






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 		UpperCAmelCase_						(	unittest.TestCase					):


      '''simple docstring'''




      def       _lowerCAmelCase       (	self				) ->				int:
        snake_case_				:							str						 =       tempfile.mkdtemp()

        snake_case_				:							str						 =       BlipImageProcessor()
        snake_case_				:							List[Any]						 =       GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"				)
        snake_case_				:							List[Any]						 =       BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert"				)

        snake_case_				:							Dict						 =       InstructBlipProcessor(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

        processor.save_pretrained(self.tmpdirname				)






      def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				List[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				).tokenizer






      def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				str:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				).image_processor






      def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				).qformer_tokenizer






      def       _lowerCAmelCase       (	self				) ->				List[str]:
        shutil.rmtree(self.tmpdirname				)






      def       _lowerCAmelCase       (	self				) ->				str:
        snake_case_				:							int						 =       [np.random.randint(255		,				size=(3, 30, 400)		,				dtype=np.uinta				)]

        snake_case_				:							List[str]						 =       [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE		,				0		,				-1				)				) for x in image_inputs]

        return image_inputs






      def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
        snake_case_				:							Any						 =       InstructBlipProcessor(
            tokenizer=self.get_tokenizer()		,				image_processor=self.get_image_processor()		,				qformer_tokenizer=self.get_qformer_tokenizer()		,				)
        processor.save_pretrained(self.tmpdirname				)

        snake_case_				:							Tuple						 =       self.get_tokenizer(bos_token="(BOS)"		,				eos_token="(EOS)"				)
        snake_case_				:							List[Any]						 =       self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE		,				padding_value=1.0				)

        snake_case_				:							Union[str, Any]						 =       InstructBlipProcessor.from_pretrained(
            self.tmpdirname		,				bos_token="(BOS)"		,				eos_token="(EOS)"		,				do_normalize=_SCREAMING_SNAKE_CASE		,				padding_value=1.0				)

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

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






      def       _lowerCAmelCase       (	self				) ->				List[Any]:
        snake_case_				:							Any						 =       self.get_image_processor()
        snake_case_				:							Any						 =       self.get_tokenizer()
        snake_case_				:							Tuple						 =       self.get_qformer_tokenizer()

        snake_case_				:							List[Any]						 =       InstructBlipProcessor(
            tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE		,				qformer_tokenizer=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							List[Any]						 =       self.prepare_image_inputs()

        snake_case_				:							Dict						 =       image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors="np"				)
        snake_case_				:							List[str]						 =       processor(images=_SCREAMING_SNAKE_CASE		,				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       _lowerCAmelCase       (	self				) ->				Optional[Any]:
        snake_case_				:							Dict						 =       self.get_image_processor()
        snake_case_				:							List[str]						 =       self.get_tokenizer()
        snake_case_				:							Any						 =       self.get_qformer_tokenizer()

        snake_case_				:							Optional[Any]						 =       InstructBlipProcessor(
            tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE		,				qformer_tokenizer=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							Union[str, Any]						 =       "lower newer"

        snake_case_				:							List[str]						 =       processor(text=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							Optional[Any]						 =       tokenizer(_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE				)
        snake_case_				:							str						 =       qformer_tokenizer(_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE				)

        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       _lowerCAmelCase       (	self				) ->				str:
        snake_case_				:							List[Any]						 =       self.get_image_processor()
        snake_case_				:							List[Any]						 =       self.get_tokenizer()
        snake_case_				:							Any						 =       self.get_qformer_tokenizer()

        snake_case_				:							Dict						 =       InstructBlipProcessor(
            tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE		,				qformer_tokenizer=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							List[Any]						 =       "lower newer"
        snake_case_				:							Optional[int]						 =       self.prepare_image_inputs()

        snake_case_				:							List[str]						 =       processor(text=_SCREAMING_SNAKE_CASE		,				images=_SCREAMING_SNAKE_CASE				)

        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(_SCREAMING_SNAKE_CASE				):
          processor()






      def       _lowerCAmelCase       (	self				) ->				Dict:
        snake_case_				:							Union[str, Any]						 =       self.get_image_processor()
        snake_case_				:							Union[str, Any]						 =       self.get_tokenizer()
        snake_case_				:							Any						 =       self.get_qformer_tokenizer()

        snake_case_				:							Any						 =       InstructBlipProcessor(
            tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE		,				qformer_tokenizer=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							Optional[int]						 =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        snake_case_				:							str						 =       processor.batch_decode(_SCREAMING_SNAKE_CASE				)
        snake_case_				:							Dict						 =       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)

        self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self				) ->				str:
        snake_case_				:							Tuple						 =       self.get_image_processor()
        snake_case_				:							int						 =       self.get_tokenizer()
        snake_case_				:							List[Any]						 =       self.get_qformer_tokenizer()

        snake_case_				:							List[Any]						 =       InstructBlipProcessor(
            tokenizer=_SCREAMING_SNAKE_CASE		,				image_processor=_SCREAMING_SNAKE_CASE		,				qformer_tokenizer=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							Optional[Any]						 =       "lower newer"
        snake_case_				:							Union[str, Any]						 =       self.prepare_image_inputs()

        snake_case_				:							Dict						 =       processor(text=_SCREAMING_SNAKE_CASE		,				images=_SCREAMING_SNAKE_CASE				)

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








import warnings
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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   List[Any]						=					'ViltImageProcessor'
						A     :   Optional[Any]						=					('BertTokenizer', 'BertTokenizerFast')






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							str						 =       None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."		,				_SCREAMING_SNAKE_CASE		,				)
										snake_case_				:							str						 =       kwargs.pop("feature_extractor"				)

								snake_case_				:							Tuple						 =       image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."				)
								if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."				)

								super().__init__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       self.image_processor






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchEncoding:
								snake_case_				:							List[Any]						 =       self.tokenizer(
								    text=_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,				return_special_tokens_mask=_SCREAMING_SNAKE_CASE		,				return_offsets_mapping=_SCREAMING_SNAKE_CASE		,				return_length=_SCREAMING_SNAKE_CASE		,				verbose=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								# add pixel_values + pixel_mask
								snake_case_				:							str						 =       self.image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE				)
								encoding.update(_SCREAMING_SNAKE_CASE				)

								return encoding






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.tokenizer.model_input_names
								snake_case_				:							Tuple						 =       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor_class






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor
