


'''simple docstring'''







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_						(       snake_case__      ):





  """simple docstring"""



  a_       :Any												=["""image_processor""", """tokenizer"""]
  a_       :Union[str, Any]												="""LayoutLMv3ImageProcessor"""
  a_       :List[Any]												=("""LayoutLMv3Tokenizer""", """LayoutLMv3TokenizerFast""")

  def __init__(					self     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=None				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=None				,							**SCREAMING_SNAKE_CASE__     :							List[str]						):





       '''simple docstring'''

       __a       =   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__				,							)
            __a       =   kwargs.pop("""feature_extractor"""						)

       __a       =   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__     :							Union[str, Any]				,							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__     :							Any				,							):





       '''simple docstring'''

       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."""						)

       # first, apply the image processor
       __a       =   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__						):
                 __a       =   [text]  # add batch dimension (as the image processor always adds a batch dimension)
            __a       =   features["""words"""]

       __a       =   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
       __a       =   features.pop("""pixel_values"""						)
       if return_overflowing_tokens is True:
            __a       =   self.get_overflowing_images(SCREAMING_SNAKE_CASE__				,							encoded_inputs["""overflow_to_sample_mapping"""]						)
       __a       =   images

       return encoded_inputs

  def __a						(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       __a       =   []
       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     :							Union[str, Any]				,							*SCREAMING_SNAKE_CASE__     :							int				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							Tuple				,							*SCREAMING_SNAKE_CASE__     :							Dict				,							**SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__						)

  @property
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       return ["input_ids", "bbox", "attention_mask", "pixel_values"]

  @property
  def __a						(					self     :							Any						):





       '''simple docstring'''

       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     :							int						):





       '''simple docstring'''

       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""				,							SCREAMING_SNAKE_CASE__				,							)
       return self.image_processor










'''simple docstring'''







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
 import torch

 from transformers import XLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_torch
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-base"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 7_6_8)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)


  @slow
  def __a						(					self     :							int						):





       '''simple docstring'''

       __a       =   XLMRobertaModel.from_pretrained("""xlm-roberta-large"""						)
       __a       =   torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]						)
       # The dog is cute and lives in the garden house

       __a       =   torch.Size((1, 1_2, 1_0_2_4)						)  # batch_size, sequence_length, embedding_vector_dim
       __a       =   torch.tensor(
           [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]						)
       #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
       #  xlmr.eval()
       #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
       with torch.no_grad():
            __a       =   model(SCREAMING_SNAKE_CASE__						)["""last_hidden_state"""].detach()
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)
       # compare the actual values for a slice of last dim
       self.assertTrue(torch.allclose(output[:, :, -1]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-3						)						)






