




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_		=							logging.get_logger(__name__)

lowercase_		=							{
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}





class 		A__    (       snake_case_			):
	lowerCamelCase__		:			str       =SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	lowerCamelCase__		:			int       ="""squeezebert"""






	def __init__(     self    ,					lowerCamelCase=30522    ,					lowerCamelCase=768    ,					lowerCamelCase=12    ,					lowerCamelCase=12    ,					lowerCamelCase=3072    ,					lowerCamelCase="gelu"    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.1    ,					lowerCamelCase=512    ,					lowerCamelCase=2    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=1e-12    ,					lowerCamelCase=0    ,					lowerCamelCase=768    ,					lowerCamelCase=4    ,					lowerCamelCase=4    ,					lowerCamelCase=4    ,					lowerCamelCase=1    ,					lowerCamelCase=4    ,					lowerCamelCase=4    ,					**lowerCamelCase    ,					)				->       str:




						"""simple docstring"""






						super().__init__(pad_token_id=lowerCamelCase    ,					**lowerCamelCase						)

						__magic_name__ :							Any		       =							vocab_size
						__magic_name__ :							Tuple		       =							hidden_size
						__magic_name__ :							str		       =							num_hidden_layers
						__magic_name__ :							Optional[int]		       =							num_attention_heads
						__magic_name__ :							List[Any]		       =							hidden_act
						__magic_name__ :							Union[str, Any]		       =							intermediate_size
						__magic_name__ :							int		       =							hidden_dropout_prob
						__magic_name__ :							List[Any]		       =							attention_probs_dropout_prob
						__magic_name__ :							Any		       =							max_position_embeddings
						__magic_name__ :							List[Any]		       =							type_vocab_size
						__magic_name__ :							Any		       =							initializer_range
						__magic_name__ :							Optional[Any]		       =							layer_norm_eps
						__magic_name__ :							Any		       =							embedding_size
						__magic_name__ :							Optional[Any]		       =							q_groups
						__magic_name__ :							List[str]		       =							k_groups
						__magic_name__ :							List[Any]		       =							v_groups
						__magic_name__ :							str		       =							post_attention_groups
						__magic_name__ :							Optional[int]		       =							intermediate_groups
						__magic_name__ :							Optional[int]		       =							output_groups





class 		A__    (       snake_case_			):






	@property
	def 			lowercase				(     self						)				->       Mapping[str, Mapping[int, str]]:




						"""simple docstring"""






						if self.task == "multiple-choice":
											__magic_name__ :							Union[str, Any]		       =							{0: "batch", 1: "choice", 2: "sequence"}
						else:
											__magic_name__ :							Union[str, Any]		       =							{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ('''input_ids''', dynamic_axis),
						        ('''attention_mask''', dynamic_axis),
						        ('''token_type_ids''', dynamic_axis),
						    ]						)









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 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							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=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						)						)



