


'''simple docstring'''


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__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Any				      =   ["image_processor", "tokenizer"]
			lowerCAmelCase       :		Optional[int]				      =   "BlipImageProcessor"
			lowerCAmelCase       :		Any				      =   "AutoTokenizer"




			def __init__(						self     :   Any		,     lowerCamelCase__     :   Tuple		,     lowerCamelCase__     :   int       )      ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[int]	    =					False
					super().__init__(lowerCamelCase__		,     lowerCamelCase__       )
					_UpperCAmelCase						:				Tuple	    =					self.image_processor




			def __call__(						self     :   Optional[Any]		,     lowerCamelCase__     :   ImageInput = None		,     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__     :   bool = False		,     lowerCamelCase__     :   bool = False		,     lowerCamelCase__     :   bool = False		,     lowerCamelCase__     :   bool = False		,     lowerCamelCase__     :   bool = False		,     lowerCamelCase__     :   bool = True		,     lowerCamelCase__     :   Optional[Union[str, TensorType]] = None		,     **lowerCamelCase__     :   Optional[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:
							_UpperCAmelCase						:				List[str]	    =					self.tokenizer
							_UpperCAmelCase						:				int	    =					self.tokenizer(
							    text=lowerCamelCase__		,     add_special_tokens=lowerCamelCase__		,     padding=lowerCamelCase__		,     truncation=lowerCamelCase__		,     max_length=lowerCamelCase__		,     stride=lowerCamelCase__		,     pad_to_multiple_of=lowerCamelCase__		,     return_attention_mask=lowerCamelCase__		,     return_overflowing_tokens=lowerCamelCase__		,     return_special_tokens_mask=lowerCamelCase__		,     return_offsets_mapping=lowerCamelCase__		,     return_token_type_ids=lowerCamelCase__		,     return_length=lowerCamelCase__		,     verbose=lowerCamelCase__		,     return_tensors=lowerCamelCase__		,     **lowerCamelCase__		,     )
							return text_encoding

					# add pixel_values
					_UpperCAmelCase						:				Union[str, Any]	    =					self.image_processor(lowerCamelCase__		,     return_tensors=lowerCamelCase__       )

					if text is not None:
							_UpperCAmelCase						:				int	    =					self.tokenizer(
							    text=lowerCamelCase__		,     add_special_tokens=lowerCamelCase__		,     padding=lowerCamelCase__		,     truncation=lowerCamelCase__		,     max_length=lowerCamelCase__		,     stride=lowerCamelCase__		,     pad_to_multiple_of=lowerCamelCase__		,     return_attention_mask=lowerCamelCase__		,     return_overflowing_tokens=lowerCamelCase__		,     return_special_tokens_mask=lowerCamelCase__		,     return_offsets_mapping=lowerCamelCase__		,     return_token_type_ids=lowerCamelCase__		,     return_length=lowerCamelCase__		,     verbose=lowerCamelCase__		,     return_tensors=lowerCamelCase__		,     **lowerCamelCase__		,     )
					else:
							_UpperCAmelCase						:				Optional[int]	    =					None

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

					return encoding_image_processor




			def 							lowerCAmelCase__			(						self     :   str		,     *lowerCamelCase__     :   Tuple		,     **lowerCamelCase__     :   Optional[Any]       )      ->Any:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   List[Any]		,     *lowerCamelCase__     :   List[Any]		,     **lowerCamelCase__     :   List[str]       )      ->int:



					'''simple docstring'''







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






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 							lowerCAmelCase__			(						self     :   int       )      ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self.tokenizer.model_input_names
					_UpperCAmelCase						:				List[Any]	    =					self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )







'''simple docstring'''


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property






@unittest.skipUnless(os.path.exists(UpperCAmelCase__		)			,				"Tatoeba directory does not exist."		)
class 		lowerCAmelCase__	(	unittest.TestCase		):




			@cached_property
			def 							lowerCAmelCase__			(						self     :   Optional[Any]       )      ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					tempfile.mkdtemp()
					return TatoebaConverter(save_dir=lowerCamelCase__       )




			@slow
			def 							lowerCAmelCase__			(						self     :   Optional[int]       )      ->List[str]:



					'''simple docstring'''







					self.resolver.convert_models(["heb-eng"]       )






			@slow
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase    ,			_UpperCAmelCase						:				Tuple	    =					self.resolver.write_model_card("opus-mt-he-en"		,     dry_run=lowerCamelCase__       )
					assert mmeta["long_pair"] == "heb-eng"



