

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    _lowercase				(					unittest.TestCase			):

   def     UpperCamelCase					(				self	:    List[str]       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		tempfile.mkdtemp()

         A_							       =		BlipImageProcessor()
         A_							       =		GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''       )

         A_							       =		BlipaProcessor(_UpperCamelCase			,						_UpperCamelCase       )

         processor.save_pretrained(self.tmpdirname       )

   def     UpperCamelCase					(				self	:    Any			,						**lowerCamelCase__	:    int       ) ->			Tuple:
         """simple docstring"""





         return AutoProcessor.from_pretrained(self.tmpdirname			,						**_UpperCamelCase       ).tokenizer

   def     UpperCamelCase					(				self	:    Dict			,						**lowerCamelCase__	:    List[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         return AutoProcessor.from_pretrained(self.tmpdirname			,						**_UpperCamelCase       ).image_processor

   def     UpperCamelCase					(				self	:    int       ) ->			str:
         """simple docstring"""





         shutil.rmtree(self.tmpdirname       )

   def     UpperCamelCase					(				self	:    List[str]       ) ->			int:
         """simple docstring"""





         A_							       =		[np.random.randint(2_5_5			,						size=(3, 3_0, 4_0_0)			,						dtype=np.uinta       )]

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

         return image_inputs

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			List[str]:
         """simple docstring"""





         A_							       =		BlipaProcessor(tokenizer=self.get_tokenizer()			,						image_processor=self.get_image_processor()       )
         processor.save_pretrained(self.tmpdirname       )

         A_							       =		self.get_tokenizer(bos_token='''(BOS)'''			,						eos_token='''(EOS)'''       )
         A_							       =		self.get_image_processor(do_normalize=_UpperCamelCase			,						padding_value=1.0       )

         A_							       =		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     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         A_							       =		self.get_image_processor()
         A_							       =		self.get_tokenizer()

         A_							       =		BlipaProcessor(tokenizer=_UpperCamelCase			,						image_processor=_UpperCamelCase       )

         A_							       =		self.prepare_image_inputs()

         A_							       =		image_processor(_UpperCamelCase			,						return_tensors='''np'''       )
         A_							       =		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     UpperCamelCase					(				self	:    Dict       ) ->			Any:
         """simple docstring"""





         A_							       =		self.get_image_processor()
         A_							       =		self.get_tokenizer()

         A_							       =		BlipaProcessor(tokenizer=_UpperCamelCase			,						image_processor=_UpperCamelCase       )

         A_							       =		'''lower newer'''

         A_							       =		processor(text=_UpperCamelCase       )

         A_							       =		tokenizer(_UpperCamelCase			,						return_token_type_ids=_UpperCamelCase       )

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

   def     UpperCamelCase					(				self	:    List[str]       ) ->			int:
         """simple docstring"""





         A_							       =		self.get_image_processor()
         A_							       =		self.get_tokenizer()

         A_							       =		BlipaProcessor(tokenizer=_UpperCamelCase			,						image_processor=_UpperCamelCase       )

         A_							       =		'''lower newer'''
         A_							       =		self.prepare_image_inputs()

         A_							       =		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     UpperCamelCase					(				self	:    List[Any]       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.get_image_processor()
         A_							       =		self.get_tokenizer()

         A_							       =		BlipaProcessor(tokenizer=_UpperCamelCase			,						image_processor=_UpperCamelCase       )

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

         A_							       =		processor.batch_decode(_UpperCamelCase       )
         A_							       =		tokenizer.batch_decode(_UpperCamelCase       )

         self.assertListEqual(_UpperCamelCase			,						_UpperCamelCase       )





   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.get_image_processor()
         A_							       =		self.get_tokenizer()

         A_							       =		BlipaProcessor(tokenizer=_UpperCamelCase			,						image_processor=_UpperCamelCase       )

         A_							       =		'''lower newer'''
         A_							       =		self.prepare_image_inputs()

         A_							       =		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 Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

UpperCamelCase__		 =     logging.get_logger(__name__)







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :Any	,__A    :str		):


       """simple docstring"""




       return [
           int(1_0_0_0 * (box[0] / width)		),
           int(1_0_0_0 * (box[1] / height)		),
           int(1_0_0_0 * (box[2] / width)		),
           int(1_0_0_0 * (box[3] / height)		),
       ]







def 						lowerCamelCase__      ( __A    :np.ndarray	,__A    :Optional[str]	,__A    :Optional[str]		):


       """simple docstring"""




