
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       a__			(			self  :       Any    )						->		str:



											"""simple docstring"""


											__lowercase								= CamembertModel.from_pretrained('camembert-base'    )
											model.to(lowerCamelCase__    )

											__lowercase								= 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():
															__lowercase								= model(lowerCamelCase__    )['last_hidden_state']
											__lowercase								= torch.Size((1, 10, 7_68)    )
											self.assertEqual(output.shape					,			lowerCamelCase__    )
											# compare the actual values for a slice.
											__lowercase								= 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    )    )



from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """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__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


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

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





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



											"""simple docstring"""


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





