




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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast





@require_vision
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						tempfile.mkdtemp()

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

             __lowerCamelCase   =						BlipaProcessor(UpperCamelCase_    ,       UpperCamelCase_   )

             processor.save_pretrained(self.tmpdirname   )
      def 			lowerCAmelCase__						(				self: Dict    ,       **UpperCamelCase_: Tuple   ):
             return AutoProcessor.from_pretrained(self.tmpdirname    ,       **UpperCamelCase_   ).tokenizer
      def 			lowerCAmelCase__						(				self: List[Any]    ,       **UpperCamelCase_: Optional[int]   ):
             return AutoProcessor.from_pretrained(self.tmpdirname    ,       **UpperCamelCase_   ).image_processor
      def 			lowerCAmelCase__						(				self: Any   ):
             shutil.rmtree(self.tmpdirname   )
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						[np.random.randint(2_55    ,       size=(3, 30, 4_00)    ,       dtype=np.uinta   )]

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

             return image_inputs
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						BlipaProcessor(tokenizer=self.get_tokenizer()    ,       image_processor=self.get_image_processor()   )
             processor.save_pretrained(self.tmpdirname   )

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

             __lowerCamelCase   =						BlipaProcessor.from_pretrained(
                 self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=UpperCamelCase_    ,       padding_value=1.0   )

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

             self.assertEqual(processor.image_processor.to_json_string()    ,       image_processor_add_kwargs.to_json_string()   )
             self.assertIsInstance(processor.image_processor    ,       UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						self.prepare_image_inputs()

             __lowerCamelCase   =						image_processor(UpperCamelCase_    ,       return_tensors="""np"""   )
             __lowerCamelCase   =						processor(images=UpperCamelCase_    ,       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: Dict   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

             __lowerCamelCase   =						"""lower newer"""

             __lowerCamelCase   =						processor(text=UpperCamelCase_   )

             __lowerCamelCase   =						tokenizer(UpperCamelCase_    ,       return_token_type_ids=UpperCamelCase_   )

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]    ,       encoded_processor[key]   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

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

             __lowerCamelCase   =						processor(text=UpperCamelCase_    ,       images=UpperCamelCase_   )

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

             # test if it raises when no input is passed
             with pytest.raises(UpperCamelCase_   ):
                    processor()
      def 			lowerCAmelCase__						(				self: Optional[Any]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

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

             __lowerCamelCase   =						processor.batch_decode(UpperCamelCase_   )
             __lowerCamelCase   =						tokenizer.batch_decode(UpperCamelCase_   )

             self.assertListEqual(UpperCamelCase_    ,       UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: List[str]   ):
             __lowerCamelCase   =						self.get_image_processor()
             __lowerCamelCase   =						self.get_tokenizer()

             __lowerCamelCase   =						BlipaProcessor(tokenizer=UpperCamelCase_    ,       image_processor=UpperCamelCase_   )

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

             __lowerCamelCase   =						processor(text=UpperCamelCase_    ,       images=UpperCamelCase_   )

             # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
             self.assertListEqual(list(inputs.keys()   )    ,       ["""pixel_values""", """input_ids""", """attention_mask"""]   )







from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	Tuple    		=   ['pixel_values']
      def __init__(				self: Any    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: Union[int, float] = 1 / 2_55    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: int = 8    ,       **UpperCamelCase_: Tuple    ,       ):
             super().__init__(**UpperCamelCase_   )

             __lowerCamelCase   =						do_rescale
             __lowerCamelCase   =						rescale_factor
             __lowerCamelCase   =						do_pad
             __lowerCamelCase   =						pad_size
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: float    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Tuple   ):
             return rescale(UpperCamelCase_    ,       scale=UpperCamelCase_    ,       data_format=UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Union[str, Any]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: int    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None   ):
             __lowerCamelCase,   __lowerCamelCase   =						get_image_size(UpperCamelCase_   )
             __lowerCamelCase   =						(old_height // size + 1) * size - old_height
             __lowerCamelCase   =						(old_width // size + 1) * size - old_width

             return pad(UpperCamelCase_    ,       ((0, pad_height), (0, pad_width))    ,       mode="""symmetric"""    ,       data_format=UpperCamelCase_   )




      def 			lowerCAmelCase__						(				self: str    ,       UpperCamelCase_: ImageInput    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[float] = None    ,       UpperCamelCase_: Optional[bool] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[Union[str, TensorType]] = None    ,       UpperCamelCase_: Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **UpperCamelCase_: Any    ,       ):
             __lowerCamelCase   =						do_rescale if do_rescale is not None else self.do_rescale
             __lowerCamelCase   =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __lowerCamelCase   =						do_pad if do_pad is not None else self.do_pad
             __lowerCamelCase   =						pad_size if pad_size is not None else self.pad_size

             __lowerCamelCase   =						make_list_of_images(UpperCamelCase_   )

             if not valid_images(UpperCamelCase_   ):
                    raise ValueError(
                        """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                        """torch.Tensor, tf.Tensor or jax.ndarray."""   )

             if do_rescale and rescale_factor is None:
                    raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

             # All transformations expect numpy arrays.
             __lowerCamelCase   =						[to_numpy_array(UpperCamelCase_   ) for image in images]

             if do_rescale:
                    __lowerCamelCase   =						[self.rescale(image=UpperCamelCase_    ,       scale=UpperCamelCase_   ) for image in images]

             if do_pad:
                    __lowerCamelCase   =						[self.pad(UpperCamelCase_    ,       size=UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						[to_channel_dimension_format(UpperCamelCase_    ,       UpperCamelCase_   ) for image in images]

             __lowerCamelCase   =						{"""pixel_values""": images}
             return BatchFeature(data=UpperCamelCase_    ,       tensor_type=UpperCamelCase_   )