       __snake_case       	=							to_pil_image(__A		)
       __snake_case	,    __snake_case       	=							pil_image.size
       __snake_case       	=							pytesseract.image_to_data(__A	,lang=__A	,output_type="""dict"""	,config=__A		)
       __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case       	=							data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       __snake_case       	=							[idx for idx, word in enumerate(__A		) if not word.strip()]
       __snake_case       	=							[word for idx, word in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       __snake_case       	=							[]
       for x, y, w, h in zip(__A	,__A	,__A	,__A		):
              __snake_case       	=							[x, y, x + w, y + h]
              actual_boxes.append(__A		)

       # finally, normalize the bounding boxes
       __snake_case       	=							[]
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(__A	,__A	,__A		)		)

       assert len(__A		) == len(__A		), "Not as many words as there are bounding boxes"

       return words, normalized_boxes




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["pixel_values"]


       def __init__(		self      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = True      , _UpperCamelCase = 1 / 2_55      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = ""      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              super().__init__(**_UpperCamelCase   )
              __snake_case       	=							size if size is not None else {"""height""": 2_24, """width""": 2_24}
              __snake_case       	=							get_size_dict(_UpperCamelCase   )

              __snake_case       	=							do_resize
              __snake_case       	=							size
              __snake_case       	=							resample
              __snake_case       	=							do_rescale
              __snake_case       	=							rescale_value
              __snake_case       	=							do_normalize
              __snake_case       	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              __snake_case       	=							image_std if image_std is not None else IMAGENET_STANDARD_STD
              __snake_case       	=							apply_ocr
              __snake_case       	=							ocr_lang
              __snake_case       	=							tesseract_config


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              __snake_case       	=							get_size_dict(_UpperCamelCase   )
              if "height" not in size or "width" not in size:
                     raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
              __snake_case       	=							(size["""height"""], size["""width"""])
              return resize(_UpperCamelCase      , size=_UpperCamelCase      , resample=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return rescale(_UpperCamelCase      , scale=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return normalize(_UpperCamelCase      , mean=_UpperCamelCase      , std=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase=None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = ChannelDimension.FIRST      , **_UpperCamelCase      , )      ->							PIL.Image.Image:






              """simple docstring"""


              __snake_case       	=							do_resize if do_resize is not None else self.do_resize
              __snake_case       	=							size if size is not None else self.size
              __snake_case       	=							get_size_dict(_UpperCamelCase   )
              __snake_case       	=							resample if resample is not None else self.resample
              __snake_case       	=							do_rescale if do_rescale is not None else self.do_rescale
              __snake_case       	=							rescale_factor if rescale_factor is not None else self.rescale_factor
              __snake_case       	=							do_normalize if do_normalize is not None else self.do_normalize
              __snake_case       	=							image_mean if image_mean is not None else self.image_mean
              __snake_case       	=							image_std if image_std is not None else self.image_std
              __snake_case       	=							apply_ocr if apply_ocr is not None else self.apply_ocr
              __snake_case       	=							ocr_lang if ocr_lang is not None else self.ocr_lang
              __snake_case       	=							tesseract_config if tesseract_config is not None else self.tesseract_config

              __snake_case       	=							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_resize and size is None:
                     raise ValueError("""Size must be specified if do_resize is True."""   )

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

              if do_normalize and (image_mean is None or image_std is None):
                     raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""   )

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

              # Tesseract OCR to get words + normalized bounding boxes
              if apply_ocr:
                     requires_backends(self      , """pytesseract"""   )
                     __snake_case       	=							[]
                     __snake_case       	=							[]
                     for image in images:
                            __snake_case	,    __snake_case       	=							apply_tesseract(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
                            words_batch.append(_UpperCamelCase   )
                            boxes_batch.append(_UpperCamelCase   )

              if do_resize:
                     __snake_case       	=							[self.resize(image=_UpperCamelCase      , size=_UpperCamelCase      , resample=_UpperCamelCase   ) for image in images]

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

              if do_normalize:
                     __snake_case       	=							[self.normalize(image=_UpperCamelCase      , mean=_UpperCamelCase      , std=_UpperCamelCase   ) for image in images]

              __snake_case       	=							[to_channel_dimension_format(_UpperCamelCase      , _UpperCamelCase   ) for image in images]

              __snake_case       	=							BatchFeature(data={"""pixel_values""": images}      , tensor_type=_UpperCamelCase   )

              if apply_ocr:
                     __snake_case       	=							words_batch
                     __snake_case       	=							boxes_batch
              return data

