




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 				lowerCAmelCase_       (					lowerCamelCase_							):
				__a     :       Tuple						=     ["image_processor", "tokenizer"]
				__a     :       Optional[int]						=     "BlipImageProcessor"
				__a     :       Union[str, Any]						=     "AutoTokenizer"






				def __init__(			self      ,snake_case__      ,snake_case__ ):
							SCREAMING_SNAKE_CASE_   : Tuple					  =      False
							super().__init__(snake_case__      ,snake_case__ )
							SCREAMING_SNAKE_CASE_   : str					  =      self.image_processor






				def __call__(			self      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = True      ,snake_case__ = False      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = 0      ,snake_case__ = None      ,snake_case__ = None      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = False      ,snake_case__ = True      ,snake_case__ = None      ,**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:
										SCREAMING_SNAKE_CASE_   : List[str]					  =      self.tokenizer
										SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      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
							SCREAMING_SNAKE_CASE_   : Dict					  =      self.image_processor(snake_case__      ,return_tensors=snake_case__ )

							if text is not None:
										SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      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:
										SCREAMING_SNAKE_CASE_   : Tuple					  =      None

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

							return encoding_image_processor






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							return self.tokenizer.batch_decode(*snake_case__      ,**snake_case__ )






				def 		snake_case	(			self      ,*snake_case__      ,**snake_case__ ):
							return self.tokenizer.decode(*snake_case__      ,**snake_case__ )






				@property
				# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.tokenizer.model_input_names
							SCREAMING_SNAKE_CASE_   : int					  =      self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )









import os
import sys
import unittest


UpperCamelCase__ :   Optional[Any]						=							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
UpperCamelCase__ :   Tuple						=							os.path.join(git_repo_path, '''src''', '''transformers''')

UpperCamelCase__ :   List[Any]						=							'''
{0} = None
'''

UpperCamelCase__ :   str						=							'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


UpperCamelCase__ :   List[Any]						=							'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''







class 				lowerCAmelCase_       (					unittest.TestCase							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")' )
							self.assertIsNone(snake_case__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      find_backend('    if not is_tokenizers_available():' )
							self.assertEqual(snake_case__      ,'tokenizers' )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      find_backend('    if not is_tensorflow_text_available():' )
							self.assertEqual(snake_case__      ,'tensorflow_text' )

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

							SCREAMING_SNAKE_CASE_   : str					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tensorflow_text' )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      find_backend(
							    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):' )
							self.assertEqual(snake_case__      ,'sentencepiece_and_tokenizers_and_vision' )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      read_init()
							# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
							self.assertIn('torch'      ,snake_case__ )
							self.assertIn('tensorflow_text'      ,snake_case__ )
							self.assertIn('sentencepiece_and_tokenizers'      ,snake_case__ )

							# 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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      create_dummy_object('CONSTANT'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,'\nCONSTANT = None\n' )

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

							SCREAMING_SNAKE_CASE_   : List[str]					  =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_object('FakeClass'      ,'\'torch\'' )
							self.assertEqual(snake_case__      ,snake_case__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      '# 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'
							SCREAMING_SNAKE_CASE_   : Dict					  =      create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']} )
							self.assertEqual(dummy_files['torch']      ,snake_case__ )



