


"""simple docstring"""






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
 import torch

SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
 from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):




 def        A_					(					cls				)     ->     Dict:
    return cls




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__ = None       ,   UpperCAmelCase__ = None       ,   UpperCAmelCase__ = None       ,   UpperCAmelCase__ = None       ,   )     ->     Any:
   a   :	Dict						=    device
   a   :	Tuple						=    shape[0]

   a   :	List[str]						=    layout or torch.strided
   a   :	int						=    device or torch.device('cpu'				)

   if generator is not None:
      a   :	int						=    generator.device.type if not isinstance(__lowerCamelCase       ,   __lowerCamelCase				) else generator[0].device.type
      if gen_device_type != device.type and gen_device_type == "cpu":
         a   :	int						=    'cpu'
         if device != "mps":
            logger.info(
                F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
                F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
                F' slighly speed up this function by passing a generator that was created on the {device} device.'				)
      elif gen_device_type != device.type and gen_device_type == "cuda":
         raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'				)

   if isinstance(__lowerCamelCase       ,   __lowerCamelCase				):
      a   :	Dict						=    (1,) + shape[1:]
      a   :	Optional[int]						=    [
          torch.randn(__lowerCamelCase       ,   generator=generator[i]       ,   device=__lowerCamelCase       ,   dtype=__lowerCamelCase       ,   layout=__lowerCamelCase				)
          for i in range(__lowerCamelCase				)
      ]
      a   :	List[str]						=    torch.cat(__lowerCamelCase       ,   dim=0				).to(__lowerCamelCase				)
   else:
      a   :	Tuple						=    torch.randn(__lowerCamelCase       ,   generator=__lowerCamelCase       ,   device=__lowerCamelCase       ,   dtype=__lowerCamelCase       ,   layout=__lowerCamelCase				).to(__lowerCamelCase				)

   return latents




def        A_					(					UpperCAmelCase__				)     ->     str:






   if is_torch_version('<'       ,   '2.0.0'				) or not hasattr(__lowerCamelCase       ,   '_dynamo'				):
      return False
   return isinstance(__lowerCamelCase       ,   torch._dynamo.eval_frame.OptimizedModule				)













"""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     A_				(   unittest.TestCase	):







				@slow
				def      _snake_case       (				self				:    Tuple						)					->       Dict:
										__magic_name__      =  CamembertModel.from_pretrained("camembert-base"						)
										model.to(__lowerCamelCase						)

										__magic_name__      =  torch.tensor(
										    [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,     device=__lowerCamelCase    ,     dtype=torch.long    ,     )  # J'aime le camembert !
										with torch.no_grad():
																__magic_name__      =  model(__lowerCamelCase						)["last_hidden_state"]
										__magic_name__      =  torch.Size((1, 1_0, 7_6_8)						)
										self.assertEqual(output.shape    ,     __lowerCamelCase						)
										# compare the actual values for a slice.
										__magic_name__      =  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						)						)



