




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 					__lowerCamelCase			(       UpperCamelCase__       ):




    """simple docstring"""

    snake_case__   = ["image_processor", "tokenizer"]
    snake_case__   = "LayoutLMv2ImageProcessor"
    snake_case__   = ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


    def __init__(			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=None  ,      SCREAMING_SNAKE_CASE__	:	int=None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[int]  ) ->			List[Any]:
         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__  ,      )
              lowerCAmelCase__            =					kwargs.pop("feature_extractor"  )

         lowerCAmelCase__            =					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__  )


    def __call__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Union[List[List[int]], List[List[List[int]]]] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[List[int], List[List[int]]]] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Union[bool, str, PaddingStrategy] = False  ,      SCREAMING_SNAKE_CASE__	:	Union[bool, str, TruncationStrategy] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[int] = None  ,      SCREAMING_SNAKE_CASE__	:	int = 0  ,      SCREAMING_SNAKE_CASE__	:	Optional[int] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	Optional[bool] = None  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = False  ,      SCREAMING_SNAKE_CASE__	:	bool = True  ,      SCREAMING_SNAKE_CASE__	:	Optional[Union[str, TensorType]] = None  ,      **SCREAMING_SNAKE_CASE__	:	Optional[Any]  ,      ) ->			BatchEncoding:
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
              raise ValueError(
                  "You cannot provide bounding boxes "
                  "if you initialized the image processor with apply_ocr set to True."  )

         if self.image_processor.apply_ocr and (word_labels is not None):
              raise ValueError(
                  "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."  )

         if return_overflowing_tokens is True and return_offsets_mapping is False:
              raise ValueError("You cannot return overflowing tokens without returning the offsets mapping."  )

         # first, apply the image processor
         lowerCAmelCase__            =					self.image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors=SCREAMING_SNAKE_CASE__  )

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
              if isinstance(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ):
                   lowerCAmelCase__            =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
              lowerCAmelCase__            =					features["words"]

         lowerCAmelCase__            =					self.tokenizer(
             text=text if text is not None else features["words"]  ,      text_pair=text_pair if text_pair is not None else None  ,      boxes=boxes if boxes is not None else features["boxes"]  ,      word_labels=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
         lowerCAmelCase__            =					features.pop("pixel_values"  )
         if return_overflowing_tokens is True:
              lowerCAmelCase__            =					self.get_overflowing_images(SCREAMING_SNAKE_CASE__  ,      encoded_inputs["overflow_to_sample_mapping"]  )
         lowerCAmelCase__            =					images

         return encoded_inputs


    def   a (			self	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			Union[str, Any]:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         lowerCAmelCase__            =					[]
         for sample_idx in overflow_to_sample_mapping:
              images_with_overflow.append(images[sample_idx]  )

         if len(SCREAMING_SNAKE_CASE__  ) != len(SCREAMING_SNAKE_CASE__  ):
              raise ValueError(
                  "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
                  f' {len(SCREAMING_SNAKE_CASE__  )} and {len(SCREAMING_SNAKE_CASE__  )}'  )

         return images_with_overflow


    def   a (			self	:	int  ,      *SCREAMING_SNAKE_CASE__	:	Optional[int]  ,      **SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			Dict:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Optional[int]  ,      *SCREAMING_SNAKE_CASE__	:	int  ,      **SCREAMING_SNAKE_CASE__	:	List[str]  ) ->			List[Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__  ,      **SCREAMING_SNAKE_CASE__  )


    @property
    def   a (			self	:	Any  ) ->			Optional[Any]:
         return ["input_ids", "bbox", "attention_mask", "image"]


    @property
    def   a (			self	:	Union[str, Any]  ) ->			int:
         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   a (			self	:	List[str]  ) ->			Tuple:
         warnings.warn(
             "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,      SCREAMING_SNAKE_CASE__  ,      )
         return self.image_processor







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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		tempfile.mkdtemp()

             _lowerCamelCase				:  List[str]						=		BlipImageProcessor()
             _lowerCamelCase				:  Union[str, Any]						=		GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model''')
             _lowerCamelCase				:  Any						=		BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert''')

             _lowerCamelCase				:  Any						=		InstructBlipProcessor(a__   ,				a__   ,				a__)

             processor.save_pretrained(self.tmpdirname)


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





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


      def 					__snake_case      (		self   ,				**a__):



             """simple docstring"""





             return AutoProcessor.from_pretrained(self.tmpdirname   ,				**a__).qformer_tokenizer


      def 					__snake_case      (		self):



             """simple docstring"""





             shutil.rmtree(self.tmpdirname)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[np.random.randint(255   ,				size=(3, 30, 400)   ,				dtype=np.uinta)]

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

             return image_inputs


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=self.get_tokenizer()   ,				image_processor=self.get_image_processor()   ,				qformer_tokenizer=self.get_qformer_tokenizer()   ,				)
             processor.save_pretrained(self.tmpdirname)

             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer(bos_token='''(BOS)'''   ,				eos_token='''(EOS)''')
             _lowerCamelCase				:  str						=		self.get_image_processor(do_normalize=a__   ,				padding_value=1.0)

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor.from_pretrained(
                 self.tmpdirname   ,				bos_token='''(BOS)'''   ,				eos_token='''(EOS)'''   ,				do_normalize=a__   ,				padding_value=1.0)

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

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


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Any						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Dict						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

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

             _lowerCamelCase				:  List[str]						=		image_processor(a__   ,				return_tensors='''np''')
             _lowerCamelCase				:  List[Any]						=		processor(images=a__   ,				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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Any						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  int						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Tuple						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''

             _lowerCamelCase				:  Any						=		processor(text=a__)

             _lowerCamelCase				:  Optional[int]						=		tokenizer(a__   ,				return_token_type_ids=a__)
             _lowerCamelCase				:  Optional[Any]						=		qformer_tokenizer(a__   ,				return_token_type_ids=a__)

             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 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		self.get_image_processor()
             _lowerCamelCase				:  Optional[int]						=		self.get_tokenizer()
             _lowerCamelCase				:  Optional[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  List[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		'''lower newer'''
             _lowerCamelCase				:  Tuple						=		self.prepare_image_inputs()

             _lowerCamelCase				:  Tuple						=		processor(text=a__   ,				images=a__)

             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(a__):
                    processor()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.get_image_processor()
             _lowerCamelCase				:  List[Any]						=		self.get_tokenizer()
             _lowerCamelCase				:  List[Any]						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  Optional[Any]						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  List[Any]						=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase				:  Tuple						=		processor.batch_decode(a__)
             _lowerCamelCase				:  str						=		tokenizer.batch_decode(a__)

             self.assertListEqual(a__   ,				a__)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		self.get_image_processor()
             _lowerCamelCase				:  Dict						=		self.get_tokenizer()
             _lowerCamelCase				:  str						=		self.get_qformer_tokenizer()

             _lowerCamelCase				:  str						=		InstructBlipProcessor(
                 tokenizer=a__   ,				image_processor=a__   ,				qformer_tokenizer=a__)

             _lowerCamelCase				:  str						=		'''lower newer'''
             _lowerCamelCase				:  str						=		self.prepare_image_inputs()

             _lowerCamelCase				:  List[Any]						=		processor(text=a__   ,				images=a__)

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

