


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 	_lowercase				(							unittest.TestCase	):






				'''simple docstring'''



				@slow
				def 		__magic_name__(	self      :Tuple     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		CamembertModel.from_pretrained('''camembert-base'''     )
										model.to(lowerCamelCase__     )

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.tensor(
										    [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]      ,      device=lowerCamelCase__      ,      dtype=torch.long      ,      )  # J'aime le camembert !
										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		model(lowerCamelCase__     )['''last_hidden_state''']
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.Size((1, 10, 768)     )
										self.assertEqual(output.shape      ,      lowerCamelCase__     )
										# compare the actual values for a slice.
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.tensor(
										    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]      ,      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     )     )




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      int      =		c_float


				@property
				def 		__magic_name__(	self      :int     )		->      int:
										return sizeof(self.value     )




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__ = 0				):
						# < 0 is possible if using a dynamic axis
						if dimension <= 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		fixed_dimension

						dimension -= num_token_to_add
						return dimension


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						return num_parameters * dtype.size


def 	_UpperCamelCase							(				lowercase__				):
						from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

						try:
												return AutoProcessor.from_pretrained(lowercase__				)
						except (ValueError, OSError, KeyError):
												__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Tuple		      =		None, None
												try:
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		AutoTokenizer.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass
												try:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		AutoFeatureExtractor.from_pretrained(lowercase__				)
												except (OSError, KeyError):
																		pass

												if tokenizer is not None and feature_extractor is not None:
																		raise ValueError(
																		    F'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''				)
												elif tokenizer is None and feature_extractor is None:
																		return None
												elif tokenizer is not None:
																		return tokenizer
												else:
																		return feature_extractor
