


'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """google/bit-50""": """https://huggingface.co/google/bit-50/resolve/main/config.json""",
}

class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase		):
     lowerCamelCase				:	List[Any]		=     '''bit'''
     lowerCamelCase				:	Any		=     ['''preactivation''', '''bottleneck''']
     lowerCamelCase				:	List[Any]		=     ['''SAME''', '''VALID''']





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]=3    ,      UpperCAmelCase__       :      Any=6_4    ,      UpperCAmelCase__       :      Any=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]    ,      UpperCAmelCase__       :      int=[3, 4, 6, 3]    ,      UpperCAmelCase__       :      int="preactivation"    ,      UpperCAmelCase__       :      str="relu"    ,      UpperCAmelCase__       :      int=None    ,      UpperCAmelCase__       :      Optional[Any]=3_2    ,      UpperCAmelCase__       :      Optional[int]=0.0    ,      UpperCAmelCase__       :      Union[str, Any]=False    ,      UpperCAmelCase__       :      Optional[int]=3_2    ,      UpperCAmelCase__       :      Tuple=1    ,      UpperCAmelCase__       :      Optional[Any]=None    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      **UpperCAmelCase__       :      str    ,      )				->  int:
        super().__init__(**UpperCAmelCase__    )
        if layer_type not in self.layer_types:
           raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
        if global_padding is not None:
           if global_padding.upper() in self.supported_padding:
              lowerCAmelCase     =			global_padding.upper()
           else:
              raise ValueError(F'''Padding strategy {global_padding} not supported'''    )
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			embedding_size
        lowerCAmelCase     =			hidden_sizes
        lowerCAmelCase     =			depths
        lowerCAmelCase     =			layer_type
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			global_padding
        lowerCAmelCase     =			num_groups
        lowerCAmelCase     =			drop_path_rate
        lowerCAmelCase     =			embedding_dynamic_padding
        lowerCAmelCase     =			output_stride
        lowerCAmelCase     =			width_factor

        lowerCAmelCase     =			['stem'] + [F'''stage{idx}''' for idx in range(1    ,      len(UpperCAmelCase__    ) + 1    )]
        lowerCAmelCase     ,  lowerCAmelCase     =			get_aligned_output_features_output_indices(
            out_features=UpperCAmelCase__    ,      out_indices=UpperCAmelCase__    ,      stage_names=self.stage_names    )









'''simple docstring'''




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel

@require_sentencepiece
@require_tokenizers
@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      str    )				->  List[str]:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-base'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 7_6_8)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Tuple:
        lowerCAmelCase     =			XLMRobertaModel.from_pretrained('xlm-roberta-large'    )
        lowerCAmelCase     =			torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]]    )
        # The dog is cute and lives in the garden house

        lowerCAmelCase     =			torch.Size((1, 1_2, 1_0_2_4)    )  # batch_size, sequence_length, embedding_vector_dim
        lowerCAmelCase     =			torch.tensor(
            [[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]]    )
        #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
        #  xlmr.eval()
        #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )['last_hidden_state'].detach()
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )
        # compare the actual values for a slice of last dim
        self.assertTrue(torch.allclose(output[:, :, -1]    ,      UpperCAmelCase__    ,      atol=1E-3    )    )





