




'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 				_snake_case	(			lowercase_							):
			lowerCAmelCase_  :   Dict		 =					["image_processor", "tokenizer"]
			lowerCAmelCase_  :   Optional[Any]		 =					"AutoImageProcessor"
			lowerCAmelCase_  :   List[str]		 =					"AutoTokenizer"




			def __init__(      self       ,							a__=None       ,							a__=None       ,							**a__							)       ->       Any:

										'''simple docstring'''




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

										snake_case_			      =   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__							)
										snake_case_			      =   self.image_processor
										snake_case_			      =   False




			def __call__(      self       ,							*a__       ,							**a__							)       ->       str:

										'''simple docstring'''




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

										snake_case_			      =   kwargs.pop("images"       ,							a__							)
										snake_case_			      =   kwargs.pop("text"       ,							a__							)
										if len(a__							) > 0:
																	snake_case_			      =   args[0]
																	snake_case_			      =   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:
																	snake_case_			      =   self.image_processor(a__       ,							*a__       ,							**a__							)
										if text is not None:
																	snake_case_			      =   self.tokenizer(a__       ,							**a__							)

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




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

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self       ,							*a__       ,							**a__							)       ->       Tuple:

										'''simple docstring'''




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




			@contextmanager
			def      lowerCAmelCase__       (      self							)       ->       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."							)
										snake_case_			      =   True
										snake_case_			      =   self.tokenizer
										yield
										snake_case_			      =   self.image_processor
										snake_case_			      =   False




			@property
			def      lowerCAmelCase__       (      self							)       ->       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      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''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      lowerCAmelCase__       (      self							)       ->       List[Any]:

										'''simple docstring'''




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

										snake_case_			      =   torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]       ,							device=a__       ,							dtype=torch.long       ,							)  # J'aime le camembert !
										with torch.no_grad():
																	snake_case_			      =   model(a__							)["last_hidden_state"]
										snake_case_			      =   torch.Size((1, 10, 768)							)
										self.assertEqual(output.shape       ,							a__							)
										# compare the actual values for a slice.
										snake_case_			      =   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							)							)




