from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_				:		List[str]       						=       logging.get_logger(__name__)

lowerCamelCase_				:		str       						=       {
    """facebook/opt-125m""": """https://huggingface.co/facebook/opt-125m/blob/main/config.json""",
    """facebook/opt-350m""": """https://huggingface.co/facebook/opt-350m/blob/main/config.json""",
    """facebook/opt-1.3b""": """https://huggingface.co/facebook/opt-1.3b/blob/main/config.json""",
    """facebook/opt-2.7b""": """https://huggingface.co/facebook/opt-2.7b/blob/main/config.json""",
    """facebook/opt-6.7b""": """https://huggingface.co/facebook/opt-6.7b/blob/main/config.json""",
    """facebook/opt-13b""": """https://huggingface.co/facebook/opt-13b/blob/main/config.json""",
}


class 						a__							(   __snake_case		):
			A__				:   Union[str, Any]								=			'opt'
			A__				:   Optional[int]								=			['past_key_values']



			def __init__(						self      ,						UpperCAmelCase=5_0_2_7_2      ,						UpperCAmelCase=7_6_8      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase=3_0_7_2      ,						UpperCAmelCase=2_0_4_8      ,						UpperCAmelCase=True      ,						UpperCAmelCase=False      ,						UpperCAmelCase=None      ,						UpperCAmelCase=0.1      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=1_2      ,						UpperCAmelCase="relu"      ,						UpperCAmelCase=0.0      ,						UpperCAmelCase=0.02      ,						UpperCAmelCase=True      ,						UpperCAmelCase=1      ,						UpperCAmelCase=2      ,						UpperCAmelCase=2      ,						UpperCAmelCase=True      ,						UpperCAmelCase=True      ,						**UpperCAmelCase      ,						) ->    int:
								super().__init__(
								    pad_token_id=UpperCAmelCase      ,						bos_token_id=UpperCAmelCase      ,						eos_token_id=UpperCAmelCase      ,						**UpperCAmelCase      ,						)
								__a							=   vocab_size
								__a							=   max_position_embeddings
								__a							=   num_attention_heads
								__a							=   word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
								__a							=   ffn_dim
								__a							=   hidden_size
								__a							=   num_hidden_layers
								__a							=   dropout
								__a							=   attention_dropout
								__a							=   activation_function
								__a							=   init_std
								__a							=   layerdrop
								__a							=   use_cache
								__a							=   do_layer_norm_before
								# We keep these variables at `True` for backward compatibility.
								__a							=   enable_bias
								__a							=   layer_norm_elementwise_affine

								# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
								# with checkpoints that have been fine-tuned before transformers v4.20.1
								# see https://github.com/facebookresearch/metaseq/pull/164
								__a							=   _remove_final_layer_norm








"""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        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







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

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