


'''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 		_snake_case     (					unittest.TestCase		):






						'''simple docstring'''







						@slow
						def lowerCAmelCase__			(	self:						Any  )				->						Optional[int]:
										__magic_name__					:       Union[str, Any]    							=							TFCamembertModel.from_pretrained("jplu/tf-camembert-base"  )

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

										__magic_name__					:       int    							=							model(_UpperCamelCase  )["last_hidden_state"]
										__magic_name__					:       Tuple    							=							tf.TensorShape((1, 10, 768)  )
										self.assertEqual(output.shape   ,				_UpperCamelCase  )
										# compare the actual values for a slice.
										__magic_name__					:       str    							=							tf.convert_to_tensor(
										    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]   ,				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  )  )






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 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_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 + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)