

"""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  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Dict       		=     ["image_processor", "tokenizer"]
 __UpperCAmelCase     : int       		=     "LayoutLMv2ImageProcessor"
 __UpperCAmelCase     : List[Any]       		=     ("LayoutXLMTokenizer", "LayoutXLMTokenizerFast")


 def __init__(      self							:      Any, UpperCAmelCase__							:      Union[str, Any]=None, UpperCAmelCase__							:      Optional[Any]=None, **UpperCAmelCase__							:      Dict     ):
    if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead.", UpperCAmelCase__, )
       __lowercase								=						kwargs.pop("feature_extractor"     )

    __lowercase								=						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__(UpperCAmelCase__, UpperCAmelCase__     )


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

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

    __lowercase								=						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=UpperCAmelCase__, add_special_tokens=UpperCAmelCase__, padding=UpperCAmelCase__, truncation=UpperCAmelCase__, max_length=UpperCAmelCase__, stride=UpperCAmelCase__, pad_to_multiple_of=UpperCAmelCase__, return_token_type_ids=UpperCAmelCase__, return_attention_mask=UpperCAmelCase__, return_overflowing_tokens=UpperCAmelCase__, return_special_tokens_mask=UpperCAmelCase__, return_offsets_mapping=UpperCAmelCase__, return_length=UpperCAmelCase__, verbose=UpperCAmelCase__, return_tensors=UpperCAmelCase__, **UpperCAmelCase__, )

    # add pixel values
    __lowercase								=						features.pop("pixel_values"     )
    if return_overflowing_tokens is True:
       __lowercase								=						self.get_overflowing_images(UpperCAmelCase__, encoded_inputs["overflow_to_sample_mapping"]     )
    __lowercase								=						images

    return encoded_inputs


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      Optional[Any], UpperCAmelCase__							:      Optional[int]     ):
    # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
    __lowercase								=						[]
    for sample_idx in overflow_to_sample_mapping:
       images_with_overflow.append(images[sample_idx]     )

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

    return images_with_overflow


 def  _lowercase			(      self							:      Optional[int], *UpperCAmelCase__							:      List[str], **UpperCAmelCase__							:      List[str]     ):
    return self.tokenizer.batch_decode(*UpperCAmelCase__, **UpperCAmelCase__     )


 def  _lowercase			(      self							:      Optional[int], *UpperCAmelCase__							:      Optional[Any], **UpperCAmelCase__							:      List[Any]     ):
    return self.tokenizer.decode(*UpperCAmelCase__, **UpperCAmelCase__     )


 @property
 def  _lowercase			(      self							:      List[Any]     ):
    return ["input_ids", "bbox", "attention_mask", "image"]


 @property
 def  _lowercase			(      self							:      List[Any]     ):
    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", UpperCAmelCase__, )
    return self.image_processor_class



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







"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class  _lowerCAmelCase  (     unittest.TestCase       ):







 """simple docstring"""







 @slow
 def  _lowercase			(      self							:      Optional[Any]     ):
    __lowercase								=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base"     )

    __lowercase								=						tf.convert_to_tensor(
        [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]], dtype=tf.intaa, )  # J'aime le camembert !"

    __lowercase								=						model(UpperCAmelCase__     )["last_hidden_state"]
    __lowercase								=						tf.TensorShape((1, 1_0, 7_6_8)     )
    self.assertEqual(output.shape, UpperCAmelCase__     )
    # compare the actual values for a slice.
    __lowercase								=						tf.convert_to_tensor(
        [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], dtype=tf.floataa, )
    # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
    # camembert.eval()
    # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

    self.assertTrue(np.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1E-4     )     )




