'''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 				a__(					UpperCamelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[Any]      =       ['''image_processor''', '''tokenizer''']
			UpperCAmelCase_			:     Tuple      =       '''LayoutLMv3ImageProcessor'''
			UpperCAmelCase_			:     Optional[Any]      =       ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
							    """ instead."""       ,    _a       ,    )
							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__(_a       ,    _a)



			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = 0       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = False       ,    __lowerCAmelCase = True       ,    __lowerCAmelCase = None       ,    **__lowerCAmelCase       ,    ):




					"""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
					lowerCAmelCase   					=  self.image_processor(images=_a       ,    return_tensors=_a)

					# second, apply the tokenizer
					if text is not None and self.image_processor.apply_ocr and text_pair is None:
							if isinstance(_a       ,    _a):
									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=_a       ,    add_special_tokens=_a       ,    padding=_a       ,    truncation=_a       ,    max_length=_a       ,    stride=_a       ,    pad_to_multiple_of=_a       ,    return_token_type_ids=_a       ,    return_attention_mask=_a       ,    return_overflowing_tokens=_a       ,    return_special_tokens_mask=_a       ,    return_offsets_mapping=_a       ,    return_length=_a       ,    verbose=_a       ,    return_tensors=_a       ,    **_a       ,    )

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

					return encoded_inputs



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  []
					for sample_idx in overflow_to_sample_mapping:
							images_with_overflow.append(images[sample_idx])

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

					return images_with_overflow



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


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



			@property
			def 							a_       (   self):




					"""simple docstring"""


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



			@property
			def 							a_       (   self):




					"""simple docstring"""


					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,    _a       ,    )
					return self.image_processor_class







			@property
			def 							a_       (   self):




					"""simple docstring"""


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










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 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     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				)				)

