


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


A   :					Any	=      logging.get_logger(__name__)







class     _lowercase				(							lowercase__							,						lowercase__):


       """simple docstring"""




       A__      		=    "maskformer-swin"

       A__      		=    {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }





       def __init__(     self							:    Any      ,   __lowerCamelCase							:    Dict=224      ,   __lowerCamelCase							:    List[str]=4      ,   __lowerCamelCase							:    int=3      ,   __lowerCamelCase							:    int=96      ,   __lowerCamelCase							:    Tuple=[2, 2, 6, 2]      ,   __lowerCamelCase							:    Tuple=[3, 6, 12, 24]      ,   __lowerCamelCase							:    Union[str, Any]=7      ,   __lowerCamelCase							:    Union[str, Any]=4.0      ,   __lowerCamelCase							:    Any=True      ,   __lowerCamelCase							:    Tuple=0.0      ,   __lowerCamelCase							:    List[str]=0.0      ,   __lowerCamelCase							:    List[str]=0.1      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Optional[int]=False      ,   __lowerCamelCase							:    Tuple=0.0_2      ,   __lowerCamelCase							:    List[Any]=1E-5      ,   __lowerCamelCase							:    Dict=None      ,   __lowerCamelCase							:    Dict=None      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             super().__init__(**__lowerCamelCase )

             lowerCamelCase__	:       int					=						image_size
             lowerCamelCase__	:       Dict					=						patch_size
             lowerCamelCase__	:       Optional[int]					=						num_channels
             lowerCamelCase__	:       Optional[Any]					=						embed_dim
             lowerCamelCase__	:       str					=						depths
             lowerCamelCase__	:       str					=						len(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						num_heads
             lowerCamelCase__	:       Optional[Any]					=						window_size
             lowerCamelCase__	:       str					=						mlp_ratio
             lowerCamelCase__	:       Tuple					=						qkv_bias
             lowerCamelCase__	:       Optional[Any]					=						hidden_dropout_prob
             lowerCamelCase__	:       List[str]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Union[str, Any]					=						drop_path_rate
             lowerCamelCase__	:       int					=						hidden_act
             lowerCamelCase__	:       Optional[Any]					=						use_absolute_embeddings
             lowerCamelCase__	:       Dict					=						layer_norm_eps
             lowerCamelCase__	:       int					=						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
             lowerCamelCase__	:       List[str]					=						int(embed_dim * 2 ** (len(__lowerCamelCase ) - 1) )
             lowerCamelCase__	:       Optional[Any]					=						["stem"] + [f"stage{idx}" for idx in range(1      ,   len(__lowerCamelCase ) + 1 )]
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						get_aligned_output_features_output_indices(
                 out_features=__lowerCamelCase      ,   out_indices=__lowerCamelCase      ,   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):


       """simple docstring"""




       @slow
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )

             lowerCamelCase__	:       str					=						tf.convert_to_tensor(
                 [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]      ,   dtype=tf.intaa      ,   )  # J'aime le camembert !"

             lowerCamelCase__	:       Any					=						model(__lowerCamelCase )["last_hidden_state"]
             lowerCamelCase__	:       List[str]					=						tf.TensorShape((1, 10, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             # compare the actual values for a slice.
             lowerCamelCase__	:       str					=						tf.convert_to_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]]]      ,   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 ) )


