


"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	['image_processor', 'tokenizer']
		__UpperCamelCase							=	'AutoImageProcessor'
		__UpperCamelCase							=	'AutoTokenizer'







		def __init__(    self					:				Dict			,      a__					:				Optional[Any]=None			,      a__					:				Tuple=None			,      **a__					:				int						)				->  Tuple:





						'''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."			,      a__			,      )
										_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__(a__			,      a__						)
						_A         =	self.image_processor
						_A         =	False







		def __call__(    self					:				List[str]			,      *a__					:				Dict			,      **a__					:				Optional[Any]						)				->  int:





						'''simple docstring'''







						if self._in_target_context_manager:
										return self.current_processor(*a__			,      **a__						)

						_A         =	kwargs.pop("images"			,      a__						)
						_A         =	kwargs.pop("text"			,      a__						)
						if len(a__						) > 0:
										_A         =	args[0]
										_A         =	args[1:]

						if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."						)

						if images is not None:
										_A         =	self.image_processor(a__			,      *a__			,      **a__						)
						if text is not None:
										_A         =	self.tokenizer(a__			,      **a__						)

						if text is None:
										return inputs
						elif images is None:
										return encodings
						else:
										_A         =	encodings["input_ids"]
										return inputs







		def        a_       (    self					:				str			,      *a__					:				Tuple			,      **a__					:				Tuple						)				->  List[str]:





						'''simple docstring'''







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







		def        a_       (    self					:				str			,      *a__					:				Any			,      **a__					:				str						)				->  List[Any]:





						'''simple docstring'''







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







		@contextmanager
		def        a_       (    self					:				str						)				->  Any:





						'''simple docstring'''







						warnings.warn(
						    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
						    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
						    "your images inputs, or in a separate call."						)
						_A         =	True
						_A         =	self.tokenizer
						yield
						_A         =	self.image_processor
						_A         =	False







		@property
		def        a_       (    self					:				str						)				->  Tuple:





						'''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					:				List[str]						)				->  Optional[Any]:





						'''simple docstring'''







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


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.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]]]			,      device=a__			,      dtype=torch.float			,      )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]			,      a__			,      atol=1E-4						)						)