


"""simple docstring"""







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


__lowercase    					=      logging.get_logger(__name__)






class 						_A  (  _a    ,_a  ):







    """simple docstring"""




    UpperCAmelCase       :							Optional[Any]	=       """maskformer-swin"""

    UpperCAmelCase       :							Optional[int]	=       {
        """num_attention_heads""": """num_heads""",
        """num_hidden_layers""": """num_layers""",
    }






    def __init__(					self				:    Any				, __UpperCAmelCase				:    List[Any]=224				, __UpperCAmelCase				:    Dict=4				, __UpperCAmelCase				:    int=3				, __UpperCAmelCase				:    int=96				, __UpperCAmelCase				:    Any=[2, 2, 6, 2]				, __UpperCAmelCase				:    Tuple=[3, 6, 12, 24]				, __UpperCAmelCase				:    Tuple=7				, __UpperCAmelCase				:    Dict=4.0				, __UpperCAmelCase				:    Union[str, Any]=True				, __UpperCAmelCase				:    Any=0.0				, __UpperCAmelCase				:    Optional[Any]=0.0				, __UpperCAmelCase				:    List[Any]=0.1				, __UpperCAmelCase				:    List[Any]="gelu"				, __UpperCAmelCase				:    Any=False				, __UpperCAmelCase				:    Optional[int]=0.02				, __UpperCAmelCase				:    Dict=1e-5				, __UpperCAmelCase				:    Union[str, Any]=None				, __UpperCAmelCase				:    str=None				, **__UpperCAmelCase				:    List[str]				, ):
      super().__init__(**__UpperCAmelCase)

      a						:		int      							=			image_size
      a						:		str      							=			patch_size
      a						:		Optional[int]      							=			num_channels
      a						:		str      							=			embed_dim
      a						:		int      							=			depths
      a						:		Dict      							=			len(__UpperCAmelCase)
      a						:		Dict      							=			num_heads
      a						:		Union[str, Any]      							=			window_size
      a						:		Optional[Any]      							=			mlp_ratio
      a						:		Any      							=			qkv_bias
      a						:		str      							=			hidden_dropout_prob
      a						:		List[str]      							=			attention_probs_dropout_prob
      a						:		Optional[int]      							=			drop_path_rate
      a						:		List[str]      							=			hidden_act
      a						:		int      							=			use_absolute_embeddings
      a						:		int      							=			layer_norm_eps
      a						:		List[str]      							=			initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      a						:		Dict      							=			int(embed_dim * 2 ** (len(__UpperCAmelCase) - 1))
      a						:		List[Any]      							=			["stem"] + [f'''stage{idx}''' for idx in range(1				, len(__UpperCAmelCase) + 1)]
      a							,		a						:		int      							=			get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase				, out_indices=__UpperCAmelCase				, stage_names=self.stage_names)












from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel







@require_tf
@require_sentencepiece
@require_tokenizers
class 						lowercase							(   unittest.TestCase		):

     @slow
     def 					a__	(  self				)						->							Any:
           _A							:     Tuple			=						TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""				)

           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]		,     dtype=tf.intaa		,     )  # J'aime le camembert !"

           _A							:     List[str]			=						model(_a				)["""last_hidden_state"""]
           _A							:     Union[str, Any]			=						tf.TensorShape((1, 10, 768)				)
           self.assertEqual(output.shape		,     _a				)
           # compare the actual values for a slice.
           _A							:     List[Any]			=						tf.convert_to_tensor(
               [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]		,     dtype=tf.floataa		,     )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()		,     expected_slice.numpy()		,     atol=1e-4				)				)

