

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import ViltImageProcessor



class 						snake_case_ (	unittest.TestCase ):






   def __init__(   self       :  Dict			,		lowercase_       :  Optional[Any]			,		lowercase_       :  str=7			,		lowercase_       :  List[Any]=3			,		lowercase_       :  Optional[Any]=18			,		lowercase_       :  List[str]=30			,		lowercase_       :  Tuple=4_00			,		lowercase_       :  Union[str, Any]=True			,		lowercase_       :  Union[str, Any]=None			,		lowercase_       :  Optional[int]=2			,		lowercase_       :  Optional[Any]=True			,		lowercase_       :  Optional[Any]=[0.5, 0.5, 0.5]			,		lowercase_       :  Dict=[0.5, 0.5, 0.5]			,		)      ->		Tuple:
       lowercase__				:     List[str]					      =       size if size is not None else {"shortest_edge": 30}
       lowercase__				:     Tuple					      =       parent
       lowercase__				:     Optional[int]					      =       batch_size
       lowercase__				:     str					      =       num_channels
       lowercase__				:     str					      =       image_size
       lowercase__				:     Tuple					      =       min_resolution
       lowercase__				:     List[Any]					      =       max_resolution
       lowercase__				:     Dict					      =       do_resize
       lowercase__				:     Union[str, Any]					      =       size
       lowercase__				:     Optional[int]					      =       size_divisor
       lowercase__				:     str					      =       do_normalize
       lowercase__				:     Any					      =       image_mean
       lowercase__				:     str					      =       image_std






   def    __UpperCamelCase  (   self       :  int						)      ->		Dict:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }







   def    __UpperCamelCase  (   self       :  Optional[int]			,		lowercase_       :  Tuple			,		lowercase_       :  List[str]=False						)      ->		Union[str, Any]:
       if not batched:
           lowercase__				:     Optional[int]					      =       self.size["shortest_edge"]
           lowercase__				:     Optional[int]					      =       image_inputs[0]
           if isinstance(lowercase_			,		Image.Image						):
               lowercase__				:     Optional[int]					      =       image.size
           else:
               lowercase__				:     Union[str, Any]					      =       image.shape[1], image.shape[2]
           lowercase__				:     Any					      =       size / min(lowercase_			,		lowercase_						)
           if h < w:
               lowercase__				:     int					      =       size, scale * w
           else:
               lowercase__				:     Optional[int]					      =       scale * h, size

           lowercase__				:     str					      =       int((13_33 / 8_00) * size						)
           if max(lowercase_			,		lowercase_						) > max_size:
               lowercase__				:     int					      =       max_size / max(lowercase_			,		lowercase_						)
               lowercase__				:     int					      =       newh * scale
               lowercase__				:     Union[str, Any]					      =       neww * scale

           lowercase__				:     List[Any]					      =       int(newh + 0.5						), int(neww + 0.5						)
           lowercase__				:     Optional[Any]					      =       (
               newh // self.size_divisor * self.size_divisor,
               neww // self.size_divisor * self.size_divisor,
           )

       else:
           lowercase__				:     Tuple					      =       []
           for image in image_inputs:
               lowercase__				:     str					      =       self.get_expected_values([image]						)
               expected_values.append((expected_height, expected_width)						)
           lowercase__				:     Dict					      =       max(lowercase_			,		key=lambda lowercase_						: item[0]						)[0]
           lowercase__				:     Union[str, Any]					      =       max(lowercase_			,		key=lambda lowercase_						: item[1]						)[1]

       return expected_height, expected_width



@require_torch
@require_vision
class 						snake_case_ (	__A						,unittest.TestCase ):
   __A			:	Any        =       ViltImageProcessor if is_vision_available() else None






   def    __UpperCamelCase  (   self       :  Any						)      ->		Any:
       lowercase__				:     Dict					      =       ViltImageProcessingTester(self						)






   @property
   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		int:
       return self.image_processor_tester.prepare_image_processor_dict()






   def    __UpperCamelCase  (   self       :  Dict						)      ->		int:
       lowercase__				:     List[str]					      =       self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(lowercase_			,		"image_mean"						)						)
       self.assertTrue(hasattr(lowercase_			,		"image_std"						)						)
       self.assertTrue(hasattr(lowercase_			,		"do_normalize"						)						)
       self.assertTrue(hasattr(lowercase_			,		"do_resize"						)						)
       self.assertTrue(hasattr(lowercase_			,		"size"						)						)
       self.assertTrue(hasattr(lowercase_			,		"size_divisor"						)						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     Dict					      =       self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size			,		{"shortest_edge": 30}						)

       lowercase__				:     Union[str, Any]					      =       self.image_processing_class.from_dict(self.image_processor_dict			,		size=42						)
       self.assertEqual(image_processor.size			,		{"shortest_edge": 42}						)






   def    __UpperCamelCase  (   self       :  Union[str, Any]						)      ->		Union[str, Any]:
       pass






   def    __UpperCamelCase  (   self       :  List[str]						)      ->		List[Any]:
       # Initialize image_processing
       lowercase__				:     str					      =       self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowercase__				:     Optional[int]					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_						)
       for image in image_inputs:
           self.assertIsInstance(lowercase_			,		Image.Image						)

       # Test not batched input
       lowercase__				:     Optional[int]					      =       image_processing(image_inputs[0]			,		return_tensors="pt"						).pixel_values

       lowercase__				:     Tuple					      =       self.image_processor_tester.get_expected_values(lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,		)

