




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							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." ,				__lowerCamelCase ,				)
										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__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 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(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							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 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor












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       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.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]]] ,				device=__lowerCamelCase ,				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] ,				__lowerCamelCase ,				atol=1e-4							)							)






