'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					int  ={
    'microsoft/swin-tiny-patch4-window7-224': (
        'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Any	       =  """swin"""

    a__	:						Any	       =  {
        """num_attention_heads""": """num_heads""",
        """num_hidden_layers""": """num_layers""",
    }



    def __init__(self   :   Any       ,      __a   :   Optional[int]=224       ,      __a   :   Tuple=4       ,      __a   :   Optional[Any]=3       ,      __a   :   List[str]=96       ,      __a   :   List[str]=[2, 2, 6, 2]       ,      __a   :   Union[str, Any]=[3, 6, 12, 24]       ,      __a   :   int=7       ,      __a   :   Optional[Any]=4.0       ,      __a   :   Union[str, Any]=True       ,      __a   :   Dict=0.0       ,      __a   :   Optional[int]=0.0       ,      __a   :   Optional[int]=0.1       ,      __a   :   Dict="gelu"       ,      __a   :   int=False       ,      __a   :   Optional[Any]=0.02       ,      __a   :   str=1E-5       ,      __a   :   str=32       ,      __a   :   Tuple=None       ,      __a   :   List[Any]=None       ,      **__a   :   List[str]       ,      ):
         super().__init__(**__a			)

         UpperCAmelCase_	   =       image_size
         UpperCAmelCase_	   =       patch_size
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embed_dim
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       len(__a			)
         UpperCAmelCase_	   =       num_heads
         UpperCAmelCase_	   =       window_size
         UpperCAmelCase_	   =       mlp_ratio
         UpperCAmelCase_	   =       qkv_bias
         UpperCAmelCase_	   =       hidden_dropout_prob
         UpperCAmelCase_	   =       attention_probs_dropout_prob
         UpperCAmelCase_	   =       drop_path_rate
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       use_absolute_embeddings
         UpperCAmelCase_	   =       layer_norm_eps
         UpperCAmelCase_	   =       initializer_range
         UpperCAmelCase_	   =       encoder_stride
         # 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
         UpperCAmelCase_	   =       int(embed_dim * 2 ** (len(__a			) - 1)			)
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Tuple	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   str			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   Dict			):
         return 1E-4





'''simple docstring'''





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 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

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

         UpperCAmelCase_	   =       model(__a			)["last_hidden_state"]
         UpperCAmelCase_	   =       tf.TensorShape((1, 10, 768)			)
         self.assertEqual(output.shape       ,      __a			)
         # compare the actual values for a slice.
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      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			)			)