       # Test batched
       lowercase__				:     Union[str, Any]					      =       image_processing(lowercase_			,		return_tensors="pt"						).pixel_values

       lowercase__				:     Any					      =       self.image_processor_tester.get_expected_values(lowercase_			,		batched=lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,		)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		Union[str, Any]:
       # Initialize image_processing
       lowercase__				:     Dict					      =       self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowercase__				:     List[str]					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_			,		numpify=lowercase_						)
       for image in image_inputs:
           self.assertIsInstance(lowercase_			,		np.ndarray						)

       # Test not batched input
       lowercase__				:     List[str]					      =       image_processing(image_inputs[0]			,		return_tensors="pt"						).pixel_values

       lowercase__				:     int					      =       self.image_processor_tester.get_expected_values(lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,		)

       # Test batched
       lowercase__				:     int					      =       image_processing(lowercase_			,		return_tensors="pt"						).pixel_values

       lowercase__				:     Union[str, Any]					      =       self.image_processor_tester.get_expected_values(lowercase_			,		batched=lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,		)







   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		int:
       # Initialize image_processing
       lowercase__				:     Dict					      =       self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowercase__				:     Any					      =       prepare_image_inputs(self.image_processor_tester			,		equal_resolution=lowercase_			,		torchify=lowercase_						)
       for image in image_inputs:
           self.assertIsInstance(lowercase_			,		torch.Tensor						)

       # Test not batched input
       lowercase__				:     Optional[Any]					      =       image_processing(image_inputs[0]			,		return_tensors="pt"						).pixel_values

       lowercase__				:     List[Any]					      =       self.image_processor_tester.get_expected_values(lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,		)

       # Test batched
       lowercase__				:     Dict					      =       image_processing(lowercase_			,		return_tensors="pt"						).pixel_values

       lowercase__				:     Dict					      =       self.image_processor_tester.get_expected_values(lowercase_			,		batched=lowercase_						)
       self.assertEqual(
           encoded_images.shape			,		(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )			,		)




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 						snake_case_ (	__A ):
   __A			:	Optional[Any]        =       ["image_processor", "tokenizer"]
   __A			:	Tuple        =       "LayoutLMv2ImageProcessor"
   __A			:	Optional[int]        =       ("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")






   def __init__(   self       :  Tuple			,		lowercase_       :  List[str]=None			,		lowercase_       :  Dict=None			,		**lowercase_       :  List[str]						)      ->		int:
       lowercase__				:     Optional[Any]					      =       None
       if "feature_extractor" in kwargs:
           warnings.warn(
               "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
               " instead."			,		lowercase_			,		)
           lowercase__				:     Any					      =       kwargs.pop("feature_extractor"						)

       lowercase__				:     List[str]					      =       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__(lowercase_			,		lowercase_						)






   def __call__(   self       :  Union[str, Any]			,		lowercase_       :  Optional[Any]			,		lowercase_       :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None			,		lowercase_       :  Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None			,		lowercase_       :  Union[List[List[int]], List[List[List[int]]]] = None			,		lowercase_       :  Optional[Union[List[int], List[List[int]]]] = None			,		lowercase_       :  bool = True			,		lowercase_       :  Union[bool, str, PaddingStrategy] = False			,		lowercase_       :  Union[bool, str, TruncationStrategy] = False			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  int = 0			,		lowercase_       :  Optional[int] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  Optional[bool] = None			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = False			,		lowercase_       :  bool = True			,		lowercase_       :  Optional[Union[str, TensorType]] = None			,		**lowercase_       :  List[str]			,		)      ->		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
       lowercase__				:     List[Any]					      =       self.image_processor(images=lowercase_			,		return_tensors=lowercase_						)

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

       lowercase__				:     List[str]					      =       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=lowercase_			,		add_special_tokens=lowercase_			,		padding=lowercase_			,		truncation=lowercase_			,		max_length=lowercase_			,		stride=lowercase_			,		pad_to_multiple_of=lowercase_			,		return_token_type_ids=lowercase_			,		return_attention_mask=lowercase_			,		return_overflowing_tokens=lowercase_			,		return_special_tokens_mask=lowercase_			,		return_offsets_mapping=lowercase_			,		return_length=lowercase_			,		verbose=lowercase_			,		return_tensors=lowercase_			,		**lowercase_			,		)

       # add pixel values
       lowercase__				:     Optional[int]					      =       features.pop("pixel_values"						)
       if return_overflowing_tokens is True:
           lowercase__				:     Tuple					      =       self.get_overflowing_images(lowercase_			,		encoded_inputs["overflow_to_sample_mapping"]						)
       lowercase__				:     str					      =       images

       return encoded_inputs






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

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

       return images_with_overflow






   def    __UpperCamelCase  (   self       :  Any			,		*lowercase_       :  Tuple			,		**lowercase_       :  List[Any]						)      ->		Union[str, Any]:
       return self.tokenizer.batch_decode(*lowercase_			,		**lowercase_						)






   def    __UpperCamelCase  (   self       :  List[Any]			,		*lowercase_       :  Tuple			,		**lowercase_       :  Union[str, Any]						)      ->		List[Any]:
       return self.tokenizer.decode(*lowercase_			,		**lowercase_						)






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






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







   @property
   def    __UpperCamelCase  (   self       :  Dict						)      ->		List[Any]:
       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,		lowercase_			,		)
       return self.image_processor



