercase_			,   )
                    return text_encoding

             # add pixel_values
             _lowerCamelCase       :		int		=self.image_processor(lowercase_			,   return_tensors=lowercase_ )

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

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

             return encoding_image_processor






      def        lowerCamelCase       (					self						:     Any			,   *lowercase_						:     Optional[int]			,   **lowercase_						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




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






      def        lowerCamelCase       (					self						:     Optional[int]			,   *lowercase_						:     Tuple			,   **lowercase_						:     str )					->						str:
             """simple docstring"""




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






      @property
      def        lowerCamelCase       (					self						:     str )					->						Union[str, Any]:
             """simple docstring"""




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






import os
import sys
import unittest


lowerCamelCase											=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowerCamelCase											=       os.path.join(git_repo_path, 'src', 'transformers')

lowerCamelCase											=       '\n{0} = None\n'

lowerCamelCase											=       '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n'


lowerCamelCase											=       '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'




class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Tuple )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
             self.assertIsNone(lowercase_ )

             _lowerCamelCase       :		List[str]		=find_backend('    if not is_tokenizers_available():' )
             self.assertEqual(lowercase_			,   'tokenizers' )

             _lowerCamelCase       :		List[Any]		=find_backend('    if not is_tensorflow_text_available():' )
             self.assertEqual(lowercase_			,   'tensorflow_text' )

             _lowerCamelCase       :		int		=find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers' )

             _lowerCamelCase       :		Dict		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tensorflow_text' )

             _lowerCamelCase       :		List[Any]		=find_backend(
                 '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
             self.assertEqual(lowercase_			,   'sentencepiece_and_tokenizers_and_vision' )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						List[str]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=read_init()
             # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
             self.assertIn('torch'			,   lowercase_ )
             self.assertIn('tensorflow_text'			,   lowercase_ )
             self.assertIn('sentencepiece_and_tokenizers'			,   lowercase_ )

             # Likewise, we can't assert on the exact content of a key
             self.assertIn('BertModel'			,   objects['torch'] )
             self.assertIn('TFBertModel'			,   objects['tf'] )
             self.assertIn('FlaxBertModel'			,   objects['flax'] )
             self.assertIn('BertModel'			,   objects['torch'] )
             self.assertIn('TFBertTokenizer'			,   objects['tensorflow_text'] )
             self.assertIn('convert_slow_tokenizer'			,   objects['sentencepiece_and_tokenizers'] )






      def        lowerCamelCase       (					self						:     int )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=create_dummy_object('CONSTANT'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   '\nCONSTANT = None\n' )

             _lowerCamelCase       :		Dict		=create_dummy_object('function'			,   '\'torch\'' )
             self.assertEqual(
                 lowercase_			,   '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n' )

             _lowerCamelCase       :		Union[str, Any]		='\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
             _lowerCamelCase       :		Tuple		=create_dummy_object('FakeClass'			,   '\'torch\'' )
             self.assertEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Dict		='# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
             _lowerCamelCase       :		Optional[int]		=create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
             self.assertEqual(dummy_files['torch']			,   lowercase_ )


