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"""






 lowerCamelCase	:str    			=		['''image_processor''', '''tokenizer''']
 lowerCamelCase	:Optional[int]    			=		'''BlipImageProcessor'''
 lowerCamelCase	:str    			=		('''BertTokenizer''', '''BertTokenizerFast''')



 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[str]:
        _A        =  False
        super().__init__(lowerCAmelCase_		,				lowerCAmelCase_							)
        _A        =  self.image_processor



 def __call__(					self		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = 0		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = None		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = False		,				lowerCAmelCase_ = True		,				lowerCAmelCase_ = None		,				**lowerCAmelCase_		,				)  ->					BatchEncoding:
        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:
               _A        =  self.tokenizer
               _A        =  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
        _A        =  self.image_processor(lowerCAmelCase_		,				return_tensors=lowerCAmelCase_							)

        if text is not None:
               _A        =  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:
               _A        =  None

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

        return encoding_image_processor



 def      UpperCAmelCase							(					self		,				*lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Optional[Any]:
        return self.tokenizer.batch_decode(*lowerCAmelCase_		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				*lowerCAmelCase_		,				**lowerCAmelCase_							)  ->					Optional[int]:
        return self.tokenizer.decode(*lowerCAmelCase_		,				**lowerCAmelCase_							)



 @property
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _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 shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import (
        AutoProcessor,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )



@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  tempfile.mkdtemp()

        _A        =  BlipImageProcessor()
        _A        =  GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""							)
        _A        =  BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""							)

        _A        =  InstructBlipProcessor(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).tokenizer



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Dict:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).image_processor



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							).qformer_tokenizer



 def      UpperCAmelCase							(					self							)  ->					int:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  [np.random.randint(2_55		,				size=(3, 30, 4_00)		,				dtype=np.uinta							)]

        _A        =  [Image.fromarray(np.moveaxis(lowerCAmelCase_		,				0		,				-1							)							) for x in image_inputs]

        return image_inputs



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname							)

        _A        =  self.get_tokenizer(bos_token="""(BOS)"""		,				eos_token="""(EOS)"""							)
        _A        =  self.get_image_processor(do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        _A        =  InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="""(BOS)"""		,				eos_token="""(EOS)"""		,				do_normalize=lowerCAmelCase_		,				padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()		,				tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,				lowerCAmelCase_							)

        self.assertEqual(processor.image_processor.to_json_string()		,				image_processor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.image_processor		,				lowerCAmelCase_							)
        self.assertIsInstance(processor.qformer_tokenizer		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  self.prepare_image_inputs()

        _A        =  image_processor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(images=lowerCAmelCase_		,				return_tensors="""np"""							)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()		,				input_processor[key].sum()		,				delta=1E-2							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)
        _A        =  qformer_tokenizer(lowerCAmelCase_		,				return_token_type_ids=lowerCAmelCase_							)

        for key in encoded_tokens.keys():
               self.assertListEqual(encoded_tokens[key]		,				encoded_processor[key]							)

        for key in encoded_tokens_qformer.keys():
               self.assertListEqual(encoded_tokens_qformer[key]		,				encoded_processor["""qformer_""" + key]							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

        self.assertListEqual(
            list(inputs.keys()							)		,				["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,				)

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase_							):
               processor()



 def      UpperCAmelCase							(					self							)  ->					int:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _A        =  processor.batch_decode(lowerCAmelCase_							)
        _A        =  tokenizer.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        _A        =  self.get_image_processor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_qformer_tokenizer()

        _A        =  InstructBlipProcessor(
            tokenizer=lowerCAmelCase_		,				image_processor=lowerCAmelCase_		,				qformer_tokenizer=lowerCAmelCase_							)

        _A        =  """lower newer"""
        _A        =  self.prepare_image_inputs()

        _A        =  processor(text=lowerCAmelCase_		,				images=lowerCAmelCase_							)

        self.assertListEqual(
            list(inputs.keys()							)		,				["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""]		,				)




