




from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_					    = logging.get_logger(__name__)

lowerCAmelCase_					    = {
    """google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		'efficientnet'

      def __init__(   self      ,				__UpperCAmelCase = 3      ,				__UpperCAmelCase = 600      ,				__UpperCAmelCase = 2.0      ,				__UpperCAmelCase = 3.1      ,				__UpperCAmelCase = 8      ,				__UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]      ,				__UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]      ,				__UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]      ,				__UpperCAmelCase = []      ,				__UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]      ,				__UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]      ,				__UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]      ,				__UpperCAmelCase = 0.25      ,				__UpperCAmelCase = "swish"      ,				__UpperCAmelCase = 2560      ,				__UpperCAmelCase = "mean"      ,				__UpperCAmelCase = 0.02      ,				__UpperCAmelCase = 0.001      ,				__UpperCAmelCase = 0.99      ,				__UpperCAmelCase = 0.5      ,				__UpperCAmelCase = 0.2      ,				**__UpperCAmelCase      ,				):
        super().__init__(**__UpperCAmelCase		)

        lowerCAmelCase__				:  Dict		       =     num_channels
        lowerCAmelCase__				:  List[str]		       =     image_size
        lowerCAmelCase__				:  Tuple		       =     width_coefficient
        lowerCAmelCase__				:  Optional[int]		       =     depth_coefficient
        lowerCAmelCase__				:  Tuple		       =     depth_divisor
        lowerCAmelCase__				:  List[Any]		       =     kernel_sizes
        lowerCAmelCase__				:  List[Any]		       =     in_channels
        lowerCAmelCase__				:  int		       =     out_channels
        lowerCAmelCase__				:  List[str]		       =     depthwise_padding
        lowerCAmelCase__				:  Optional[Any]		       =     strides
        lowerCAmelCase__				:  List[Any]		       =     num_block_repeats
        lowerCAmelCase__				:  List[Any]		       =     expand_ratios
        lowerCAmelCase__				:  Union[str, Any]		       =     squeeze_expansion_ratio
        lowerCAmelCase__				:  Any		       =     hidden_act
        lowerCAmelCase__				:  Tuple		       =     hidden_dim
        lowerCAmelCase__				:  List[Any]		       =     pooling_type
        lowerCAmelCase__				:  Optional[Any]		       =     initializer_range
        lowerCAmelCase__				:  int		       =     batch_norm_eps
        lowerCAmelCase__				:  Tuple		       =     batch_norm_momentum
        lowerCAmelCase__				:  str		       =     dropout_rate
        lowerCAmelCase__				:  Union[str, Any]		       =     drop_connect_rate
        lowerCAmelCase__				:  Optional[int]		       =     sum(__UpperCAmelCase		) * 4






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		version.parse('1.11'	)

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







      @property
      def 	__magic_name__(   self		):
        return 1e-5







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  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''		)

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

        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase		)['''last_hidden_state''']
        lowerCAmelCase__				:  List[Any]		       =     tf.TensorShape((1, 10, 768)		)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        # compare the actual values for a slice.
        lowerCAmelCase__				:  int		       =     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		)		)

